https://github.com/lfrison/i4b
공식 코드Constrained Reinforcement Learning for Safe Heat Pump Control
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/lgcnsai/ps-kd-pytorch
공식 코드Self-Knowledge Distillation with Progressive Refinement of Targets
https://github.com/lhoangan/multas
공식 코드https://github.com/li-fangyu/sequential_rec
공식 코드Improving Sequential Recommendation Models with an Enhanced Loss Function
https://github.com/li-xirong/ude
공식 코드https://github.com/liamheng/annotation-free-medical-image-enhancement
공식 코드https://github.com/liamjdavis/sudoku-smt-solvers
공식 코드Evaluating SAT and SMT Solvers on Large-Scale Sudoku Puzzles
https://github.com/liamlian0727/uiis10k
공식 코드https://github.com/liaopeiyuan/artbench
공식 코드The ArtBench Dataset: Benchmarking Generative Models with Artworks
https://github.com/liaorongfan/deeppersonality
공식 코드https://github.com/liar-zzy/benchmarking-ultra-high-definition-single-image-reflection-removal
공식 코드https://github.com/libeineu/GPKD
공식 코드Learning Light-Weight Translation Models from Deep Transformer
https://github.com/lidiagarrucho/mama-mia
공식 코드A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
https://github.com/lifelong-ml/offline-compositional-rl-datasets
공식 코드Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning
https://github.com/lifelonglab/m2i_i2m_benchmark
공식 코드From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning
https://github.com/lightning-uq-box/lightning-uq-box
공식 코드Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning
https://github.com/lijincm/CoCa
공식 코드https://github.com/likaixin2000/mmcode
공식 코드https://github.com/likyoo/open-cd
공식 코드https://github.com/liminxian/DukeMTMC-SI-Tracklet
공식 코드https://github.com/limu-research/2025-sac-rnn-attention-kd
공식 코드Knowledge Distillation in RNN-Attention Models for Early Prediction of Student Performance
https://github.com/lin-rany/codeIF
공식 코드https://github.com/lina-usc/jansen-rit-model-benchmarking-deep-learning
공식 코드Deep Jansen-Rit Parameter Inference for Model-Driven Analysis of Brain Activity
https://github.com/linchen99/fedned
공식 코드Federated Learning with Extremely Noisy Clients via Negative Distillation
https://github.com/lingjuanlv/FPPDL
공식 코드https://github.com/lingjzhu/spoken_sent_embedding
공식 코드Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings
https://github.com/linglongqian/csai
공식 코드Knowledge Enhanced Conditional Imputation for Healthcare Time-series
https://github.com/lingxitong/CAMELYON-PLUS-BENCHMARK
공식 코드Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections
https://github.com/linhaowei1/clog
공식 코드CLoG: Benchmarking Continual Learning of Image Generation Models
https://github.com/linhaowei1/kumo
공식 코드Generative Evaluation of Complex Reasoning in Large Language Models
https://github.com/linrongc/solution_youtube8m_v3
공식 코드https://github.com/linxins97/nlpbench
공식 코드NLPBench: Evaluating Large Language Models on Solving NLP Problems
https://github.com/linzhiqiu/t2v_metrics
공식 코드GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation
https://github.com/liqinye/sidyp
공식 코드Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement
https://github.com/lirongwu/hgmd
공식 코드https://github.com/lirongwu/tgs
공식 코드A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.