https://github.com/martinjurkovic/syntherela
공식 코드Benchmarking the Fidelity and Utility of Synthetic Relational Data
Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
- 90
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/martinmenten/skeletonization-for-gradient-based-optimization
공식 코드https://github.com/martinzaefferer/COBBS
공식 코드https://github.com/martinzaefferer/zaef20b
공식 코드https://github.com/marvinlvn/babyslm
공식 코드BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models
https://github.com/mary-paterson/laryngealcancerclassificationbenchmark
공식 코드https://github.com/maryammaryam877/genai_tigs
공식 코드Benchmarking Generative AI Models for Deep Learning Test Input Generation
https://github.com/masayakawamura/mb-istft-vits
공식 코드https://github.com/mastervito/sws
공식 코드SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning
https://github.com/mateoespinosa/concept-quality
공식 코드Towards Robust Metrics for Concept Representation Evaluation
https://github.com/matt-baugh/nnood
공식 코드nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods
https://github.com/mattbierbaum/arxiv-public-datasets
공식 코드https://github.com/mattdl/continualevaluation
공식 코드Continual evaluation for lifelong learning: Identifying the stability gap
https://github.com/matteospanio/torchfx
공식 코드TorchFX: A modern approach to Audio DSP with PyTorch and GPU acceleration
https://github.com/matthewcym/sflm
공식 코드Revisiting Self-Training for Few-Shot Learning of Language Model
https://github.com/matthewcym/voicebench
공식 코드https://github.com/matthias-wright/art-fid
공식 코드https://github.com/mattsheng/PAN_SR
공식 코드Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$
https://github.com/matus-pikuliak/genderbench
공식 코드https://github.com/maudzung/SFA3D
공식 코드https://github.com/maxiaum/kdia
공식 코드https://github.com/maximilianb2/pc-gym
공식 코드https://github.com/maxium0526/category-wise-fine-tuning
공식 코드https://github.com/mazpie/genrl
공식 코드GenRL: Multimodal-foundation world models for generalization in embodied agents
https://github.com/mazqtpopx/cranfield-navigation-gym
공식 코드Benchmarking Deep Reinforcement Learning for Navigation in Denied Sensor Environments
https://github.com/mberr/ea-sota-comparison
공식 코드A Critical Assessment of State-of-the-Art in Entity Alignment
https://github.com/mboudiaf/TIM
공식 코드https://github.com/mboudiaf/pytorch-meta-dataset
공식 코드https://github.com/mbzuai-nlp/urdufactcheck
공식 코드UrduFactCheck: An Agentic Fact-Checking Framework for Urdu with Evidence Boosting and Benchmarking
https://github.com/mbzuai-oryx/video-llava
공식 코드https://github.com/mc-eiu/mc-eiu
공식 코드Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking Dataset
https://github.com/mcao92/mobilesci
공식 코드Towards Real-time Video Compressive Sensing on Mobile Devices
https://github.com/mccree177/mpcformer
공식 코드MPCFormer: fast, performant and private Transformer inference with MPC
https://github.com/mcg-nju/sorce
공식 코드https://github.com/mcg-nju/structured-sparse-rcnn
공식 코드https://github.com/mcleish7/clrs4lm
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.