Category
Transformer、GNN、軽量化など、モデル実装や既存モデル改良に関係する深層学習技術を追います。
🤗 Transformersは、テキスト・ビジョン・音声など複雑なモデル定義をサポートするフレームワークで、インフェレンスターやトレーニングに使用できる。
この研究では、COVID-19臨床パスウェイズの予測監視を支援するために、パイプラインを構築しました。このパイプラインには、データリフティング、時間的再構成、イベントログの構築、プリフィックスベースの表現、予測モデルの整
このリポジトリでは、私的なAIプラットフォームであるDocGPTを提供しています。
🤗 Transformersは、テキスト・ビジョン・音声など複雑なモデル定義をサポートするフレームワークで、インフェレンスターやトレーニングに使用できる。
SGLangは、大規模言語モデルのサービングフレームワークです。このライブラリは、高性能なサービスフレームワークで、大規模言語モデルのサービングをサポートしています。
オープンソースのAI推論最適化と展開用ツールキットです。
🤗 Transformersは、テキスト・ビジョン・音声など複雑なモデル定義をサポートするフレームワークで、インフェレンスターやトレーニングに使用できる。
SGLangは、大規模言語モデルのサービングフレームワークです。このライブラリは、高性能なサービスフレームワークで、大規模言語モデルのサービングをサポートしています。
オープンソースのAI推論最適化と展開用ツールキットです。
このリポジトリでは、私的なAIプラットフォームであるDocGPTを提供しています。
分散トレーニングと推論を容易、効率的に実行するためのディープラーニング最適化ライブラリです。
このリポジトリでは、64MパラメータのGPTを完全にTrainingし、2時間以内に完成させる手法を提供します。
データサイエンスの学習には役立つリポジトリ。実世界の問題に応じた学習が可能。
マシンラーニングシステムの理論と実装に関する本。
中国の工业界で開発された機械学習フレームワーク。並列化および分散処理を可能にしている。
SANAは、高解像度画像生成モデルSANAを紹介する本研究であり、低計算コストで優れた高解像度画像を生成できる。
Machine Learning Engineeringは、機械学習の開発と運用をサポートするためのリソースを提供する。
FastVideoは、加速されたビデオ生成用の統合推論とポストトレーニングのフレームワークです。
zenmlは、データパイプラインからエージェントまで、AIプラットフォームです。
デベロッパー向けのモデロプティミゼーションフレームワークです。モデルの高速化と効率化を実現することができます。
PyTorchで使用できる画像エンコーダとバックボーンの最大のコレクションです。トレーニング、評価、推論など様々なスクリプトや事前の重み付きデータが含まれます。
Neural Network Inference用の高性能フレームワークです。モバイルプラットフォームに最適化されています。
パラメータ効率の向上のための最先端のフィネチュニングフレームワークです。
電気生理信号から表現を学習し、脳コンピューターインターフェースの開発を支援する。
このリポジトリでは、トークナイザーの最適化を提供しています。
LLMやVLMのFine-Tuningを簡素化したライブラリ。
LLMを利用するために、セマンティック検索やLLMのオーケストレーションなどを行えるフレームワーク。
presidioは、テキスト、画像、構造化データを含む敏感データを検出、削除、マスク、アノニマイズするオープンソースフレームワークです。自然言語処理、パターンマッチング、カスタマイズ可能なパイプラインをサポートします。
このリポジトリでは、AIエンジニアリングのためのオープンソースプラットフォームであるMLflowを提供しています。
AIモデルを高速にトレーニングするためのライブラリ。1台から10000台のGPUで利用可能。
「fastai」は、深層学習のライブラリです。
AI用のデータセットを提供するプラットフォームです。
セマンティックシーケンス分割モデルのライブラリです。
kserveは、分散型生成的および予測性AI推論プラットフォームであり、可 scale, multi-framework デプロイをサポートして Kubernetes 上に展開されることができます。
Implementation for MatMul-free LM.
PyTorchベースのリージョニングLMMを作成するためのチュートリアルです。
[ICLR2025 Spotlight] SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
感覚変換、すなわちバーチャルエキスパートが可能なPytorch実装。
Recent advances in automatic rigging now deliver animation-ready 3D assets at scale, yet generating the motion
Retail search systems serve diverse geographic regions with distinct query patterns, vocabularies, and product
Trade-up recommendation identifies higher-quality alternatives that preserve a customer's purchase intent whil
We present a Hessian-based approach to numerically continue periodic orbits in dynamical systems. A loop (peri
Chilli (Capsicum annuum) is one of India's most economically significant crops, yet its productivity is persis
Generating categorical graphs requires choosing node and edge types that form a coherent structure without dep
Cooperative multi-agent reinforcement learning (MARL) systems rely on past experience for learning coordinated
Does a simplified legal clause still say what the original said? The checks in current use cannot establish th
Late Gadolinium Enhancement (LGE) on cardiac magnetic resonance is a key marker of myocardial scar, but its li
As spaceborne computing systems increasingly rely on neural network (NN) accelerators, the opacity of commerci
Cryptocurrency markets exhibit extreme volatility and non-stationary dynamics that challenge conventional fore
While machine-learning interatomic potentials (MLIPs) have successfully learned potential energy surfaces (PES
Artificial intelligence models are promising for medical diagnosis, but they require large numbers of unbiased
Large language models (LLMs) show strong reasoning ability, but their explanations can remain inconsistent, we
The number of discrete class-separability jumps observed during ResNet finetuning is examined empirically as a
Evaluating the calibration of Large Language Models (LLMs) is critical for their safe deployment as zero-shot
Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications re
AI-driven de novo molecular design offers a promising route to accelerate early-stage drug discovery by genera
This paper introduces Deep Microcompression (DMC), a hardware-aware pipeline for deep learning inference on ba
Conformal counterfactual inference enables network operators to use logged telemetry to reliably answer 'what-
When a customer adds a professional camera to their cart, should the system suggest a matching lens, a generic
As quantum hardware scales to larger devices, the classical software layers that interface with it must evolve
Scaling laws guide the design choices for training large foundation models, but deriving them involves trainin
Federated scientific machine learning enables institutions to train neural surrogates without centralizing loc
Deep Imbalanced Regression (DIR) is pervasive in continuous prediction tasks across diverse modalities, such a
Large Reasoning Models (LRMs) achieve superior problem-solving through extended Chain-of-Thought (CoT) generat
Reasoning allows artificial intelligence models to revisit and correct their mistakes, enabling recent frontie
Recent benchmarks such as PowerGraph provide large collections of power-grid graphs for cascading-failure clas
Diffusion probabilistic models can capture the multi-modal, interaction-rich distribution of joint future traj
Can we recover the 3D poses of multiple people using only sound? This paper presents the first attempt to esti
Although Deep Neural Networks have become foundational in many areas of Machine Learning, high computational d
Modern LLM agents operate in persistent workspaces whose accumulated history can exceed both GPU KV capacity a
Remote robotic systems operating over wireless networks must maintain reliable control despite limited communi
Client heterogeneity creates both an opportunity and a risk in personalized federated graph learning. Knowledg
Personalized federated learning (PFL) is a promising paradigm for collaborative learning over distributed devi
Deep models for sales forecasting, such as WaveNet-style dilated convolutional networks, are accurate but opaq
Graph representation learning (GRL) serves as a canonical paradigm for modeling complex networks. However, rea
Distillation is common in LLM post-training, where on-policy knowledge distillation (OPKD) uses student-genera
The high training cost of Graph Neural Networks (GNNs) has raised growing concerns regarding model ownership i
Due to resource constraints or external and internal uncertainties, clients in real-world federated learning s
The Duckworth-Lewis-Stern (DLS) method has been the international standard for revising target scores in rain-
Prefix caching, in which a serving engine reuses the key and value tensors of a shared prompt prefix across re
Where inside a language model does refusal live, and does that place change when the architecture does? In a t
Inferring cellular dynamics from unpaired single-cell snapshots requires modeling both state transitions and p
Vision transformers typically treat every image token as equally important, yet for most tasks in computer vis
Multimodal emotion recognition has attracted growing interest due to its importance in human-computer interact
We introduce WEECFP, a parameter-free 1024-dimensional continuous molecular fingerprint that scatters each Mor
Symbolic regression (SR) discovers closed-form mathematical expressions from data, offering interpretability b
As generative audio models grow in complexity, the computational and ecological costs of synthesizing everyday
Graph coarsening reduces the large Quadratic Unconstrained Binary Optimization (QUBO) formulations arising whe
Intelligent extended reality (XR) systems increasingly use eye and head tracking to infer user intent, task, a
We study stochastic gradient descent with random reshuffling for finite sums \[ F(x)=\frac1n\sum_{i=1}^n f_i(x
We investigate how optimizers scale across the overtraining axis and show that relative optimizer performance
Modern fine-grained Mixture-of-Experts (MoE) models route each token to a small number of experts and renormal
Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data
We study regression under bounded responses in terms of excess mean squared error. When the comparator class i
We consider density estimation under the relaxed local differential privacy condition that the privatized dist
Diffusion TV is an interactive AI art installation that offers a tangible and embodied experience of diffusion
Visuomotor imitation policies can achieve high performance under in-distribution visual conditions yet fail wh
Computer-use agents have advanced on benchmarks like OSWorld and AndroidWorld, but still act mostly through th
The integration of GenAI tools into higher education assessment raises important questions about how students
Public vulnerability databases collect rich information about known software flaws, including their weakness t
A transformer language model assigns a single, context-independent vector to a word type at its embedding laye
On-policy distillation (OPD) provides dense, per-token supervision for language model post-training, but its e
Automated reference-based evaluation methods play a critical role in assessing natural language generation sys
Layer dropout (a.k.a. stochastic depth) has been shown to enable faster training, higher accuracy, and robustn
Large language model agents increasingly rely on execution traces to master complex interactive tasks. However
Commonsense reasoning in computer vision encompasses integrating visual data and contextual knowledge, crucial
We present a unified reinforcement-learning (RL) framework that discovers compact parametrized quantum circuit
Mixture-of-Experts (MoE) architectures provide an efficient paradigm for scaling large language models (LLMs),
On-Policy Distillation (OPD) has emerged as a widely adopted post-training paradigm for enhancing large langua
The integration of artificial intelligence (AI), particularly large language models (LLMs), into educational a
Large language models are now trained and evaluated under a diverse set of paradigms: supervised fine-tuning (
Biological agents navigate familiar environments not by re-solving routes for each new goal, but by reusing a
We present Discovery Loop, a lightweight system that uses a large language model (LLM) to iteratively evolve o
As evolutionary transfer optimization (ETO) scales to larger collections of related tasks, problem evaluation
As LLMs take on roles requiring moral advice, understanding how they attribute moral agency becomes critical.
LLM-based chatbots are increasingly used as everyday confidants. Because they are designed to maximize user sa
What is the relation between understanding and compression, and why does human understanding take such a heavi
Multi-expert models have become the dominant paradigm for long-tailed learning, largely attributed to their pr
Recently, multimodal large-scale reasoning models have demonstrated remarkable capabilities in solving complex
The widespread adoption of artificial intelligence (AI) within real-world applications has raised a lot of con
Reliability evaluation of deep neural networks under hardware faults commonly relies on fault injection, but e
Four-dimensional (4D) flow magnetic resonance imaging (MRI) is a powerful non-invasive technique for visualizi
Changes in sensor height and viewpoint alter object-level point distributions, making cross-platform LiDAR uns
Fraudulent messages sent via Short Message Service (SMS) are increasingly obfuscated to evade cost-conscious c
Accurate station-level precipitation nowcasting is critical for agriculture, water resource management, and di
Cross-cultural mediation by large language models (LLMs) requires deciding both when to intervene and how to r
Long-horizon predictive maintenance requires models to distinguish slowly evolving degradation from normal ope
Embodied agents performing long-horizon tasks require a memory representation in which the state transitions o
Proteins perform diverse cellular functions, and even single amino-acid substitutions can alter stability, act
Diffusion language models (DLMs) offer a non-autoregressive alternative for mobile edge agentic artificial int
In this paper, we study the problem of personalized survey response prediction using fine-tuned large language
Personalizing large language models (LLMs) is essential for delivering AI assistance that aligns with individu
Generative AI and coding agents can accelerate research software development, but they also increase the need
Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations f
Urban air quality can vary significantly along transit corridors, necessitating high-resolution monitoring. Th
AI coding systems are moving from autocomplete and chat toward agents that can inspect repositories, edit mult
We introduce PetQA, a Korean long-form question-answering (QA) benchmark for evaluating veterinary knowledge a
Leveraging their inherent sparse event-driven computation, spiking neural networks (SNNs) offer a promising pa
Computational phylogenetics has become an essential tool in historical linguistics, yet its application at a g
Retrieval-Augmented Generation (RAG) enhances language models with external knowledge, but the lengthy retriev
We propose an influence score to quantify the contribution of attention heads to classification decisions in T
Machine translation (MT) offers a scalable way to extend English instruction-tuning data to multiple languages
This paper describes the participation of the BIT.UA team from the University of Aveiro in the 14th edition of
Mixture-of-Experts (MoE) models activate only a small subset of experts per token, but the full expert set oft
Constructive feedback is crucial for creative writers to refine their storytelling abilities. Since receiving
We construct a corpus of 1,262 verse-commentary (urai) pairs from five Classical Tamil source sections, rangin
Traditional Retrieval-Augmented Generation (RAG) systems score each passage independently against the query, a
The Multi-Level Intermediate Representation (MLIR) has made reusable compiler infrastructure practical for dom
When there is not enough labeled data to properly train deep learning models, transfer learning can help. We s
Visual reasoning tasks require a system to jointly perceive visual content and apply formal relational constra
Recent advances in Earth Observation representation learning accommodate heterogeneous sensors and missing obs
Palynomorphs (microscopic, organic-walled fossils such as pollen, spores, and dinoflagellates) are important h
Hyperspectral and multispectral image fusion (HMIF) aims to reconstruct a high-resolution hyperspectral image
Explicit primitive-based radiance fields such as 3D Gaussian Splatting typically model view-dependent appearan
Continuous sliders are useful only when coefficient changes produce predictable image changes. Yet most diffus
Pathology foundation models integrated with multiple instance learning achieve competitive accuracy within sin
Background and Objective: External evaluation of medical-imaging AI is often collapsed into discrimination. We
Automated gastrointestinal (GI) endoscopy classification requires models that generalize across diverse modali
Autonomous systems require robust low-latency perception under rapidly changing scene dynamics and challenging
Species identification in camera trap images has been widely studied, but key ecological modeling tasks such a
Personalization models generate new images guided by a few subject references, while style transfer methods ai
Cross-view localization (CVL) estimates the pose of a ground image by matching it to a geo-referenced satellit
Recovering camera and hand motion in world coordinates from egocentric video is a key capability for activity
Instruction-guided 3D editing is essential for interactive content creation, yet it faces a significant bottle
Monocular depth estimation foundation models, such as the Depth Anything series, have achieved remarkable perf
Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos that contain moments relevant to a
Lesion-focused image classification presents a core analytical challenge, as discriminative signals are often
Multi-modal medical images and clinical reports provide complementary anatomical, functional, and semantic inf
Industrial anomaly detection faces two engineering bottlenecks: memory bank construction latency and inference
To enable accurate and rapid photon control point and proton beamlet dose calculation in the DoseRAD2026 chall
Recent zero-shot 3D visual grounding methods leverage vision-language models (VLMs) to localize objects in 3D
Synthetic Aperture Radar (SAR) images have all-weather, day-and-night observation capabilities. However, compa
Structure-from-Motion (SfM) is a fundamental tool for sparse 3D reconstruction with broad impact in robotics a
We present DiT Readout (ReaDiT) Guidance, a lightweight framework for controlling generation with Diffusion Tr
Diffusion Transformers (DiTs) have emerged as a dominant architecture for high-quality text-to-image generatio
Recent generative image-editing Diffusion Transformers (DiTs) demonstrate impressive semantic editing capabili
Human-robot interaction (HRI) enables intuitive and intelligent collaboration between humans and robots in rea
Robotic systems increasingly operate in dynamic, uncertain, and open-ended environments, where design-time ass
Safe robot navigation in dense crowds requires reasoning about pedestrian motion and how it may change in resp
Natural-language instruction changes can directly alter robot behavior. A reliable embodied system should pres
Vision-Language-Action (VLA) or World Action (WAM) models have recently demonstrated remarkable performance in
Rigid Body Dynamics (RBD) forms the computational core of real-time robotic control, but its immense computati
Reliable autonomous mapping, environmental sampling, last-mile logistics, and infrastructure deployment depend
Strict nonlinear normal modes provide very regular families of oscillations within conservative mechanical sys
Three-dimensional scene graphs (3DSGs) have emerged as a promising approach for building geometrically grounde
We study the online fair division of indivisible items, where items arrive one at a time and must be allocated
Envy-free cake cutting is a central problem in fair division with a striking divide between existence and comp
The strategic facility location problem is defined as follows: $n$ agents report their location in a metric sp
AIの入門コースを提供する。
デバイス上のLLM推論をXビット量化を使用したもの。
トークナイザーの高速化を目的としているライブラリ。
Large language models demonstrate increasingly strong reasoning capabilities through effective post-training.
We introduce Mitra-v2, a tabular foundation model that delivers state-of-the-art performance on real-world cla
Language generation is almost universally treated as a sequential process: autoregressive models emit one toke
We develop an energy-based reduced-order model for micromagnetic magnetization dynamics that couples a convolu
Merging a LoRA adapter into its base model is standard deployment practice: it removes the runtime adapter's p
We present Hakken, a domain-agnostic prediction and explanation system performing knowledge prediction, i.e.,
Quantization is widely used to reduce the computational and memory demands of neural-network inference. In rec
Federated unlearning aims to remove a client's data from a shared model without retraining from scratch. Some
In Geometric Deep Learning, inductive biases serve two primary functions: enforcing manifold constraints and e
Data collected during aerial and spaceborne imaging spectroscopy campaigns enables the detection of transient
Expert pruning reduces the memory and serving cost of Mixture-of-Experts (MoE) models by removing low-importan
Learning rich medical concept representations is essential for EHR prediction. Text-attributed knowledge graph
Deep neural operators learn mappings between input functions and complete PDE solution fields, enabling forwar
Adaptive network intrusion detection systems retrain classifiers after drift alarms, but an alarm detects chan
Pretrained vision-language-action (VLA) models enable broad manipulation but remain unreliable in tasks demand
We introduce a lightweight Quantum Prototypical Recurrent Unit (QPRU) that requires significantly fewer parame
Large-scale logistics networks require synthetic data generation capabilities to support scenario-based planni
Sparse autoencoders (SAEs) are widely used to interpret language model activations, but SAE training and laten
A modular deep Recurrent Neural Network (RNN) is introduced to facilitate the process of deploying various arc
We present TNFlow, a transformer and normalizing flow architecture for inferring the surface composition of Tr
Temporal integration gives continuous-time recurrent networks memory, but in deep stacks it also delays bottom
We introduce an uncoupled learning algorithm which, when employed by all players of an arbitrary $N$-player no
Reinforcement learning with verifiable rewards (RLVR) and on-policy distillation (OPD) have emerged as two dom
Blackwell's 4-bit floating-point (FP4) tensor cores do not automatically make attention faster because softmax
Current conditioned generative models heavily rely on score functions for guidance during training. When the g
Reinforcement learning from human feedback (RLHF) has emerged as a powerful yet sample-inefficient approach fo
What can a single layer of self-attention compute? We study head complexity: the minimum number of attention h
The geometric morphology of deposited filaments can significantly influence the structural performance and sta
Evaluating large language models (LLMs) in safety-critical, physics-governed environments requires more than a
Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressiv
Despite strong performance on held-out electroencephalography (EEG) data, seizure detectors may fail under rea
Brain-MRI inpainting replaces a masked region of a scan with synthesized, anatomically plausible healthy tissu
The problem. A long-lived KV cache must be compressed before the queries that will read it exist; selection by
Retrieval-augmented generation (RAG) complements parametric models with retrieved external evidence. The same
Generating complete 3D scenes from sparse, unconstrained views is a fundamental challenge in 3D vision which r
Attention mechanisms are central to modern foundation models, yet their training dynamics remain poorly unders
Federated Learning (FL) with Differential Privacy (DP) is increasingly adopted to preserve data confidentialit
Graphs are a fundamental data structure underlying many problems in the natural and social sciences. Over the
Energy consumption is one of the largest operational expenditure items for mobile network operators, yet site-
A free pause token gives a language model extra compute to form each next-token prediction (as a pause, or thi
We study the ordered Bernoulli-word kernel f(p,n,k)=p^k(1-p)^(n-k) and the geometry generated by its inverse-i
Athlete monitoring data may be recorded minute by minute throughout a match or training session, while injury
Despite increasing reliance on LLMs that reason with external evidence supplied by tools, retrieval-augmented
Bayesian Network (BN) fusion combines multiple input networks into a single structure, balancing dependency pr
Let $L\subseteqΣ^*$ and fix a morphism $h:Σ^*\to M$ into a finite monoid. We study exact factorization and can
Rigorous results show that feedforward neural networks can overcome the curse of dimensionality in the numeric
Deep neural networks deployed in the wild must be both efficient and adaptable, requiring model compression an
Species Distribution Modelling (SDM) is essential for understanding how environmental conditions shape biodive
As integrated circuit technology scales into the nanometer regime, the traditional disconnect between logic sy
Existing theories derive neural scaling from data geometry or a specified data-model spectrum, but systems tra
Logit-based knowledge distillation for autoregressive language models usually aligns teacher and student next-
LLM-based code generation fails when correctness depends on execution-dependent coupling: the meaning of one r
Self-driving laboratories (SDLs) combine automated experimentation with adaptive decision-making to accelerate
Accurate city-level IP Geolocation is an important enabler for the modern digital ecosystem, underpinning serv
Unattended interactive autonomy - machines that step into danger in place of humans and complete tasks with hu
Recent work on controllable music generation has focused on autoregressive models, leaving diffusion-based sys
Cardiac, neural, behavioral, and speech measurements from wearable and mobile devices provide partial, noise-s
Pause-token methods improve LLM reasoning by inserting special tokens into sequences. Prior work explains thes
Large language models deployed in high-stakes settings frequently generate plausible but ungrounded claims. St
Purpose: Increased number of chest radiograph (CXR) scans create a triage bottleneck, queueing urgent examinat
Weakly-Supervised Dense Video Captioning aims to localize and describe multiple events in untrimmed videos giv
Cortical neurons fire sparsely -- often fewer than one spike per sensory window -- making rate coding insuffic
Constant optimization refines the numerical coefficients of candidate expressions in tree-based genetic progra
Runtime traces are becoming a central substrate for understanding agentic systems, yet interpretation has focu
Latent entity extraction (LEE) tackles the challenge of identifying implicit, contextually inferred entities w
Safety alignment is usually posed as a topic-level question: is this subject harmful? Deployments ask a narrow
Hybrid language models combine attention with a fixed-size recurrent state, but the role of each channel remai
ASR systems sometimes produce fluent text that is unrelated to the speech they receive. We view these hallucin
Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation me
On-policy distillation (OPD) combines student-generated rollouts with dense token-level supervision from a tea
MLLM-based embedding models remain limited in compositional retrieval, often failing to distinguish scenes con
Neural machine translation (NMT) systems typically produce a single output per input, obscuring the alternativ
We present Alignment-Free Text-Audiobox (Text-AB), a unified framework for high-quality voice dubbing and full
Zero-shot vision-language models (VLMs) are increasingly used as training-free species recognizers, but report
In numerical signal processing for electroacoustic composition, the progressive loss of specific development a
LETHE (Latent-parameter Evolution with Temporal Hierarchical quasi-Equilibrium) is a self-referential sonic-ob
Synthetic data augmentation has become a common strategy for addressing class imbalance in NLP, but most appro
Large Language Models (LLMs) demonstrate strong multilingual reasoning performance, yet their robustness to se
Knowledge graph embedding (KGE) demonstrates its effectiveness for predicting missing links in knowledge graph
Retrieval-augmented generation (RAG) can improve the specificity and grounding of large language model respons
Automated evaluation of creativity tasks remains challenging for LLM-as-a-Judge, as LLM is susceptible to bias
Large language models achieve superior performance on tasks that require extended reasoning, but long chains o
Transformers lack a native lookup mechanism, requiring repeated dense computation to recognize and reuse local
Moral language plays a central role in shaping online endorsement and the diffusion of information, yet existi
Tamil spell and grammar correction is challenging because Tamil is an agglutinative low-resource language with
Large language models (LLMs) face severe memory bottlenecks in long-context inference due to the linearly grow
Video-language benchmarks are usually constructed by the dataset authors without published reliability statist
Robots interacting with people must recognize not only explicit commands, but also social cues such as invitat
Oral potentially malignant disorders (OPMDs) are critical precursors to oral cancer, yet clinical detection re
Eulerian video amplification boosts sub-pixel motion by band-pass filtering per-pixel intensity traces and app
Automated aortic segmentation in 4D flow MRI is essential for reproducible hemodynamic assessment but is limit
Fine-grained visual understanding depends on local detail, yet visual encoders face a trade-off between costly
We introduce S$^3$T (Self-Supervised Self-Distillation over Time), which, to the best of our knowledge, is the
While data-driven 3D shape correspondence estimation has recently seen substantial progress, robust matching u
Online 3D reconstruction models perform poorly on long videos. This happens because regressing poses relative
Evaluating physical reasoning in video models is difficult because absolute motion measurements depend on fram
We propose Puffin-World, a unified multimodal architecture that integrates physical understanding, spatial sim
3D Foundation Models (3DFMs) such as VGGT have recently pushed the boundaries of 3D vision by predicting rich
This paper proposes AdaRoboVLG, a task-adaptive Vision-Language-Grasp (VLG) framework that supports generaliza
Dense semantic segmentation allocates computational resources uniformly across the entire image, regardless of
Pathology foundation models improve transferable representation learning for histopathology, but recent gains
Bridging the gap between the discrete reasoning of Vision-Language Models and the continuous, physics-constrai
Video diffusion models (VDMs) have achieved impressive progress in text-to-video generation, but their high me
Verifying that manufactured batches of milling tools or carbide rotary burrs conform to production order sheet
Real-world image super-resolution (SR) increasingly relies on Diffusion Transformer (DiT) backbones, whose int
Communities are fundamental spatial units that shape urban form and social life. Whether a residential compoun
We introduce LLaDA-Image, a unified framework that pairs a 6B Diffusion Transformer (DiT) trained from scratch
Implicit neural representations (INRs) can model continuous 3D shapes with a shared coordinate decoder and per
Histopathological subtyping relies on the recognition of characteristic histological patterns. These patterns
Pairwise preference labels rank complete images, yet Diffusion-DPO applies their effect over many spatial and
Streaming video understanding requires Vision Language Models (VLLMs) to process growing video streams and ans
Background. Remote photoplethysmography estimates the cardiovascular pulse from facial video, and its explanat
3D scene representations like NeRF and 3D Gaussian Splatting (3DGS) suffer severe artifacts in sparse-view set
Vision Foundation Models (VFMs) provide transferable patch representations for few-shot industrial anomaly det
Vector-quantization based image compression has achieved strong rate--distortion performance, yet most of them
Training-free, camera-controlled novel view synthesis from a single image using pre-trained video diffusion mo
Recent advances in text-to-video (T2V) diffusion models have demonstrated remarkable generative capabilities,
While deep generative models offer new opportunities for medical image synthesis and data sharing, their abili
World models (WMs) have demonstrated strong potential for end-to-end autonomous driving by learning predictive
Thermal infrared imaging offers reliable perception in darkness and adverse weather, but thermal datasets rema
World models offer a promising paradigm for autonomous driving by predicting how traffic scenes may evolve and
Head-mounted displays (HMDs) fundamentally limit emotion recognition in virtual reality (VR): by occluding the
We present FlashRender, a few-step generative rendering framework that retakes a source video along a target c
Existing safety alignment methods for vision-language models usually modify the model behavior globally: once
Infrared-visible object detection (IVOD) integrates complementary evidence from visible and infrared sensors f
Preprocessing-based defenses are the standard first-line response to adversarial attacks on edge vision system
The continuous emergence of high-quality video deepfakes requires detectors that continually adapt to new forg
Recent years have witnessed growing interest in continual anomaly detection for industrial visual inspection.
Contrastive language-image learning (CLIP) has become a key paradigm for remote sensing vision-language unders
Depth can resolve appearance ambiguity in RGB-D salient object detection (SOD), yet sensor depth is not unifor
Aligning video generative models to human preferences heavily relies on Reinforcement Learning (RL), which suf
Diffusion models have become the mainstream paradigm for modern visual generation and have substantially advan
Advances in neural rendering have enabled high-fidelity multi-view reconstruction of 3D scenes. However, free-
Vision-language models (VLMs) are increasingly deployed in high-stakes settings, where a response that is reas
A key bottleneck in 3D Gaussian Splatting training is the continual growth of Gaussian primitives, which incre
Parking spot classification is a fundamental task in intelligent transportation systems, yet most deep learnin
Vision-language-action (VLA) policies have shown strong potential for general-purpose robotic manipulation, bu
Vision-Language Navigation in Continuous Environments (VLN-CE) requires an agent to follow natural language in
This paper presents an end-to-end computational pipeline that converts a selected object mesh, a measured obje
Simulation is increasingly used to support safety-related decision-making in road transport, particularly for
Autonomous navigation in space requires reliable terrain assessment for safe operations, especially in undergr
Vision-language-action (VLA) models with billions of parameters now dominate the LIBERO manipulation benchmark
Robots operating in physical environments make control decisions based on uncertain sensor measurements, which
Post-training VLA policies typically rely on supervised fine-tuning with costly expert demonstrations or reinf
Autonomous unmanned aerial vehicles (UAVs) increasingly operate in cluttered environments where global planner
Evaluating robot manipulation policies is becoming increasingly important as generalist models, particularly v
End-to-end autonomous driving has increasingly adopted world model-based reinforcement learning frameworks to
We present Iris-mini and Iris-pro, two search agents trained at the 35B-A3B and 397B-A17B scales, together wit
Audio-video diffusion models rely on cross-modal attention to coordinate text, sound, and visual content, yet
The ambition of the 2025 PNPL competition (Landau et al., 2025) was to launch a multi-year curriculum for non-
Hybrid LLMs pair softmax attention with linear-attention layers such as Gated DeltaNet (GDN), whose recurrent
Pythonで使えるマシンラーニングライブラリを紹介している。
BERTopicでは、BERTとc-TF-IDFを用いて、トピックを簡単に分析して解釈できるようにしたモデルを紹介しています。
Several classical machine-learning methods, such as KRRs and SVRs, are both computationally and analytically t
The Gromov-Wasserstein (GW) distance provides a principled framework for aligning metric measure (mm) spaces b
Graph neural networks (GNNs) are a class of neural networks suitable for learning on graph-structured data. Th
We study when and how momentum improves large-batch training in the one-pass regime, using power-law kernel re
Learned graph simulators provide an efficient alternative to high-fidelity solvers for granular dynamics. Howe
Self-supervised pretraining has transformed language and vision, but its value for molecular graph neural netw
We study online maximization of nonnegative, non-monotone DR-submodular functions over compact convex down-clo
Stochastic gradient Markov chain Monte Carlo (SGMCMC) methods enable scalable Bayesian inference, but their pe
Neural Architecture Search (NAS) has emerged as a powerful paradigm for automatically designing deep neural ne
This chapter reconstructs the Hopfield network as a physical theory of memory rather than merely an early neur
This chapter reconstructs the McCulloch-Pitts program as a physics of neural computation rather than the famil
Large language models (LLMs) exhibit in-context learning capabilities, where they can learn new tasks from pro
Using a zoom-in tool is an important foundational part of modern visual agents, because it allows to efficient
Long-term LLM agents must preserve information across interactions while distinguishing repeated evidence, his
We present Jina-OCR-v1, an end-to-end document parsing model built to serve on low-budget GPUs. It combines th
Nastase et al. (2026) argue that large language models (LLMs) may illuminate language processing because both
Visual token pruning reduces the inference cost of vision-language models (VLMs), but most methods only ask wh
On-policy distillation (OPD) accelerates post-training by providing dense token-level supervision from a froze
Evaluating LLM agents is essential for guiding their development, yet it has grown prohibitively expensive: a
Streaming video understanding is a critical capability for real-world applications, including embodied intelli
Autonomous agents are beginning to carry out machine-learning (ML) research end to end. These agents combine a
Language models spend most of their attention on a small fraction of context, yet they read the entire KV cach
Vision Transformers (ViTs) typically process every image using a fixed input resolution and model width, even
Robot learning increasingly depends on broad and diverse demonstrations, yet collecting robot data remains exp
Certifiable methods have arisen as a means to guarantee global optimality of solutions to non-convex problems
Multi-segment soft robotic arms can continuously reconfigure their body shapes for safe interaction, but tip c
Vision-Language-Action (VLA) policies fuse multimodal sensory inputs, but training on limited and homogeneous
World models are an emerging paradigm in representation learning in which an agent jointly learns state-action
Domestic robots rely on rich perception to operate in private homes, but privacy risk persists even when raw s
Autonomous robots powered by deep learning face a fundamental auditability challenge: when incidents occur, in
Predictive models are increasingly used in robotics for state estimation, planning, control, and policy evalua
Vision-Language-Action (VLA) Models are increasingly used in robotics for their ability to ground language and
Foothold-constrained terrain is characterized by sparse, discontinuous, or geometrically restricted feasible f
Multi-axis 3D printing enables support-free fabrication and improved part quality, but robustly processing rea
Humanoid robots responding to emergency stop commands typically execute a fixed maneuver, without reasoning ab
Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen
Foot forward kinematics (FK) is widely used to improve proprioceptive legged odometry by providing reliable ve
Autonomous lunar missions require real-time per- ception under three coupled constraints: extreme low-light co
Intermittent state measurements pose fundamental challenges to model predictive control of constrained nonline
This study proposes a novel Model Predictive Path Integral (MPPI)-based task-space control framework. The prop
Fully-powered knee prostheses, unlike traditional passive knees, can perform controlled positive work, reducin
Augmenting the dexterity of human surgeons has the potential to free them from tedious subtasks. We consider d
This paper is the first in a series on Turn-Based Combat Arena, a configurable framework for turn-based strate
We model insider threat detection as a dynamic Bayesian game in which a platform coordinates a committee of st
A central challenge in mechanism design is to develop truthful trade mechanisms that maximize the expected gai
We investigate the fair allocation of indivisible items among agents with asymmetric entitlements in mixed man
We study the dynamics of Stochastic Gradient Descent (SGD), which is known to steer deep neural networks towar
Speech brain-computer interfaces (speech BCIs) translate neural activity into language, offering a path toward
We introduce SolarWM, a fully open foundation for building interactive video world models from data preparatio
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for large language mo
Model compression techniques such as pruning and quantization facilitate the efficient deployment and accelera
Open Source Computer Vision Libraryは、画像やビデオを分析するためのライブラリです。
The Rashomon effect is a machine learning phenomenon where equally accurate models produce different predictio
We extend recent work establishing an equivalence between one-layer transformers and nearest-neighbor classifi
We prove that a multi-head scaled dot product attention can be viewed as a parameter identification strategy.
The present research is devoted to the nonparametric estimation of a density-dependent drift coefficient in a
Reconstructing a damaged musical fragment is an inverse problem: the observed sequence contains partial inform
Matrix-variate data with missing entries arise frequently in applications where observations are naturally org
No single optimization method is uniformly best for all problems, and the most suitable optimizer choice can c
Automated repair of Hardware Description Language (HDL) designs remains challenging due to the large search sp
Emerging edge AI workloads increasingly require arithmetic units that can trade computational accuracy for eff
Denoising diffusion models are the dominant architecture for image generation, whereas most natural language g
Multi-agent combinatorial optimization problems are notoriously challenging due to their NP-hard nature. Recen
Many current end-to-end driving policies emit a pool of candidate trajectories and select one, which makes sel
Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and
Dexterous manipulation policies learned by imitation are typically evaluated for robustness to variation in sc
Cooperation in multi-UAV systems requires reliable relative perception so that follower vehicles can maintain
In Model Predictive Control (MPC), cost-function weights shape closed-loop behavior, yet changing conditions o
Executing long-term tasks in dynamic environments requires embodied agents to maintain robust and adaptive 3D
Action chunking is a standard execution strategy in modern Vision-Language-Action (VLA) frameworks, but fixed
Exploring unknown environments with multiple UAVs requires coordination under intermittent communication, maki
Robotic throwing enables fast object transport and extends a robot's reachable workspace beyond traditional pi
Accurate tilt angle estimation is important in many engineering applications, such as robotics, motion trackin
Intelligent vehicles increasingly support adaptive applications beyond driving themselves, ranging from contex
We present ADAPT, an end-to-end framework for interactive, text-conditioned humanoid whole-body control. Unlik
Physical human--robot interaction software often couples desired-behavior specification with constrained reali
Cooperative perception allows a drone fleet to combine observations from multiple viewpoints. However, existin
In indoor environments, object positions frequently change due to human activities or embodied-agent interacti
Language-driven dexterous grasp models, such as DextER, perform well when instructions specify where to grasp,
Animals' body morphology shapes the gait patterns they can perform, where mechanical resonance reduces the nee
Inoue et al. have introduced the successful derivation game (SDG) on context-free grammars (CFGs), which is a
We study graph coloring with color preferences, in which each vertex ranks the available colors. In addition t
Ensuring factuality remains a critical challenge for deploying LLMs in high-stakes settings. Existing hallucin
Compact token sequences are essential for efficient 3D generation. However, existing 3D tokenizers typically o
The attention prefilling phase of long-context LLM inference scales quadratically, making self-attention a sev
この研究では、COVID-19臨床パスウェイズの予測監視を支援するために、パイプラインを構築しました。このパイプラインには、データリフティング、時間的再構成、イベントログの構築、プリフィックスベースの表現、予測モデルの整
transformerlab-appは、AI研究者に、ローカルハードウェアとGPUクラスタ間でモデルを平滑にトレーニング、評価、およびスケールさせることができる、オープンソースの研究環境です。
この研究では、ストリーミングでのビデオ超解像化を実現する一ステップのディフュージョンフレームワーク「FlashVSR」を提案しています。このフレームワークは、局所制限の疎注意と小さい条件的デコーダを組み合わせて、効率的に
MindSpore + 🤗Huggingface: Run any Transformers/Diffusers model on MindSpore with seamless compatibility and a
This paper develops a general convolutional neural network (CNN) framework for detecting heterogeneous event-d
We study federated online reinforcement learning with linear function approximation. While recent multi-agent
Many parameter-efficient methods generate the parameters of a large neural network from a low-dimensional late
Cluster algorithms, such as the Swendsen--Wang and Wolff methods, are among the most successful MCMC methods f
This paper studies the problem of computing the stochastic probability (shortest code length) of the encoded v
Informative label missingness can change the usual efficiency ordering between completely and partially labell
Vision Transformers provide strong visual representations but typically rely on slowly updated parameters, lim
The performance of artificial intelligence (AI) and machine learning (ML) models degrades when the problem the
Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solutio
Inference with transformer-based large language models (LLMs) is often limited by the memory-bound KV cache an
Canonical neural circuit motifs are usually described functionally: divisive normalization rescales population
Systematic generalization remains a significant challenge in deep learning. In particular, combinatorial gener
Long-tail autonomous driving failures are often framed as rare-object recognition errors. We argue that this v
World models have made remarkable progress in action-conditioned future prediction for embodied agents, yet st
Partially observable locomotion requires a policy to act when task-relevant properties of the robot--environme
Off-road navigation can fail when physical structures induce irrecoverable states such as high-centering or en
In animals such as elephants and octopuses, acquiring non-visual information about an object and physically en
Source term estimation (STE), which aims to estimate key properties of the gas source, is essential for identi
This paper investigates the problem of position estimation of unmanned surface vessels (USVs) operating in coa
Accurate dynamic models play a central role in achieving reliable control of quadcopters. Classical system ide
Long-horizon physical-world agents must reason over distant goals while grounding decisions in reliable closed
Direct vision-language-action policies generate continuous robot actions efficiently, but standard behavior cl
Dexterous human-machine interaction requires intuitive and expressive interfaces that can be efficiently deplo
In the approval-based committee selection problem, a committee is said to be in the core if no subset of voter
Uncoupled no-regret dynamics provide a decentralized route to equilibrium, but prior guarantees for individual
Auto-bidding is a long-horizon sequential decision problem for maximizing conversion value under budget and ke
Multimodal models often build on architectures designed for generative vision-language modeling, typically com
Information retrieval (IR) increasingly targets open-ended queries that admit diverse perspectives. Existing I
Token prediction is a central pre-training objective for modern language models. Despite its empirical success
Large language models are increasingly required to generate responses that satisfy multiple competing objectiv
Structured pruning uses surrogate objectives because direct task evaluation over every feasible mask is too ex
In this paper, we study fair division problems in which resources are structured as graphs and agents must rec
Sampled-token on-policy distillation (OPD) efficiently transfers capabilities from teacher to student using st
Rubric-based reinforcement learning extends RL beyond tasks with exact answers or rule-based verifiers by scor
Physics-informed neural networks (PINNs) often face ill-conditioned objectives that limit high-accuracy traini
While modern generative models excel at modeling complex data, precise inference-time control in conditional g
We study active elicitation of agent preferences for collectively choosing among $m$ alternatives using promin
Reinforcement learning with verifiable rewards (RLVR) substantially improves single-sample accuracy (pass@1) b
We establish quantitative convergence to the target and uniform-in-time propagation of chaos for Langevin-regu
For finite-dimensional linear inverse problems where the variables are Gaussian, it is well-known that the min
Global goodness-of-fit and discrepancy statistics can establish that a sample departs from a reference distrib
The VCG family and the AGV mechanism are two classical approaches to efficient implementation in the static so
We consider two-player GR(1) games on graphs, where the system player Eve must satisfy \[ \Box\Diamond A_1\lan
この論文では、Causal-Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive
Recurrent fast-weight memories and selective state-space models compress an expanding context into a fixed-siz
In the framework of network dynamics, learning models, and neural tangent kernels (NTK), we show that the corr
The Maximum Entropy on the Mean (MEM) method provides a flexible computational framework for solving inverse p
We propose a multivariate extension of the pseudo-Voigt profile-a weighted convex combination of Gaussian and
Bayesian inference in compound loss models must often be repeated across policies, market scenarios, and prior
Reliable prediction of time-varying channel state information (CSI) is essential for efficient wireless commun
Kernels measure similarity or correlation in tasks such as regression and classification. The Gaussian kernel,
Graphs are invariant under node permutations, motivating the use of permutation-equivariant architectures in g
Prior analyses by Derezinski and Warmuth established all-size sampling identities, selected-OLS unbiasedness,
Across various non-verifiable tasks, human evaluation is reliable but expensive, while automatic metrics are m
Randomized sketch-and-solve algorithms accelerate overconstrained $\ell_2$ regression by replacing the input w
In neuroevolution, indirect encoding generates neural network connectivity from a compact genome rather than s
Object classification in event-based computer vision is a task that is attracting considerable research attent
While infinitely repeated games admit a rich set of Nash equilibria, finitely repeated games typically have a
语言サーバーの開発に使用するライブラリを紹介している。
画像認証システムにおける悪用された画像からの画像の認証方法を提示しました。
An intuitive method for dimensionality reduction is proposed, which is highly effective for finding interestin
The simple exclusion process (SEP) is a paradigmatic model for nonequilibrium transport, yet the rich dynamics
Stochastic gradient descent (SGD) is typically analyzed at a deterministic horizon chosen before the algorithm
Mapping spiking neural networks (SNNs) onto neuromorphic many-core platforms is often formulated with graph pa
Using the logical basis of synthesizing Hopfield Neural Network with desired corners of hypercube as stable st
In this paper, we introduce ES-AHD, a novel framework that fundamentally integrates Evolution Strategy (ES) in
On-policy distillation trains a language model on its own generations while a teacher scores them token by tok
AwesomeOPDはオンPolicy distillationの最適化用リストである。オンPolicy distillationでは、学習済みモデルを小さくすることでモデルを高速化する。
Optimizing expensive, high-dimensional black-box functions remains a central challenge in modern machine learn
Time series forecasting models are widely used in high-stakes settings, yet their predictions remain difficult
We uncover ELR collapse in language model pretraining: learning rate (LR) and parameter norm govern loss dynam
In this work, we investigate rapid prediction of the dominant $n{=}0$ vertical instability growth rate in C-Mo
Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classi
Probabilistic models of Directed Acyclic Graphs (DAGs) with latent variables impose equality constraints on th
Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditabl
The STAR (Student-Teacher Achievement Ratio) experiment (1985, Tennessee, USA) is a landmark hierarchical data
Learning operators from sequentially collected data arises in adaptive experimental design, Bayesian optimizat
In reinforcement learning policy evaluation, classic on-policy methods often suffer from high variance when es
Numerous methods have been developed to quantify feature attributions in individual predictions for tree ensem
Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervi
Modern score-based generative models have achieved remarkable empirical success in high-dimensional tasks such
Lipschitz constants are a standard way to quantify the sensitivity of neural networks to small input perturbat
Social interaction can improve collective learning but also amplify early mistakes. We study this tension when
Low-earth-orbit (LEO) satellites enable high-resolution, large-scale Earth observation for applications such a
Data mixing is a central design problem in large language model pretraining: given a fixed token budget, pract
Revelation Control is the problem of choosing priced interventions that reveal hidden state only insofar as th
Many common data dependencies can be characterized by graphs: time series data are sequential (chain graph), i
Highly overparameterized models often predict well despite interpolating training data in complex domains, cha
Modern AI models such as tabular foundation models and gradient-boosted ensembles can outpredict classical met
Timely risk classification is essential in many clinical monitoring settings, where decisions must balance the
We establish a strong averaging principle for fast-slow stochastic differential equations with a time-dependen
Interpretability is critical for machine learning models deployed in scientific space missions such as ESA's A
The dynamics of particles in the early universe are described by Boltzmann equations, which involve high-dimen
We analyze a variant of stochastic gradient descent with initial regularization (SGDIR) and derive dimension-f
We develop a finite-width geometric framework describing how learned feature geometries are organized, transpo
A rapidly growing range of sequential data tasks, such as identifying trend reversals in financial markets, au
This paper studies the Sinkhorn distributionally robust hypothesis testing (SDRHT) problem, seeking a robust d
Deep Equilibrium Models (DEQs) compute predictions from a hidden representation unchanged by the model update.
Mixed-integer programming (MIP) lies at the core of operations research and industrial optimization. While lar
長時間のビデオ生成を実現するためのモデルのサポートを紹介している。
Q-learning with linear function approximation can be unstable because an arbitrary approximation architecture
We design and analyze randomized strategyproof mechanisms for multi-facility location under the utilitarian so
The rapid growth of Low Earth Orbit satellite constellations has created increasing demand for efficient and s
Zeroth-order (ZO) optimization estimates gradients using only forward-pass evaluations, making it suitable for
Reservoir computing (RC) couples a fixed recurrent dynamical system with a trained lightweight readout, but th
We study security games on attack graphs, where an adaptive attacker seeks to reach a target by sequentially a
Rust言語でCandleライブラリを利用して、PythonやPyTorchを使用せずにDecoder-only LLMを自作した。
この論文では、ディフュージョンモデルの高速化を目的としたNVIDIA FastGenについて説明しています。FastGenは、ディフュージョンモデルから高速に生成することが可能です。
The study employed an Artificial Neural Network in combination with the optimized Adaptive Moment Estimation (
We develop analytical and particle-based methods for uncertainty propagation in random neural network models,
Current approaches to simulating biological neural circuits, whether on general-purpose hardware or dedicated
A recurring pattern in neural computation is the reintroduction of dynamical and biological structure into mod
Deep Learning models can include billions of parameters or more, making it difficult to explain their internal
Biological and artificial systems offer complementary forms of adaptation, learning, and computation, with adv
A convolutional sequence labeler's receptive field is routinely treated as the extent of the model's usable co
We study the classical and parameterized complexity of efficient connected allocation problems on graphs, wher
Knowledge-Based Visual Question Answering (KB-VQA) relies on retrieving external information to answer queries
Learning algorithms are often used to make decisions in repeated multi-agent environments. When another player
Regret matching+ (RM+) is parameter free, scale invariant, and central to large game solving, but its only gen
We study the trade-off between firm-side fairness and coalition stability in many-to-one matching markets with
In collection operations, accumulating payload progressively slows the vehicle, imposing a cumulative penalty
Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an
Shared autonomy requires principled mechanisms for allocating and transferring control between a human and an
Global workspace theory explains conscious access as the broadcasting of selected information to the rest of t
Deciding whether a Sokoban puzzle is solvable is PSPACE-complete (Culberson, 1997): solutions can be exponenti
Generative search engines (GSEs) answer user queries directly from crawled web content. The capture of value f
An invariant behavioral profile is the defining vulnerability of traditional honeypot installations: a skilled
Spiking Neural Networks (SNNs) serve as core architectures for neuromorphic computing thanks to event-driven o
We introduce the Open-Strategy Dictator Game (OSDG), a variant of the classic dictator game in which each play
Computing Nash equilibria in interdependent security (IDS) games on networks is computationally expensive: bes
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep neural networks by e
Liquid民主主義の選出問題において、選出プロセスを効率化するための手法を提案。各意思決定者が他者に信頼する方法を考慮し、意思決定者間の協力と意思決定の正当性を考慮する。
このライブラリは、コンピューター ビジョンのための高度なAI解釈と可視化ソリューションです。このライブラリは、CNN、ビジョン トランスフォーム、分類、物体検出、分割、画像類似度など、さまざまなコンピューター ビジョンの
OpenRLHFは、Ray上に構築された強化学習フレームワークです。このフレームワークは、PPO、DAPO、REINFORCE++など、様々な強化学習アルゴリズムをサポートしています。
spiking vision transformersではQuery-Keyスコーリングが伝統的なDenseネットワークから継承されていたが、spike timingはspikingネットワークにおける自然変数である。L
Spiking Neural Networks (SNNs) encode information through binary spikes and compute in an event-driven manner,
Finding a representative description of graph entities that captures their structural roles and homophily is a
この研究では、半推測状態の状況でアダプティブ行動を示すために、反射的な組織がどのように機能するかを調査する。これでは、現時点の観察だけを基に、内部状態が情報を保持できる計算プロパティを開発します。
Spiking Transformers model token interactions primarily through spiking self-attention (SSA). However, binary
Full justified representation (FJR) is among the strongest known satisfiable proportionality axioms for approv
As the adoption of satellite-enabled Internet of Things (IoT) continues to rise, its intricate multidomain arc
The emergence of language-based AI agents promises to transform the scope of machine economic activity. Instea
We study the agent-wise disjunction of two central fairness notions for indivisible items, where every agent m
We study a noncooperative resource-allocation game in which $m$ players distribute fixed resources among $n$ p
Inventory and distribution planning in Physical Internet networks requires coordinating factory-hub assignment
この研究では、BDH-CQ（Bert Discriminator-Hidden Chain Question）と呼ばれる新しい認識モデルを提案した。これは、従来の言語モデルに加えて、リーソン（reasoning）機能を持
Inspired by possible future markets of autonomous routing and driving (ARAD), we introduce competitive mediato
Spiking Transformers provide a promising paradigm for efficient visual processing with spike-driven computatio
Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing.
The leximin++ proof of Plaut and Roughgarden for agents with identical monotone valuations gives a natural EFX
The peer-review process, the bedrock of scientific advancement, is increasingly undermined by sophisticated co
Generalized Hopfield networks are introduced where memories and neurons are continuous variables that lie on a
In evolutionary algorithms powered by language models, the LLM acts as a single operator that simultaneously u
Large mixture-of-experts (MoE) language models with 26--120 billion parameters exceed the memory capacity of c
In a finite recursive game in the sense of Everett, both players have stationary epsilon-optimal strategies fo
State-space models (SSMs) provide a powerful theoretical framework to enable parallel training of recurrent ne
Generative models are often evaluated through singular artifacts, whereas human creativity typically emerges t
Relayは、計算コストを削減しながらLLMエボリューションのための並列プロセスの一時的なハンドオフを実現します。
エージェント評価のプロセスを迅速化するために、AV-AIVATは、イレギュラー情報ゲームにおいて、停止条件を確実に確立し、結果が明らかとなって停止できる手法を提案します。
固定価格の物を分配する問題は重要な問題である。Fair and Efficient Balanced Allocationsは、固定価格の物を分配する方法を提供し、利益を最大化する。
回帰ニューラルネットワークの接続を簡素化する方法がいくつか提案されてきたが、生物学的背景に基づいた方法は少ない。研究者たちは、これまでノイズから切断するノイズ-プルーンという方法を提案しており、これは再起動後の機能を最も
Particle swarm optimization (PSO) has been widely applied to solve complex optimization problems from real-wor
データドライブの日常生活時間の最適化を促進するために、Quality Diversity を使用して時間と健康の関係を考慮したオプティミゼーションテクニックを提案した。
We present a unified theoretical framework connecting classical universal approximation theory, Fourier-featur
Can cooperation among large language model (LLM) agents be evolutionarily stable against free-rider invasion?
In approval-based multiwinner voting, voters submit an approval ballot on which basis a committee of fixed siz
通信と計算のコスト
We study the expected average rank achieved by the Efficiency-Adjusted Deferred Acceptance (EADA) mechanism in
Spiking point cloud networks usually scan space in a fixed, input-agnostic order, which leaves the most distin
作品たちは、大規模言語モデルによって生成された結果の理解を進めます。大規模言語モデルは、概念を生成するための新しいメカニズムを提供します。
LLMが外部アクションを取り続けている場合、エージェントのメモリーが古くなったり誤った情報を持ったりする可能性があります。この問題を解決するために、この研究ではSafeCommitという技術を提案しています。SafeCo
ANNとSNNのファイダテッド・ラーニングを容易にするために、研究者たちはAS-FedBridgeという技術を提案しています。AS-FedBridgeは、ANNとSNNの間の代表的不整合を解消するように設計され、両方のネ
フリーザントランソーメールの特性化を改善し、非線形のプローブを使用した。
この研究では、記憶を維持し更新する能力を強化するため、繰り返し神経ネットワークを用いて記憶の特性を分析しました。記憶を維持するための神経計算の一種として、ダイショビュールノーマリゼーション（Divisive Normal
Handwritten Text Recognition (HTR) is computationally imbalanced in two ways: most image pixels are background
Spiking Neural Networks (SNNs) enable event-driven computation with sparse activations, but building multimoda
This paper explores the challenges and the methodologies associated with learning quality representations in s
The emergence of orientation selectivity in the primary visual cortex (V1) remains a central question in compu
Genetic Programming Symbolic Regression (GPSR) generates mathematical expressions to model input-output relati
複雑性の進化をモデル化する手法としてHash Chemistryを提案した。この手法には、複雑な現象をモデル化し、複雑性の進化を理解できるようになる。
ニュロモーティックコンピューティングの研究を目的としたフレームワーク
In dynamic multi-mode project scheduling, activities have alternative execution modes and uncertain durations,
Spiking neural networks (SNNs) are promoted as an energy-efficient substrate because sparse, event-driven acti
回避可能な計算資源である雑音を扱い、学習と推論を行うための神経回路モデル(NNN)を提案。このモデルが学習し推論するための、伝達の反対方向の重みトランスポートの問題を回避できた。
交通量の予測は、自律走行車や交通ルートの調整など、知能交通システムの基本機能である。この仕事では、大規模言語モデルのニュアラーキテクチャ探索を使用して交通量予測を改善し、交通量予測モデルを設計するために使用された手法と課
Particle swarm optimization (PSO) is a widely used metaheuristic, prized for its simplicity and small paramete
Elasto-plastic boundary value problems in geotechnical engineering are conventionally solved by the Finite Ele
We show with experiments and system-level simulations that it is possible to successfully mitigate the impact
Echo-state networks enable efficient temporal learning by fixing the recurrent dynamics and training only a li
大規模データを処理する環境では、クラスタリングアルゴリズムのスケーラビリティは重要である。Density-Based方法 (例: DBSCAN) では、ノイズや線形クラスタリングに対する強靭さがあるが、ノイズの有無や長い
EEG foundation-model gains may depend on cohort, montage, or probe design. We evaluated five models on five ta
Expensive constrained optimization problems in real-world industry design often involve constraint thresholds
The standard CEC 2017 benchmark applies bias, shift, and rotation simultaneously, confounding their individual
Selection Hyper-heuristics (HHs) automate algorithmic design by selecting from a set of low-level heuristics w
Artificial Intelligence (AI) systems often perform well on isolated tasks but struggle under continual learnin
シミュレーション駆動設計では、高精度なシミュレーションを少なくすることで設計を実現しています。既存の手法では、その問題に取り組むために最適化アルゴリズムが改善されてきましたが、問題の定義自体は検討されていません。この論文
Modern neural networks primarily adapt through parameter modification within predefined computational structur
静的な制約の下で動的な可動体システムを生成するために、制約を考慮した流れを検討します。この方法は、シンボリック制約を使用して、生成された流れが構造的に可能であることを確認します。
We study the depth required by ReLU networks to exactly represent piecewise linear functions, focusing specifi
この研究では、人工知能の研究者と神経科学者の間の分野を結びつけるために、脳のシステム構造を研究し、その研究から導かれた新しいアプローチを提案しました。
Multi-object tracking (MOT) plays a fundamental role in visual perception, where accurate trajectory predictio
We present a regression-based approach to Arabic dialect geolocation that models dialectal variation as a cont
fMRIデータから視覚情報を解釈するために、スパイクニューラルネットワークを用いた方法を提案し、fMRIデータから視覚情報を解釈する検証を行う。
この論文では、機械学
Direct feedback alignment (DFA) trains hidden layers with fixed random projections of the output error, avoidi
動的グラフの構造と意味のパターンを捉えるため、最新の研究では、ラベル付けされたデータの欠如に対応するために、生成的または対比的のパラダイムを導入する。ただし、これらの方法は複雑なエッジレベルからの再構築の目標に依存し、グ
We study the expressivity of shallow polynomial neural networks (PNNs) with monomial activation functions over
Biological evolution sustains complex dynamics without any fitness function, yet virtually all evolutionary al
Can evolutionary dynamics characteristic of biological development arise without a designer-specified fitness
Large-scale multi-objective optimization problems (LSMOPs) are challenging due to their high-dimensional decis