https://github.com/janosh/matbench-discovery
공식 코드Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
Papers with Code 아카이브
Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 11
- 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/janosh/pymatviz
공식 코드Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
https://github.com/jarrycyx/unn
공식 코드CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
https://github.com/jarucezh/cldfd
공식 코드Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification
https://github.com/jarvisyjw/gv-bench
공식 코드https://github.com/jaryP/Cascaded-Scaling-Classifier
공식 코드Class incremental learning with probability dampening and cascaded gated classifier
https://github.com/jaryp/cil-margin-dampening-gated-classifier
공식 코드Class incremental learning with probability dampening and cascaded gated classifier
https://github.com/jasonlsc/gscodec_studio
공식 코드GSCodec Studio: A Modular Framework for Gaussian Splat Compression
https://github.com/jasonwang959/stpmp
공식 코드Spatio-Temporal Branching for Motion Prediction using Motion Increments
https://github.com/java-bench/javabench
공식 코드JavaBench: A Benchmark of Object-Oriented Code Generation for Evaluating Large Language Models
https://github.com/jawadtahir/challenger
공식 코드The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data
https://github.com/jayabrata97/Deep-Attention-Driven-Reinforccement-Learning-DAD-RL
공식 코드https://github.com/jayqine/mgd-ssss
공식 코드Multi-Granularity Distillation Scheme Towards Lightweight Semi-Supervised Semantic Segmentation
https://github.com/jbwang1997/crosskd
공식 코드CrossKD: Cross-Head Knowledge Distillation for Object Detection
https://github.com/jc-202/sa-mlp
공식 코드SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLP
https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
공식 코드Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets
https://github.com/jcruan519/idat
공식 코드https://github.com/jcyk/xamr
공식 코드https://github.com/jeffreywpli/stronger-than-you-think
공식 코드Stronger Than You Think: Benchmarking Weak Supervision on Realistic Tasks
https://github.com/jeffwang987/openoccupancy
공식 코드OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception
https://github.com/jehanyang/emgbench
공식 코드EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography
https://github.com/jejjohnson/oceanbench
공식 코드https://github.com/jekimlab/reffakd
공식 코드ReffAKD: Resource-efficient Autoencoder-based Knowledge Distillation
https://github.com/jemjemzzz/code-ownership
공식 코드https://github.com/jenine-321/genface
공식 코드GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning
https://github.com/jeromeramos70/user-profile-recommendation
공식 코드Transparent and Scrutable Recommendations Using Natural Language User Profiles
https://github.com/jessefarebro/dqn-ale
공식 코드https://github.com/jessicafry/tidmad
공식 코드TIDMAD: Time Series Dataset for Discovering Dark Matter with AI Denoising
https://github.com/jessytan/low-complexity-asc
공식 코드Low-Complexity Acoustic Scene Classification Using Parallel Attention-Convolution Network
https://github.com/jetbrains-research/coqpilot
공식 코드https://github.com/jetrunner/beyond-preserved-accuracy
공식 코드Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression
https://github.com/jeunsom/TGD
공식 코드Leveraging Topological Guidance for Improved Knowledge Distillation
https://github.com/jgh9094/GPTP-2021-Exploration-Of-Exploration
공식 코드https://github.com/jha-lab/cnn_design-space
공식 코드CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework
https://github.com/jha-lab/codebench
공식 코드CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework
https://github.com/jhoelli/xtsc-bench
공식 코드XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.