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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/valeoai/MFEval
공식 코드Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?
https://github.com/valuebyte-ai/awesome-llm-psychometrics
공식 코드Large Language Model Psychometrics: A Systematic Review of Evaluation, Validation, and Enhancement
https://github.com/valvoda/neuraltransducer
공식 코드https://github.com/vanderschaarlab/invconban
공식 코드Inverse Contextual Bandits: Learning How Behavior Evolves over Time
https://github.com/vanderschaarlab/temporai
공식 코드TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine
https://github.com/vatass/adaptiveshrinkagedkgp
공식 코드Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories
https://github.com/vcg-uvic/image-matching-benchmark
공식 코드Image Matching across Wide Baselines: From Paper to Practice
https://github.com/vchitect/vbench
공식 코드VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models
https://github.com/vectara/FaithJudge
공식 코드Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards
https://github.com/vectorinstitute/humanibench
공식 코드HumaniBench: A Human-Centric Framework for Large Multimodal Models Evaluation
https://github.com/vertaix/llm4mat-bench
공식 코드LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction
https://github.com/victorzxy/graphac
공식 코드Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
https://github.com/viczong/extract_covid19_events_from_twitter
공식 코드COVID-19 event extraction from Twitter via extractive question answering with continuous prompts
https://github.com/video-db/ocr-benchmark
공식 코드Benchmarking Vision-Language Models on Optical Character Recognition in Dynamic Video Environments
https://github.com/vijaydwivedi75/lrgb
공식 코드https://github.com/vimal-isi-edu/BioFors
공식 코드https://github.com/vinairesearch/tise-toolbox
공식 코드https://github.com/vinayakkapoor/vision_based_grasping_benchmarking
공식 코드A Benchmarking Study of Vision-based Robotic Grasping Algorithms
https://github.com/vincentdengp/3d-lr
공식 코드Can 3D Vision-Language Models Truly Understand Natural Language?
https://github.com/vio1etus/flpoison
공식 코드SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning
https://github.com/vis-sustech/visual-analytics-for-emo-algorithm-comparison
공식 코드https://github.com/visinf/idsds
공식 코드https://github.com/visipedia/newt
공식 코드Benchmarking Representation Learning for Natural World Image Collections
https://github.com/visteam-isr-uc/ylfw
공식 코드Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition
https://github.com/vita-epfl/jrdb-traj
공식 코드JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds
https://github.com/vita-group/large_scale_gcn_benchmarking
공식 코드A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
https://github.com/vita-group/ssharmonization
공식 코드https://github.com/vitae-transformer/aptv2
공식 코드APTv2: Benchmarking Animal Pose Estimation and Tracking with a Large-scale Dataset and Beyond
https://github.com/vlar-group/unsupobjseg
공식 코드Benchmarking and Analysis of Unsupervised Object Segmentation from Real-world Single Images
https://github.com/vorausrobotik/voraus-ad-dataset
공식 코드The voraus-AD Dataset for Anomaly Detection in Robot Applications
https://github.com/vr18ub/court_view_generation
공식 코드One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support
https://github.com/vslaykovsky/ai-sourcing-benchmark
공식 코드Evaluating AI Recruitment Sourcing Tools by Human Preference
https://github.com/vut-hfut/micro-action
공식 코드Benchmarking Micro-action Recognition: Dataset, Methods, and Applications
https://github.com/vwxyzjn/cleanrl
공식 코드CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
https://github.com/wabyking/qnn
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.