https://github.com/unique1i/scenesplat
공식 코드SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretraining
Papers with Code 아카이브
Data-free Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/unites-lab/fmts
공식 코드https://github.com/unites-lab/moe-quantization
공식 코드Examining Post-Training Quantization for Mixture-of-Experts: A Benchmark
https://github.com/uoe-agents/epymarl
공식 코드Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
https://github.com/uoe-agents/lb-foraging
공식 코드Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
https://github.com/uoe-agents/revisiting-maddpg
공식 코드https://github.com/uoe-agents/robotic-warehouse
공식 코드Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
https://github.com/uqhwen2/FCCM
공식 코드Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective
https://github.com/urbslab/streamline
공식 코드https://github.com/usail-hkust/bag_of_tricks_for_llm_jailbreaking
공식 코드https://github.com/usail-hkust/jailtrickbench
공식 코드https://github.com/usc-fortis/ad-llm
공식 코드AD-LLM: Benchmarking Large Language Models for Anomaly Detection
https://github.com/uscnlp-lime/cq-bench
공식 코드https://github.com/usmaann/BioALBERT
공식 코드Benchmarking for Biomedical Natural Language Processing Tasks with a Domain Specific ALBERT
https://github.com/ustcagi/pruningrag
공식 코드Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
https://github.com/ustcagi/rag-x
공식 코드Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
https://github.com/utcs-scea/Altis
공식 코드https://github.com/utel-uiuc/gtfs_llm
공식 코드ChatGPT for GTFS: Benchmarking LLMs on GTFS Understanding and Retrieval
https://github.com/utiasDSL/gym-pybullet-drones
공식 코드https://github.com/utkuozbulak/imagenet-adversarial-image-evaluation
공식 코드Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks
https://github.com/uwsbel/simbench
공식 코드https://github.com/valence-labs/OpenQDC
공식 코드https://github.com/valence-labs/Tx-Evaluation
공식 코드https://github.com/valence-labs/TxPert
공식 코드https://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/vcl-iisc/data-free-defense-at-test-time
공식 코드https://github.com/vcl-iisc/improved-data-free-test-time-adversarial-defense
공식 코드https://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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.