「classification」の検索結果
172 件🤗 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。
paperless-ngxは、コミュニティによってサポートされたスーパーチャージドのドキュメント管理システムで、ドキュメントのスキャン・インデックス・アーカイブが可能である。
データラベル化と注釈化を行うためのツールです。
FiftyOneは、データセットの精査とAIモデル可視化を支援するライブラリです。このライブラリは、データセットの品質を高め、AIモデルを可視化するのを支援するために使用できます。
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。
ultralyticsはYOLO(You Only Look Once)の技術を使用したオブジェクト検出ライブラリで、高い精度を提供している。
supervisionは、機械学習技術を活用して、ユーザー独自のコンピュータビジョンツールを作成することができる。
CVATは、機械学習用の業界標準のデータエンジンです。さまざまなスケールのチームが使用し、さまざまなスケールのデータに対応しています。
イメージを注釈するツール。ポリゴン、長方形、円、線、点などを注釈することができる。
CLIPという画像認識モデルをオープンソースとして実装したライブラリ。
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。
このプロジェクトは２Ｄおよび３Ｄ顔の分析を実現するための基盤プロジェクトであり、最先端の技術を導入して顔の分析を実現します。
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。
この研究では、自然言語処理の負担を減らすモジュラリティを目指しています。モジュラリティとは、システムを小さくて独立した部分に分割して、それぞれを簡素化することです。この研究では、文脈に応じてモジュラリティを変更できるメカ
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。
stanzaは、さまざまな言語を処理するための言語処理用ライブラリです。
マシン学習、統計学習などに関する統計的エンジンです。
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。
Trade-up recommendation identifies higher-quality alternatives that preserve a customer's purchase intent whil
Chilli (Capsicum annuum) is one of India's most economically significant crops, yet its productivity is persis
Building on the pioneering paper of Kearns, Roth, and Ryu (SODA'26), we study information aggregation in a net
Late Gadolinium Enhancement (LGE) on cardiac magnetic resonance is a key marker of myocardial scar, but its li
Artificial intelligence models are promising for medical diagnosis, but they require large numbers of unbiased
The number of discrete class-separability jumps observed during ResNet finetuning is examined empirically as a
This study introduces an intelligent framework that integrates machine learning and deep neural network ensemb
Time series data is very common in many real-world applications and in numerous domains, with increasing inter
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
Recent benchmarks such as PowerGraph provide large collections of power-grid graphs for cascading-failure clas
Tabular foundation models (TabFMs) achieve strong performance on structured data, particularly for standard cl
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
Intelligent extended reality (XR) systems increasingly use eye and head tracking to infer user intent, task, a
Recent years have witnessed great advances in the reasoning ability of Large Language Models (LLMs). However,
Commonsense reasoning in computer vision encompasses integrating visual data and contextual knowledge, crucial
Graph pooling aims to compress the graph, including both node embeddings and their underlying topological patt
Multi-expert models have become the dominant paradigm for long-tailed learning, largely attributed to their pr
The widespread adoption of artificial intelligence (AI) within real-world applications has raised a lot of con
Fraudulent messages sent via Short Message Service (SMS) are increasingly obfuscated to evade cost-conscious c
Media bias in news articles operates through subtle linguistic cues---loaded language, selective framing, and
Long-horizon predictive maintenance requires models to distinguish slowly evolving degradation from normal ope
Generative AI research has increasingly evaluated factuality, citation, coverage, and report structure. Yet pa
Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations f
We propose an influence score to quantify the contribution of attention heads to classification decisions in T
Few-shot action recognition (FSAR) aims to recognize unseen action categories with only a small number of anno
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
Acquiring high quality annotated medical image data is critical for training deep learning models; however, an
Single domain generalization (SDG) aims to learn a model from one labeled source domain that generalizes to un
Lesion-focused image classification presents a core analytical challenge, as discriminative signals are often
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
We introduce Mitra-v2, a tabular foundation model that delivers state-of-the-art performance on real-world cla
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
Large language models (LLMs) are increasingly used to annotate cultural texts at scales that are impractical f
Adaptive network intrusion detection systems retrain classifiers after drift alarms, but an alarm detects chan
We explore the use of simulated data for training a model for protein annotation in crowded cryo-electron tomo
Ensuring the security of the power system is essential for stability and reliability, especially in the event
Multi-Task semantic communication (SemCom) prioritizes simultaneous execution of multiple tasks over bit-accur
Label removal occurs frequently in classification systems with evolving taxonomies, where categories must be d
Federated Learning (FL) with Differential Privacy (DP) is increasingly adopted to preserve data confidentialit
Experimental design is commonly framed as choosing the experiment expected to provide the most information. Un
We map tree species across Denmark using National Forest Inventory plots and EO data, while evaluating the pot
Nano self-assembly organizes molecular components into bioactive nanoscale structures. Self-assembled nanopart
Whisper is a widely used foundation model for automatic speech recognition (ASR), but its generative decoder c
Agent reinforcement learning (RL) increasingly runs through full execution harnesses, and a multi-harness reci
Large language models deployed in high-stakes settings frequently generate plausible but ungrounded claims. St
Real-world dynamics are inherently compositional: multiple entities move simultaneously within a shared scene,
Recognizing specific objects onboarded without a labeled training set recurs across manufacturing and service
Purpose: Increased number of chest radiograph (CXR) scans create a triage bottleneck, queueing urgent examinat
A critical challenge in deploying Large Language Models (LLMs) is developing reliable mechanisms to estimate t
Modern misinformation is often heard before it is read, yet fact-checking systems are still evaluated mainly o
ASR systems sometimes produce fluent text that is unrelated to the speech they receive. We view these hallucin
Aligning large language models (LLMs) is essential for their safe deployment. Current alignment methods mainly
Zero-shot vision-language models (VLMs) are increasingly used as training-free species recognizers, but report
In online meeting delegation, LLM agents fail to recognize when to speak. With no structured way to track stan
Isolated Sign Language Recognition (ISLR) is conventionally cast as closed-set classification over gloss label
Synthetic data augmentation has become a common strategy for addressing class imbalance in NLP, but most appro
Land ownership in Bangladesh is recorded in Ana-Ganda-Kora-Kranti-Til, a base-16 positional fraction system wi
The growing scale of academic peer review has motivated the use of Large Language Models (LLMs) as review assi
Emotion recognition benchmarks often predict one emotion per text, missing many real-world scenarios where two
Vision foundation models (VFMs) are valuable in data-scarce domains such as surgery, where a single pretrained
Robots interacting with people must recognize not only explicit commands, but also social cues such as invitat
Visual Place Recognition (VPR) localizes a query image by retrieving database images of the same or nearby pla
Noisy annotations pose a significant challenge for supervised deep learning, as neural networks rely on large-
Verifying that manufactured batches of milling tools or carbide rotary burrs conform to production order sheet
Few-shot fine-grained image classification (FSFGIC) aims to classify similar images with limited labeled examp
Communities are fundamental spatial units that shape urban form and social life. Whether a residential compoun
We present ENEAS, a unified, text-promptable method for instance tracking and semantic discovery. Text-prompta
Sign language dictionaries are essential resources for sign language learners, yet automatically retrieving a
Histopathological subtyping relies on the recognition of characteristic histological patterns. These patterns
Accurate segmentation of corneal layers in optical coherence tomography (OCT) is essential for quantitative as
Vision Foundation Models (VFMs) provide transferable patch representations for few-shot industrial anomaly det
Head-mounted displays (HMDs) fundamentally limit emotion recognition in virtual reality (VR): by occluding the
Existing safety alignment methods for vision-language models usually modify the model behavior globally: once
Automated pig monitoring is essential for assessing their health, behaviour, and welfare. To date, most pig mo
Contrastive language-image learning (CLIP) has become a key paradigm for remote sensing vision-language unders
Parking spot classification is a fundamental task in intelligent transportation systems, yet most deep learnin
Camera traps have become an essential tool for wildlife monitoring, motivating the development of computer vis
Accurate identification of weld seam geometries is essential for automated robotic post processing operations
The ambition of the 2025 PNPL competition (Landau et al., 2025) was to launch a multi-year curriculum for non-
Graph neural networks (GNNs) are a class of neural networks suitable for learning on graph-structured data. Th
A fundamental quantity in machine learning is the optimal performance achievable by any model on a given task.
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
Evaluating LLM agents is essential for guiding their development, yet it has grown prohibitively expensive: a
Vision-language models (VLMs), such as CLIP, have achieved strong performance across multimodal tasks by align
Vision Transformers (ViTs) typically process every image using a fixed input resolution and model width, even
World models are an emerging paradigm in representation learning in which an agent jointly learns state-action
Autonomous robots powered by deep learning face a fundamental auditability challenge: when incidents occur, in
Ultra-low-altitude unmanned aerial vehicles (UAVs) require surround vision near buildings, vegetation, and oth
Post-hoc calibration corrects reported confidence, yet a multiclass calibrator can also change the associated
Traditional speaker-attributed ASR systems treated ASR and speaker diarization as two separate tasks. Recently
Generative data augmentation is widely used to mitigate class imbalance, yet its theoretical effect on downstr
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 consider semi-supervised classification from a partially classified sample arising from a two-component Wei
Early recognition of lane-change intention is essential for proactive decision-making in autonomous driving an
For noisy real-world environments such as those in open public spaces, spoken dialogue systems for both autono
Language-driven dexterous grasp models, such as DextER, perform well when instructions specify where to grasp,
YOLOv5という物体検出アルゴリズムをPyTorchから他の言語に変換できるライブラリ。
CoreNLPはJavaで開発されたNLPツールのセットであり、分割、文分割、名詞認識、パーシング、コorefence、感情分析などを行える。
This paper develops a general convolutional neural network (CNN) framework for detecting heterogeneous event-d
Scientific discovery often requires reasoning over competing hypotheses that are consistent with experimental
Informative label missingness can change the usual efficiency ordering between completely and partially labell
We propose a new design of fair classifiers for multi-class classification problems in the presence of vector-
Neuroevolution of Augmenting Topologies (NEAT) and its advanced version, Evolvable-Substrate HyperNEAT (ES-Hyp
Vision Transformers provide strong visual representations but typically rely on slowly updated parameters, lim
Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solutio
Long-tail autonomous driving failures are often framed as rare-object recognition errors. We argue that this v
Tactile sensing is essential for physical interaction in robotics and human--machine systems. However, combini
Dexterous human-machine interaction requires intuitive and expressive interfaces that can be efficiently deplo
We study complete allocations under nonnegative additive valuations through the positive supports of goods, fo
Understanding agent behavior requires methods that scale to thousands of trajectories and surface new patterns
Token prediction is a central pre-training objective for modern language models. Despite its empirical success
This paper introduces a family of multiclass linear Perceptron classifiers with a multiplicative margin mechan
While modern generative models excel at modeling complex data, precise inference-time control in conditional g
Thermodynamic computers are stochastic physical devices designed to perform calculations at the thermal energy
Nearest neighbor classification relies fundamentally on how locality is defined, yet conventional $k$-NN impos
Kernels measure similarity or correlation in tasks such as regression and classification. The Gaussian kernel,
Object classification in event-based computer vision is a task that is attracting considerable research attent
Automatic Speech Recognition (ASR) technologies have achieved remarkable performance in recent years through t
Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classi
Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although
Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervi
ES-HyperNEAT evolves substrate topology through adaptive quadtree subdivision; to our knowledge, no implementa
Timely risk classification is essential in many clinical monitoring settings, where decisions must balance the
Analog circuit topology synthesis remains challenging because useful designs occupy a tiny fraction of a combi
This paper studies multiple fixed points in a discrete-time hysteresis neural network. The network consists of
spaCyはPythonで動くIndustrial-strength Natural Language Processing(Language理解の高度なライブラリです。文脈理解、構文解析、名詞の抽出など、複雑なNLPタ
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learni
Deep Learning models can include billions of parameters or more, making it difficult to explain their internal
Historical newspapers are an abundant record of public life, but their dense, irregular and sometimes noisy la
Automated machine learning (AutoML) systems search for pipelines within a space of preprocessing operators, le
An invariant behavioral profile is the defining vulnerability of traditional honeypot installations: a skilled
Evolutionary feature construction has shown strong promise in symbolic regression by automatically discovering
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
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
Spiking Transformers provide a promising paradigm for efficient visual processing with spike-driven computatio
Generative models can reproduce an observational distribution while encoding an incorrect causal structure. We
Spiking point cloud networks usually scan space in a fixed, input-agnostic order, which leaves the most distin
Handwritten Text Recognition (HTR) is computationally imbalanced in two ways: most image pixels are background
This paper explores the challenges and the methodologies associated with learning quality representations in s
Many learning problems require representations that reconcile direct input, nearby structure, and broader cont
EEG foundation-model gains may depend on cohort, montage, or probe design. We evaluated five models on five ta
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。
この論文では、機械学
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