https://github.com/unique1i/scenesplat
공식 코드SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretraining
Papers with Code 아카이브
Knowledge Distillation
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Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/unites-lab/doge
공식 코드DOGe: Defensive Output Generation for LLM Protection Against Knowledge Distillation
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/usail-hkust/rdat
공식 코드Robust Spatiotemporal Traffic Forecasting with Reinforced Dynamic Adversarial Training
https://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/utkarshnath/Adjoint-Network
공식 코드Adjoined Networks: A Training Paradigm with Applications to Network Compression
https://github.com/utkuozbulak/imagenet-adversarial-image-evaluation
공식 코드Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attacks
https://github.com/uvavision/tv-gzsl
공식 코드On the Transferability of Visual Features in Generalized Zero-Shot Learning
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/vangorade/rethinkingkd_isbi24
공식 코드https://github.com/varungumma/fairseq
공식 코드https://github.com/vatass/adaptiveshrinkagedkgp
공식 코드Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.