「generation」の検索結果
336 件Unsloth Studioは、オープンモデルのトレーニングと実行を支援するWebUIです。このライブラリは、Gemma4、Qwen3.5などのオープンモデルのテストとトレーニングを支援するために使われます。
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、データとAIアルゴリズムを製品化するためのプラットフォームであるTaipyを提供しています。
.diffusion モデルのライブラリ。画像・動画・音声生成に利用可能。
Rustを使ってモジュラーLLMアプリケーションを構築することができるライブラリです。
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。
xtunerは、超大規模MoEモデルを高速にトレーニングするためのトレーニングエンジンです。
ベクトル検索と構造化されたフィルタリングを組み合わせたベクターデータベースです。
TensorZeroは、LLMゲートウェイ、オブザーバビリティ、評価、最適化、実験を統一したオープンソースのLLMOpsプラットフォームです。
flyteは、高度に動的で堅牢なAIオーケストレーションプラットフォームであり、データ、モデル、コンピューティングを統合してAIワークフローを作成することができます。
音声認識、声活動検出、テキスト処理などを行う、基盤となる音声認識ツールキットを提供する。
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。
統計チャートの生成は、タブラーのデータから生成することが難しい。新しい作成フローでは、データのスクリーン、プロット提案、コード生成、レンダリング、検証による改良が含まれる。
LightX2Vは、軽量の画像およびビデオ生成推論フレームワークです。
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。
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。
ゼネレーティブAIに関連するリソースの一覧。
(この項は、MiniOneRec — Minimal reproduction of OneRecのリポジトリで説明しているため、このリポジトリは削除しました)
モデルをサービングするためのライブラリを紹介している。
AIエージェントの開発と実装を行うためのエンドツーマンド、コードファーストのチュートリアル。
Trade-up recommendation identifies higher-quality alternatives that preserve a customer's purchase intent whil
Generating categorical graphs requires choosing node and edge types that form a coherent structure without dep
Forecasting time series accurately is critical for applications with complex data ranging from energy systems
AI-driven de novo molecular design offers a promising route to accelerate early-stage drug discovery by genera
Large Reasoning Models (LRMs) achieve superior problem-solving through extended Chain-of-Thought (CoT) generat
Diffusion probabilistic models can capture the multi-modal, interaction-rich distribution of joint future traj
Bidirectional discrete diffusion model appears naturally suited to genomic modeling because it can reconstruct
Learning-augmented algorithms use fallible predictions while retaining formal performance guarantees. This sur
Distillation is common in LLM post-training, where on-policy knowledge distillation (OPKD) uses student-genera
Designing viable drug candidates requires searching a combinatorially large and rugged chemical space for mole
As generative audio models grow in complexity, the computational and ecological costs of synthesizing everyday
Multimodal Graph Neural Networks have become standard for recommendation by augmenting sparse interaction data
Diffusion TV is an interactive AI art installation that offers a tangible and embodied experience of diffusion
Wireless signals with position-related labels are pivotal for both performance evaluation and model training i
Data-sovereignty regulations increasingly require public institutions to deploy open-source, on-premise LLM ag
We propose Ref-GeNVS, a training-free, reflection-aware method for generative novel view synthesis (NVS) in mi
Machine-learning performance modeling is a uniquely hostile terrain for long-lived software: the assumptions b
Model upgrades are routine; memory migrations are not. An agent can keep the same memory store and still forge
Quantum circuits are central to implementing quantum algorithms on quantum devices, where quantum gates must b
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
Autonomous AI agents increasingly select actions in environments whose memory, execution-time, runtime, comput
Formalizing mathematics in a proof assistant, where a machine checks every definition, statement and proof, ha
This paper addresses navigation by composite heterogeneous robots in a decentralized system when policy reason
Agents tend to optimize, select, or constrain execution structures before decisive runtime outcomes are observ
Recent structured RAG methods leverage tree- or graph-based reasoning structures to improve multi-hop QA. Howe
Graph pooling aims to compress the graph, including both node embeddings and their underlying topological patt
Large language models (LLMs) have significantly advanced automated program repair (APR), yet existing evaluati
Improving an industrial recommender is an iterative research-and-engineering process rather than a direct path
Computer-use agents can execute increasingly complex tasks in graphical interfaces, but their interaction expe
Text-to-audio-video (T2AV) generation has advanced rapidly, but its evaluation still underestimates the audio
Financial large language models are increasingly deployed for summarization of reports and disclosures, where
Synthetic medical time-series generation can alleviate data scarcity and support the development of reliable c
Contextualized visual personalization can retrieve a true record yet apply it to the wrong visual subject. We
Activation steering has shown promise for controlling LLM generation along well-defined attributes, but it rem
Diffusion language models (DLMs) offer a non-autoregressive alternative for mobile edge agentic artificial int
Large language models (LLMs) increasingly rely on information retrieval (IR) systems, such as Retrieval-Augmen
Generative AI research has increasingly evaluated factuality, citation, coverage, and report structure. Yet pa
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
AI coding systems are moving from autocomplete and chat toward agents that can inspect repositories, edit mult
Self-evolving language models improve by proposing candidate updates and keeping whatever raises a visible sco
Natural-language-to-SQL systems have ad- vanced rapidly on academic benchmarks, yet production enterprise sche
We introduce PetQA, a Korean long-form question-answering (QA) benchmark for evaluating veterinary knowledge a
A 0.6B language model, asked to verify 1,200 logical conclusions (half valid, half corrupted by a single seman
Memory-based evolutionary algorithms for dynamic optimization often carry a redundant second copy of the genot
Retrieval-Augmented Generation (RAG) enhances language models with external knowledge, but the lengthy retriev
Vision-Language Models are commonly evaluated through their final predictions, but understanding whether these
Long-form literary narratives pose a distinctive information-processing challenge for retrieval-augmented gene
This paper describes the participation of the BIT.UA team from the University of Aveiro in the 14th edition of
We construct a corpus of 1,262 verse-commentary (urai) pairs from five Classical Tamil source sections, rangin
Language models are trained on tokenized text that obscures the sound structure of words, yet they reliably pr
In persona-based dialogue generation (PDG), LLMs often overuse persona attributes by incorporating them regard
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
We study the problem of generating a compositional 3D representation of a cluttered scene containing hundreds
Palynomorphs (microscopic, organic-walled fossils such as pollen, spores, and dinoflagellates) are important h
Continuous sliders are useful only when coefficient changes produce predictable image changes. Yet most diffus
Image generation and editing models have advanced rapidly, yet remain unreliable when prompts require external
Autonomous systems require robust low-latency perception under rapidly changing scene dynamics and challenging
Acquiring high quality annotated medical image data is critical for training deep learning models; however, an
In the field of computer vision and graphics, high-quality reconstruction of the human body in static scenes h
Personalization models generate new images guided by a few subject references, while style transfer methods ai
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
Interactive visual world models must distinguish observation from physical intervention. Camera motion reveals
We present InterSing, a framework for generating realistic 3D head animations for duet singing performances. U
Single domain generalization (SDG) aims to learn a model from one labeled source domain that generalizes to un
Zero-shot 6D pose estimation pipelines increasingly rely on strong downstream pose solvers, but their performa
3D surface cutting and UV unwrapping are fundamental problems in computer graphics. Traditional geometric opti
Synthetic Aperture Radar (SAR) images have all-weather, day-and-night observation capabilities. However, compa
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
Robotic systems increasingly operate in dynamic, uncertain, and open-ended environments, where design-time ass
Achieving robust SLAM in large-scale underground coal mines with complex structures and severe degeneracies re
World-action models guide action generation with predicted future observations, but vision-centric predictions
Fixed 3D Gaussian Splatting (3DGS) reconstructions provide realistic novel views but lack the traversability c
デバイス上のLLM推論をXビット量化を使用したもの。
この論文では、映像 diffuision モデルを用いて変化する動画の差分を予測する方法を説明する。
医学画像に対する疾患検出モデルを開発し、臨床現場で早期検出と迅速な介入を容易にすることを目的としたフレームワークを提案します。
Language generation is almost universally treated as a sequential process: autoregressive models emit one toke
Transferable coarse-grained (CG) force fields compress chemical space: by aggregating atoms into a reduced set
Large-scale logistics networks require synthetic data generation capabilities to support scenario-based planni
Language-model judges now gate training data, score generations, and drive leaderboards. The judge is then a m
Reasoning traces from chain-of-thought models appear to offer a legible window into how a model arrives at its
Current conditioned generative models heavily rely on score functions for guidance during training. When the g
Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressiv
Brain-MRI inpainting replaces a masked region of a scan with synthesized, anatomically plausible healthy tissu
Agent evaluations report a tool-call rate read off the serving stack. That number can be zero while the model
Medical image inpainting has the potential to improve automated brain MRI analysis by reconstructing healthy t
Reinforcement learning (RL) has substantially advanced code generation with large language models (LLMs) throu
RL-based post-training for reasoning models is increasingly bottlenecked by repeated fresh rollout generation,
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
We introduce Stateless Bernoulli Watermarking (SBW), a new statistical watermark for Large Language Models tha
Accurate cloud-cover forecasts are important for temperature prediction, radiation forecasting, and solar-powe
Despite increasing reliance on LLMs that reason with external evidence supplied by tools, retrieval-augmented
As integrated circuit technology scales into the nanometer regime, the traditional disconnect between logic sy
Diffusion reinforcement learning (RL) has recently achieved significant success in post-training image and vid
Semantic ID (SID) generative recommendation predicts the next item by generating a short tuple of discrete tok
This paper examines the relationship between the parameters of autoencoder models and the statistical properti
Personally Identifiable Information (PII) detection is a foundational component of data protection infrastruct
High-dimensional simulation of multivariate extremes is fundamentally limited by the combinatorial complexity
LLM-based code generation fails when correctness depends on execution-dependent coupling: the meaning of one r
Whisper is a widely used foundation model for automatic speech recognition (ASR), but its generative decoder c
Designing and authoring high-performance custom kernels for accelerators is a complex task that requires deep
Recent work on controllable music generation has focused on autoregressive models, leaving diffusion-based sys
This paper investigates structural priming in language model (LM) production, examining how preceding structur
Large language models deployed in high-stakes settings frequently generate plausible but ungrounded claims. St
Multilingual language models often produce inconsistent answers to semantically equivalent questions across la
Real-world dynamics are inherently compositional: multiple entities move simultaneously within a shared scene,
Enterprise AI deployments fail not from model inadequacy, but because organizations lack a structured substrat
Constant optimization refines the numerical coefficients of candidate expressions in tree-based genetic progra
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
Early-onset colorectal cancer is increasing among younger adults, yet red-flag symptoms in this age group have
Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or
Neural machine translation (NMT) systems typically produce a single output per input, obscuring the alternativ
Background: Researchers increasingly use repeated identical prompts to audit stochastic variation in large lan
While diffusion base models such as GPT-Image-2 and Nano-Banana exhibit remarkable visual expressiveness, thei
Procedural instruction following is a basic requirement for controllable language-model systems, especially wh
We present Alignment-Free Text-Audiobox (Text-AB), a unified framework for high-quality voice dubbing and full
AI reviewers can now produce many specific criticisms, but more criticism is not necessarily a better review.
Question answering agents in long-term conversations must reason over massive, temporally dispersed dialogue h
Large language models (LLMs) have become ubiquitous tools for code generation and editing. However, developmen
AI-assisted computer-aided design (CAD) for industrial products involves two challenging phases. Part-level ge
Third-party retrieval-augmented generation (RAG) marketplaces create a new auditing problem: data providers ma
LETHE (Latent-parameter Evolution with Temporal Hierarchical quasi-Equilibrium) is a self-referential sonic-ob
The comparative analysis of banks' financial statements poses significant challenges for automated question an
Chain-of-thought (CoT) reasoning improves large reasoning models (LRMs) on complex tasks but often produces lo
In low-resource settings, deploying TTS typically requires choosing between a large voice-cloning model with c
Retrieval-augmented generation (RAG) can improve the specificity and grounding of large language model respons
Reasoning traces of large language models are widely read as containing "breakthrough" moments and early-legib
In frame semantics, sentence comprehension is assumed to proceed by relating lexical meaning to background kno
The Neural Finite State Machine (NFSM) framework offers a pragmatic path to full-duplex dialogue by serializin
Large Language Models (LLMs) often help users generate artifacts through iterative cycles of generation and re
Eye movement biometrics (EMB) is an emerging behavioral modality for user authentication, particularly in virt
Visual Place Recognition (VPR) localizes a query image by retrieving database images of the same or nearby pla
While data-driven 3D shape correspondence estimation has recently seen substantial progress, robust matching u
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
Generative image models can now produce high-quality images, follow complex instructions, and support precise
Video virtual try-on (VVT) aims to generate realistic videos of a person wearing a target garment. Recent meth
Seeing frames in order does not mean representing time. Modern VideoLMs receive ordered video streams, yet the
This paper proposes AdaRoboVLG, a task-adaptive Vision-Language-Grasp (VLG) framework that supports generaliza
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
We present OctWorld, a video diffusion framework with persistent 3D memory for generating explorable, world-co
Scalable Vector Graphics (SVG) generation is attracting increasing attention as generative models improve in e
We introduce LLaDA-Image, a unified framework that pairs a 6B Diffusion Transformer (DiT) trained from scratch
Mirrors are common in real-world images, yet producing geometrically consistent reflections with generative mo
Video generators build long videos by composing shorter parts, either by generating segments one after another
3D scene representations like NeRF and 3D Gaussian Splatting (3DGS) suffer severe artifacts in sparse-view set
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
We present FlashRender, a few-step generative rendering framework that retakes a source video along a target c
Action-conditioned video models require large-scale visual data paired with control signals that are temporall
Generative retrieval has demonstrated significant success by unifying representation learning and search into
Existing safety alignment methods for vision-language models usually modify the model behavior globally: once
3D Gaussian Splatting has become a de facto scene representation for novel view synthesis, yet robustly learni
Answering what-if queries about a scene with a VLM usually means injecting the assumption as text or repaintin
Automatic generation of hand gestures is essential for the transmission of Indian classical dance and critical
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
We present PointGT, a point-based 3D representation that enables simultaneous editing of object geometry and a
Contact-rich loco-manipulation requires a bridge between semantic action generation and physical interaction c
World models have progressed from compact latent dynamics to generative, controllable, and interactive simulat
This paper presents an end-to-end computational pipeline that converts a selected object mesh, a measured obje
Audio-video diffusion models rely on cross-modal attention to coordinate text, sound, and visual content, yet
Scaling interactive and verifiable environments is critical for training terminal agents. As frontier models b
Generative models have been studied experimentally and theoretically as priors for inverse problems such as co
Reinforcement learning typically optimizes average reward. For generative policies, the average can hide an im
Learning graph structures from data is a fundamental problem that spans a wide range of signal processing and
Generative modeling directly on geometric manifolds can avoid errors introduced by flattening non-Euclidean da
Stochastic gradient Markov chain Monte Carlo (SGMCMC) methods enable scalable Bayesian inference, but their pe
Heuristic design for combinatorial optimization remains heavily reliant on expert knowledge, while existing la
This chapter reconstructs the Hopfield network as a physical theory of memory rather than merely an early neur
Writing proficiency manifests in how students develop content, organize ideas, choose words, and use language.
Expressive speech systems make a decision before any waveform is rendered: how an utterance is delivered. In d
This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced
Libraries and archives manage large collections with limited staff and computing budgets, yet common benchmark
Language models spend most of their attention on a small fraction of context, yet they read the entire KV cach
Autonomous robots powered by deep learning face a fundamental auditability challenge: when incidents occur, in
Generating safety-critical scenarios is essential for evaluating autonomous driving systems. However, existing
In trajectory planning for autonomous driving, hybrid planning architectures are often realized as a collectio
Joint audio-video generation models have made substantial progress in visual quality and audio-visual synchron
Visual fluency in generated video does not imply physical reliability, and a scalar quality score alone is inc
Faithfully translating research papers into repository-level implementations remains challenging because paper
Model compression techniques such as pruning and quantization facilitate the efficient deployment and accelera
NestJSベースのAIチャットボット開発ツールです。
マルチラギングスピーチ生成やクリエイティブボイスデザイン、ルートライフクライミングなど、テクスチャファリーTTSの最新技術を実現するためのフレームワークです。
Generative data augmentation is widely used to mitigate class imbalance, yet its theoretical effect on downstr
Uniform-state discrete diffusion models update all tokens in parallel while keeping every position revisable.
Sampling from distributions conditioned on desired semantic properties is an emerging challenge in modern gene
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
Black-Box Optimization (BBO) has broad applications, while traditional algorithms such as evolutionary algorit
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
In camera-controlled video generation, geometry-aware positional encodings condition tokens on camera extrinsi
Vision-and-Language Navigation (VLN) requires an agent to navigate through unseen 3D environments according to
High-precision insertion remains a fundamental challenge in robotic manipulation due to the strict alignment r
Intelligent vehicles increasingly support adaptive applications beyond driving themselves, ranging from contex
Animals' body morphology shapes the gait patterns they can perform, where mechanical resonance reduces the nee
Retrieval is the first stage of modern search and advertising systems, selecting a candidate set from a large
Compact token sequences are essential for efficient 3D generation. However, existing 3D tokenizers typically o
An image may be worth a thousand words, but most captioning models describe it in only a few. Modern vision-la
Embedding-based code retrieval is a core component of coding agents and retrieval-augmented code generation, w
Large language models (LLMs) trained only on text and code can sometimes generate programs that draw recogniza
Understanding animal motion is fundamental to modeling animal behavior and biomechanics, yet progress in this
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
分析システムの性能を向上するための学習モデル開発を行う。
学習中のアイデアや知識を整理するための日記。
When do text embeddings work as inputs to empirical analysis? Their use rests on an assumption: that we can tr
Neuroevolution of Augmenting Topologies (NEAT) and its advanced version, Evolvable-Substrate HyperNEAT (ES-Hyp
The performance of artificial intelligence (AI) and machine learning (ML) models degrades when the problem the
Chain-of-thought (CoT) reasoning powers generative models by eliciting intermediate steps before producing an
World models have made remarkable progress in action-conditioned future prediction for embodied agents, yet st
Robotic perception from a single viewpoint is often limited by self-occlusion and incomplete surface visibilit
Direct vision-language-action policies generate continuous robot actions efficiently, but standard behavior cl
Vision-language-action (VLA) models remain brittle in manipulation tasks that require physical commonsense. Cu
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
AIパワードシステムやデータパイプラインの監視と評価を行うための、オープンソースのMLとLLMの観測性フレームワーク。
Awesome-Video-Diffusionは、Recent Diffusion Models for Video Generation, Editing, and Othersのリストを公開しています。
Token prediction is a central pre-training objective for modern language models. Despite its empirical success
Missing data, measurement error, and population heterogeneity are pervasive challenges in analyzing data arisi
Large language models are increasingly required to generate responses that satisfy multiple competing objectiv
Rubric-based reinforcement learning extends RL beyond tasks with exact answers or rule-based verifiers by scor
この作品は机械学習の論文を100行のコードで実装する方法について説明しています。
Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation an
While modern generative models excel at modeling complex data, precise inference-time control in conditional g
Text-driven 3D generation has advanced rapidly in creating large-scale outdoor environments and detailed indoo
Automated bidding (autobidding) is a core component of modern online advertising systems. Within this componen
この論文では、Causal-Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive
Bayesian inference in compound loss models must often be repeated across policies, market scenarios, and prior
Graphs are invariant under node permutations, motivating the use of permutation-equivariant architectures in g
Evaluating customer creditworthiness is crucial for retail banking operations, as it impacts marketing strateg
Generative AI is transforming how people access information, challenging traditional advertising mechanisms bu
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。
Reconstructing continuous physical fields from sparse measurements is central to scientific monitoring, invers
Paradigmatic interaction models explain how collective behaviors can emerge in complex systems from interactio
Using the logical basis of synthesizing Hopfield Neural Network with desired corners of hypercube as stable st
The functional annotation of genes in non-model organisms remains a significant challenge in computational bio
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
Generative models are commonly ranked by Fréchet Inception Distance (FID) and Kernel Inception Distance (KID),
Current LLM agent systems decide delegation before reasoning begins (a router picks a model) or after a respon
Modern score-based generative models have achieved remarkable empirical success in high-dimensional tasks such
Predictive Coding (PC) is a neural learning paradigm that enables parallelizable neural network layer updates.
ES-HyperNEAT evolves substrate topology through adaptive quadtree subdivision; to our knowledge, no implementa
Developments in high-performance computing (HPC) technology continue to drastically increase quantities of ava
Change data synthesis provides a cost-effective solution for expanding training data and improving the perform
販売データを分析するために、機械学習モデルが使用されるリソースが提供されていました。
Wunjo CE: Face Swap, Lip Sync, Control Remove Objects & Text & Background, Restyling, Audio Separator, Clone V
Discrete diffusion models offer a promising alternative to autoregressive generation by enabling parallel upda
Recent advances in continuous diffusion and flow-based language models (LMs) have achieved performance competi
Large language models (LLMs) often produce fluent but incorrect answers with unwarranted confidence. A central
This paper studies the Sinkhorn distributionally robust hypothesis testing (SDRHT) problem, seeking a robust d
This paper proposes the Coronavirus Optimization Algorithm (COA), a SARS-CoV-2-inspired success-history adapti
Analog circuit topology synthesis remains challenging because useful designs occupy a tiny fraction of a combi
Population optimizers such as CMA-ES, DE, and multi-objective evolutionary algorithms drive search mainly thro
本研究では、生成推奨システムにおけるアイテムIDの構築、調整、生成の手法について、アイテムIDの構築方法を分析しています。
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。
OpenWorldLibは、進化する世界モデルを提供する統一されたコードベースです。
Zeroth-order (ZO) optimization estimates gradients using only forward-pass evaluations, making it suitable for
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。
医療画像分析で、深層學習モデルが実装されている問題に対する解決策を提示します。治療を導くために、批判的結果に影響を与える変化について特に重点が置かれています。
Historical newspapers are an abundant record of public life, but their dense, irregular and sometimes noisy la
Large language models can generate executable programs, which makes it possible to search directly over proced
We reconstruct the mentor--student network through which documented scholarly training passed across roughly n
We investigate whether agentic artificial intelligence can automate parts of the process of designing genetic
Most LLM-based automated algorithm design methods optimize a designated component within a human-specified sca
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
AIドライブのマルチエージェント研究アシスタント。仮説の生成、データ分析、およびレポートの生成を自動化する。
画像エディティング用推論モデルの改良方法についての公式実装であるFlowEdit。
An invariant behavioral profile is the defining vulnerability of traditional honeypot installations: a skilled
エッジ・アセンブリ・クロスオーバーを用いた進化的マルチタスクアルゴリズムを用いて、旅行販売人が最短のルートで目的地へ移動する問題を解決した。
マルチモデルのロードバランシングを考慮するために、TEMPOというフレームワークを提案。このフレームワークは、テストケースごとに最適なロードバランシング方法を決定できる。
画像生成のためのHigh Quality Training Free Inpaintを提供します。このInpaintはStable Diffusionモデルに使用でき、ComfyUIもサポートしています。
Emotion-driven Style Controlを使用してテキストから声の変換が実行され、感情のあるテキストをエモタイザブルな声に変換することが可能になります。
Competitive artificial-life systems can rank trained controllers differently under training and ecological eva
Inventory and distribution planning in Physical Internet networks requires coordinating factory-hub assignment
理論オブミンドの評価基準「Avalon-ToM-Bench」を提案。社会的認識を評価するための基準を提供する。
A common problem in social choice is to determine whether there is a social choice procedure, such as a voting
Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate pr
We develop a formal framework for analyzing indirect geoeconomic influence. The influencing state (sender) doe
Generative models are often evaluated through singular artifacts, whereas human creativity typically emerges t
この論文では、自律的な生成モデルの内部表現を分析し、未知のデータ分類に基づいて順序性が生じていることを示します。
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?
Generative models can reproduce an observational distribution while encoding an incorrect causal structure. We
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。
Automated formulaic alpha discovery aims to generate predictive and interpretable trading signals from large s
この論文では、スワームと進化アルゴリズムを分析するための新しいアプローチを提案しています。このアプローチでは、候補生成を分離し、目標によらず変化する部分と、目標に関係する部分を同定します。
Automated heuristic design (AHD) with large language models (LLMs) has produced strong heuristics for combinat
物理的システムの分離方程式を解くためには、ニュラルネットワークの設計、損失関数の定義、および最適化ダイナミクスの手動調整が必要である。研究者は、自動設計のためにLarge Language Models (LLMs)を利
Echo-state networks enable efficient temporal learning by fixing the recurrent dynamics and training only a li
Parent selection significantly affects exploration, exploitation, and complexity control in genetic programmin
Closed-loop generative selection has become a workhorse of computational drug discovery: a learned generative
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
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
Analytical placers rely on differentiable objective functions to guide placement, typically combining intermed
Biological evolution sustains complex dynamics without any fitness function, yet virtually all evolutionary al
Large-scale multi-objective optimization problems (LSMOPs) are challenging due to their high-dimensional decis