https://github.com/twinkle0331/lgtm
공식 코드Tailoring Instructions to Student's Learning Levels Boosts Knowledge Distillation
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/twinkle0331/xcompression
공식 코드Exploring Extreme Parameter Compression for Pre-trained Language Models
https://github.com/twni2016/Meta-SAC
공식 코드Meta-SAC: Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient
https://github.com/tyang816/venusfactory
공식 코드https://github.com/tyq1024/rlx2
공식 코드RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
https://github.com/uark-aicv/cattleface-rgbt-benchmark
공식 코드https://github.com/uark-aicv/ecg_ssl_12lead
공식 코드Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning
https://github.com/ubc-nlp/udistilwhisper
공식 코드uDistil-Whisper: Label-Free Data Filtering for Knowledge Distillation in Low-Data Regimes
https://github.com/ubcdingxin/soap-kd
공식 코드https://github.com/ubicomplab/rppg-toolbox
공식 코드https://github.com/ubioinformat/graphemb
공식 코드Towards a more inductive world for drug repurposing approaches
https://github.com/ubiquitouslearning/mobiledlframeworksbenchmark
공식 코드https://github.com/ubiquitouslearning/slm_survey
공식 코드https://github.com/ucd-bdlab/icebench
공식 코드IceBench: A Benchmark for Deep Learning based Sea Ice Type Classification
https://github.com/ucdvision/simreg
공식 코드SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation
https://github.com/uci-soe/fairytaleqadata
공식 코드https://github.com/uclanlp/biases-llm-reference-letters
공식 코드"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters
https://github.com/uclanlp/persona-biases
공식 코드Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems
https://github.com/uclouvain-cbio/2022-scp-data-analysis
공식 코드https://github.com/ucsc-real/cifar-10-100n
공식 코드Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
https://github.com/ucsc-vlaa/dmae
공식 코드https://github.com/ufal/olimpic-icdar24
공식 코드Practical End-to-End Optical Music Recognition for Pianoform Music
https://github.com/uhhds/whattheweight
공식 코드What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition
https://github.com/uhhhci/immersive-ngp
공식 코드https://github.com/uicdice/imm-logistic-regression
공식 코드Induced Model Matching: How Restricted Models Can Help Larger Ones
https://github.com/uiuc-focal-lab/quacer-b
공식 코드https://github.com/uiuc-kang-lab/injecagent
공식 코드InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents
https://github.com/ukplab/argument-topic-diversity
공식 코드https://github.com/ukplab/arxiv2024-leaderboard-generation
공식 코드https://github.com/ul-fmf/mlfmf-data
공식 코드MLFMF: Data Sets for Machine Learning for Mathematical Formalization
https://github.com/uliege-driving/dade
공식 코드Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments
https://github.com/uliege-driving/msc-tta
공식 코드Multi-Stream Cellular Test-Time Adaptation of Real-Time Models Evolving in Dynamic Environments
https://github.com/umar-hashmi/FlexibilityAndStorageModel
공식 코드https://github.com/umautobots/nsavp_tools
공식 코드Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception
https://github.com/umbertocappellazzo/CL_SLU
공식 코드https://github.com/umd-huang-lab/waves
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.