https://github.com/picda/picbench
공식 코드PICBench: Benchmarking LLMs for Photonic Integrated Circuits Design
Papers with Code 아카이브
Knowledge Distillation
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- 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/pierlj/aaf_framework
공식 코드A Comparative Attention Framework for Better Few-Shot Object Detection on Aerial Images
https://github.com/pierreadorni/capabilities-encoding
공식 코드https://github.com/pierrecolombo/rankingnlpsystems
공식 코드What are the best systems? New perspectives on NLP Benchmarking
https://github.com/pimed/trusglandsegmentation
공식 코드https://github.com/pixwse/tiny2
공식 코드Tiny models from tiny data: Textual and null-text inversion for few-shot distillation
https://github.com/pizzareapers/fedsdaf
공식 코드FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain Generalization
https://github.com/pjlallen/surgical-image-restoration
공식 코드Benchmarking Laparoscopic Surgical Image Restoration and Beyond
https://github.com/pku-baichuan-mlsystemlab/fb-bench
공식 코드FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs' Responsiveness to Human Feedback
https://github.com/pku-dair/hetu-galvatron
공식 코드Galvatron: An Automatic Distributed System for Efficient Foundation Model Training
https://github.com/pku-liang/origen
공식 코드OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection
https://github.com/pku-sec-lab/mpcvit
공식 코드https://github.com/pkulcwmzx/knowledge-boundary
공식 코드https://github.com/plan-lab/mtsbench
공식 코드mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
https://github.com/plantnet/malpolon
공식 코드MALPOLON: A Framework for Deep Species Distribution Modeling
https://github.com/plrbear/auxskd
공식 코드https://github.com/plumdeq/neuro-kglink
공식 코드https://github.com/pluslabnlp/geneva
공식 코드https://github.com/pmc-patients/pmc-patients
공식 코드https://github.com/pmernyei/wiki-cs-dataset
공식 코드Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks
https://github.com/pmoriano/benchmarking-unsupervised-online-IDS-masquerade-attacks
공식 코드Benchmarking Unsupervised Online IDS for Masquerade Attacks in CAN
https://github.com/pokerwf/KnowLogic
공식 코드SCoRE: Benchmarking Long-Chain Reasoning in Commonsense Scenarios
https://github.com/polixir/neorl2
공식 코드https://github.com/polowitty/covdocker
공식 코드CovDocker: Benchmarking Covalent Drug Design with Tasks, Datasets, and Solutions
https://github.com/polyagent/from-bytes-to-borsch
공식 코드From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language Representation
https://github.com/poopit/dcd-official
공식 코드Discriminator-Cooperated Feature Map Distillation for GAN Compression
https://github.com/power-operation/ckdformer
공식 코드PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power Station
https://github.com/pppa2019/contexualdynamicmapping
공식 코드Enhancing Cross-Tokenizer Knowledge Distillation with Contextual Dynamical Mapping
https://github.com/prafulla77/discourse_tdg_aacl2022
공식 코드Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs
https://github.com/pragmaticslab/vote_and_rank
공식 코드Vote'n'Rank: Revision of Benchmarking with Social Choice Theory
https://github.com/prakashchhipa/robsutness-evaluation-of-self-supervised-methods-distribution-shifts-and-corruptions
공식 코드Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?
https://github.com/pralab/imagenet-patch
공식 코드ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
https://github.com/pranav-chib/trajimpute
공식 코드Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking
https://github.com/pranavmaneriker/graphconformal-code
공식 코드Conformal Prediction: A Theoretical Note and Benchmarking Transductive Node Classification in Graphs
https://github.com/prannaykaul/lvc
공식 코드Label, Verify, Correct: A Simple Few Shot Object Detection Method
https://github.com/prantik-pdeb/best-les
공식 코드BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.