「classification」の検索結果
181 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。
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。
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。
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。
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。
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。
Set separation by continuous linear functionals is fundamental in functional analysis and convex optimization
Diffusion models represent a powerful class of generative models known for their solid theoretical foundations
Vision-language models (VLMs) increasingly support construction hazard monitoring, yet their frequent false al
Medicinal plants are usually identified using practitioners' experience, but reproducible, image based methods
Recent advances in large language models have enabled agentic systems that automate machine learning pipeline
An AI inventory is a structured, often public record in which a government or organization lists and describes
AI-generated images have reached unprecedented realism, while instruction-driven AI editors re-synthesize imag
Understanding what kind of prompts users typically ask and how well an LLM performs on these prompt categories
High-resolution range profiles (HRRPs) provide compact radar target representations but exhibit strong aspect
Diffusion-based data augmentation has emerged as a powerful strategy for addressing data-scarce image classifi
Separating touching nuclei requires local boundary detail, while cell type classification requires broader mor
Self-attention has become the standard for modern sequence modeling, enabling Transformers to model long-range
Large 3D repositories make it easy to collect assets, but difficult to determine whether they can be reused re
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。
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。
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。
Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a singl
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
Conformal prediction (CP) provides marginal coverage guarantees, but marginal coverage does not determine wher
A substantial line of work builds rotation invariance directly into point cloud architectures. We benchmark 10
Autonomous vehicles require robust perception systems to operate safely under diverse environmental conditions
Hyperparameter optimization (HPO) returns a single optimal configuration, systematically discarding the explor
Communication is important for our well-being, but it can be impaired by disabilities, such as locked-in syndr
In this paper, we study when two parties should pool their data to train a machine learning model. Two agents,
Most malware detection research focuses on identifying whether a file is malicious before it executes. Far les
Discrete diffusion models remain far less understood than their continuous counterparts, despite recent breakt
Suicide prevention remains a critical public health challenge, and electronic health records (EHRs) provide an
While structural and functional magnetic resonance imaging (sMRI, fMRI) offer profound potential for researchi
In node selection tasks, the goal is to identify a single unknown target node in a graph. We consider probabil
Large-scale annotation of environmental sound recordings presents significant challenges under time and resour
Anomaly detectors rank unfamiliar examples for expert confirmation, and the confirmed examples teach a classif
LaBraM, an early EEG foundation model, demonstrated that large-scale EEG pretraining can yield strong represen
Multimodal fusion is studied under hybrid federation, where data are partitioned across samples and feature bl
Two machines can each recognize an object well and still fail together. We study classifiers that see differen
Representation alignment (REPA) is widely used to accelerate the training of latent-space diffusion transforme
Overall and group-level averages, and ranking metrics such as the AUC, can hide sets of observations for which
Protein-protein interactions underpin nearly all biological processes. Yet, designing proteins with desired in
Federated learning (FL) runtimes standardize aggregation and deployment, but each new study still requires fed
The Model Context Protocol (MCP) lets agentic AI frameworks connect language models to external tool providers
Language models in customer-facing products must be continually updated, since the data they serve shifts over
Respiratory sound classification from auscultation recordings is confounded by heterogeneous acquisition condi
Instruction tuning for multi-task network traffic analysis requires data with clearly defined sources, supervi
Repository-level AI coding benchmarks measure task success but do not directly capture the conditions required
Accurate preoperative prediction of lung adenocarcinoma invasiveness is important for surgical planning and in
Recent advancements in sign language processing can be attributed to developments in both large-scale dataset
Mathematical formalization plays an important role in establishing rigorous and machine-verifiable foundations
Reaction data underpin machine learning for reaction prediction, condition recommendation, and retrosynthetic
The widespread adoption of Large Language Models (LLMs) demands reliable detection of human-authored versus sy
We propose a simple, linguistically motivated approach to detecting AI-generated peer reviews based on the inh
Improving a sentiment classifier with limited resources involves more than simply deciding how much data to co
Programs that use a language model as a classifier act on its most probable label. Text injected into the inpu
Large language models are increasingly used as measurement instruments for socially consequential constructs s
Dialectal Arabic speech recognition remains constrained by the scarcity of dialect-resolved transcribed speech
Robust 3D point-cloud recognition is critical for robotics, mapping, and urban sensing, yet point-cloud classi
Foundational vision models have become the de facto standard for many vision tasks due to their strong perform
Open-set recognition (OSR) requires neural networks to classify known classes while rejecting unknown samples,
Video dubbing aims to synthesize lip movements that match target speech while preserving the speaker identity,
Source-only domain generalization (DG) for remote sensing semantic segmentation seeks reliable predictions in
Earth observation (EO) workflows require selecting among known outcomes, estimating confidence, and processing
Geospatial text spotting involves locating and transcribing geographic labels, such as road names, district na
Fingerspelling - the sequential signing of letters that compose proper names, technical terms, and out-of-voca
An image that is hard to classify on its own may have a helpful neighbor in the same unlabeled batch. Zero-sho
Image-text corpora contain copies of one image, captions that describe one image in different ways, captions r
Multi-source remote sensing image classification benefits from complementary spectral and structural informati
Multi-source remote sensing data classification has received growing attention recently. However, existing met
Adapting features before upsampling can improve dense prediction, but the gains may arise from changes in feat
Urban photogrammetric point clouds are commonly processed as local 3D patches, limiting the context available
Lip reading plays a crucial role in applications such as speech recognition, assistive technologies for the he
Financial document recognition is commonly evaluated using character and word error rates, which do not adequa
Multi-task dense prediction seeks a single, coherent representation that supports simultaneously discriminativ
Face anti-spoofing (FAS) models often learn attack- and dataset-specific cues that degrade performance under u
This paper studies what subspace representations contribute to synthetic-to-real transfer in 3-D surface inspe
Skeleton-based action recognition is a pivotal task in computer vision; however, conventional Graph Convolutio
Joint nuclei instance segmentation and classification requires precise boundaries and neighborhood information
Widespread image sharing on social media has increased the need for visual privacy recognition to identify ina
Jointly trained multimodal networks frequently underutilize weaker modalities, as faster-learning modalities d
Skin cancer classifiers need large sets of annotated images, which are scarce, especially for rare lesion type
Inertial sensors are moving into clothing, from sensorized sportswear to garments instrumented with hundreds o
Hyperspectral image (HSI) classification requires the representation of contiguous spectral signatures and spa
Computed tomography (CT) provides diverse patient information and various clinical tasks, for which numerous d
Wearable robot interfaces must connect user input to timely physical action. We present the proposed platform,
Robust multimodal perception for soft robotic grasping is challenged by modality-dependent noise, semantic amb
Robot policies still fail at everyday manipulation: an unfamiliar task, an object never held before, a scene t
People face the problem of classifying objects into categories while aggregating their opinions. We axiomatica
Faster Whisper transcription with CTranslate2
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。
An invertible graph aggregation preserves the complete feature table, but can increase the size of a classifie
We consider classification in high-dimensional feature spaces where only a vanishing fraction of features are
Training a classifier means letting a validation metric decide which model is kept and when training stops. A
We study the implicit bias of Adam and its similarity to sign gradient descent on homogeneous models in binary
Fairness-aware classification seeks to balance predictive accuracy and fairness across demographic groups. Alt
Selective prediction with split conformal calibration promises a post-hoc fix for molecular property predictio
Machine learning systems are challenging to test using traditional software testing methods because their beha
Coding agents can destroy data, leak secrets and message the wrong person faster than a human can review. Coul
Social media translation requires preserving meaning while capturing the cultural nuances of user-generated co
LLM-based role-playing agents are fluent at imitating a character's style, yet struggle to make role-consisten
Visual-text compression renders long contexts as images and is engineered around how much of the text a vision
Robust segmentation of biomedical images remains challenging primarily due to ill-defined object boundaries. A
Task-optimized deep neural networks have risen to prominence as the most predictive phenomenological models of
Scarce and unrepresentative training data limit the performance of prostate MRI prediction models. Synthetic T
Roadside sensing infrastructure is essential for Intelligent Transportation Systems (ITS), but cameras and leg
Concept-based explanations of melanoma classifiers are rarely tested against controls with known targets. We b
Automated classification of retinal pathologies from Optical Coherence Tomography (OCT) images remains challen
Studies of neural network generalization and robustness often summarize smoothness with a single number, such
Latent-space path planning (LSPP) compresses the configuration space of a robotic manipulator into a compact,
Robotic surface exploration benefits from tactile interfaces that accommodate contact and capture variations i
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。
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。
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
Learning from extensive longitudinal clinical records--spanning repeated encounters, evolving conditions, and
The MetaFine Open Competition (RoboFineMani workshop, CoRL 2026) evaluates fine-grained manipulation beyond bi
Concept erasure is a prominent approach to achieving fairness in machine learning through the removal of sensi
We study the general problem of estimating $D(\dde(S))$, the probability that an unknown distribution $D$ assi
Gating mechanisms allow a fixed neural network to adapt its computation to the current input by dynamically mo
Tactile sensing provides robots with direct observations of contact physics, local geometry, and interaction d
Defense systems for maritime assets must distinguish approaching benign craft from uncrewed surface vessels (U
Continual adaptation requires deciding not only how to update a model, but whether to commit an update without
Adaptive inference systems balance accuracy and inference cost by routing inputs to models with varying capabi
In this work, we formalize the concept of intrinsic dimension of objective landscapes introduced in Li et al.
Parameter estimation and classification in binary logistic regression deteriorate severely when the number of
Learning a shared subspace in multitask learning is an important statistical problem, yet exact rates remain p
Formula-Driven Supervised Learning (FDSL) enables vision pre-training without real images, but existing method
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。
Neural networks are powerful memorizers which are often able to interpolate their training data even when the
This work investigates device-informed spike-timing-dependent plasticity (STDP) in spiking neural networks usi
Ship-to-home fulfillment requires a retailer to select a fulfillment location and carrier before the delivery
Graph neural networks (GNNs) rely critically on neighborhood aggregation, yet conventional mean aggregation ma
Objective: Variable selection for clinical prediction models is commonly done on statistical grounds related t
Field applications in chemical sensing, from portable safety monitors to mobile robots, require models that ar
Prediction models degrade when the deployment population differs from the one used to train them, and training
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
Neural architecture search (NAS) is challenging because of the vast, non-linear space of artificial neural net
Hyperdimensional computing (HDC) offers efficient learning and inference for resource-constrained systems, yet
Replicator dynamics, the central model of evolutionary game theory, describe populations evolving under select
Shapley-value-based explanations are widely used for feature attribution in machine learning. However, in comp
An action seen once and the same action seen three times receive the same action-recognition label, so that su
Text injection trains speech recognition models on unpaired text alongside paired audio. We feed the speech en
中文分词 词性标注 命名实体识别 依存句法分析 成分句法分析 语义依存分析 语义角色标注 指代消解 风格转换 语义相似度 新词发现 关键词短语提取 自动摘要 文本分类聚类 拼音简繁转换 自然语言处理
Spiking neural networks (SNNs) are well suited to event-driven edge intelligence, but the arithmetic complexit
Spiking Neural Networks (SNNs) promise energy-efficient inference through sparse, event-driven computation, ye
EEG foundation models are advancing at an unprecedented pace, yet their evaluation is driven by downstream acc
Smart-city applications often combine scarce labeled data with abundant unlabeled observations. Semi-supervise
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。
A very simple framework for state-of-the-art Natural Language Processing (NLP)
On neuromorphic chips, spiking neural networks (SNNs) consume far less energy than artificial neural networks
The rapid growth in the size of state-of-the-art machine-learning (ML) models has led to increasing compute de
Weight perturbation (WP) estimates learning directions from random parameter perturbations and a scalar change
Face image quality assessment (FIQA) estimates the utility of a face image for automated face recognition and
Closed-loop neuromodulation requires sleep staging that respects stage-specific minimum dwells: stimulation po
Exploiting temporal information in datasets with high temporal dependencies (such as spoken keyword recognitio
The Continuous Thought Machine (CTM) is a recurrent network that attends to inputs and makes predictions based
Plurality-voting ensembles aggregate the predictions of multiple base classifiers and output the most voted cl
Although spiking neural networks (SNNs) show promise in energy-efficient computing through event-driven comput
Online motor imagery-based brain-computer interfaces (MI-BCIs) typically decode continuous EEG using fixed-len
This survey introduces contract design to computer scientists: using outcome-based payments to incentivize eff
Functional magnetic resonance imaging (fMRI) commonly characterizes large-scale brain organization through fun
Many machine learning approaches are two-level: an expert selects a class of methods, within this class the ch
Small tabular datasets with expert-designed spectral features are the norm in operational Earth observation, a
Biological connectomes encode structured solutions to visual computation that may provide reusable inductive b
Living neuronal networks transform inputs through recurrent cellular and population dynamics, yet it is unknow
Segment-based explanation methods such as LIME return a single explanation for each prediction, computed from
Hybrid quantum classical neural networks integrate parameterized quantum circuits (PQCs) with established deep
Aggregate accuracy cannot reveal which utterances are locally vulnerable or how internal activity changes when
Intrinsic explainability remains a challenging problem, particularly in contexts where multilayer perceptrons
Fast and accurate qubit-state assignment is essential for feedback, calibration, and error correction in quant
Spiking Neural Networks (SNNs) have attracted increasing attention due to their impressive temporal dynamics,
As the input dimension $n$ grows, rule-based machine learning, such as Learning Classifier Systems (LCSs), fac
We propose a new spiking neural network (SNN) design to process static images and event streams using time-to-
Clinical scalp electroencephalography (EEG) offers a noninvasive window into neural dynamics of neuropsychiatr
A critical analysis of contemporary approaches to the study of conscious states. The review focuses on methods