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Data-free Knowledge Distillation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/andyisokay/delta-influence
공식 코드https://github.com/andyreagan/sentiment-analysis-comparison
공식 코드https://github.com/aniket004/bri3l
공식 코드https://github.com/aniketrege/cure-bench
공식 코드CuRe: Cultural Gaps in the Long Tail of Text-to-Image Systems
https://github.com/anira-project/anira
공식 코드ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications
https://github.com/anjany/verse
공식 코드https://github.com/ankilab/spoken-100
공식 코드https://github.com/anmolchaure/climate_emulators
공식 코드Finding the Perfect Fit: Applying Regression Models to ClimateBench v1.0
https://github.com/anon-user-for-web/badlad
공식 코드BaDLAD: A Large Multi-Domain Bengali Document Layout Analysis Dataset
https://github.com/anonno2/crosstalk-generation
공식 코드Can Language Models Make Fun? A Case Study in Chinese Comical Crosstalk
https://github.com/ansebi/wain22_sim_env
공식 코드Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets
https://github.com/ansfl/MEMS-IMU-Denoising
공식 코드https://github.com/ant-research/easytemporalpointprocess
공식 코드https://github.com/antoalli/3d_os
공식 코드https://github.com/anton-bushuiev/ppiref
공식 코드https://github.com/anugyas/ood-on-nae
공식 코드https://github.com/anupamaray/ensembleqml_application
공식 코드https://github.com/anwar-said/neurograph
공식 코드NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
https://github.com/aobol/kamnet
공식 코드KamNet: An Integrated Spatiotemporal Deep Neural Network for Rare Event Search in KamLAND-Zen
https://github.com/aorogat/CBench
공식 코드CBench: Towards Better Evaluation of Question Answering Over Knowledge Graphs
https://github.com/aparna-aketi/cross_feature_contrastive_loss
공식 코드Cross-feature Contrastive Loss for Decentralized Deep Learning on Heterogeneous Data
https://github.com/aperezlebel/benchmark_mv_approaches
공식 코드Benchmarking missing-values approaches for predictive models on health databases
https://github.com/apliko-xyz/pytdc
공식 코드https://github.com/appfl/appfl
공식 코드Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
https://github.com/apple/ml-recurrent-drafter
공식 코드Recurrent Drafter for Fast Speculative Decoding in Large Language Models
https://github.com/apple/ml-synthbias
공식 코드Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs
https://github.com/apple2373/wbc-benchmark
공식 코드Benchmarking White Blood Cell Classification Under Domain Shift
https://github.com/applied-ai-research-lab/orchestrator-agent-trust
공식 코드https://github.com/applied-machine-learning-lab/erase
공식 코드ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems
https://github.com/aprbw/ood_electricity_forecasting_during_covid-19
공식 코드https://github.com/archettialberto/federated_survival_datasets
공식 코드Heterogeneous Datasets for Federated Survival Analysis Simulation
https://github.com/archiel19/frax4
공식 코드https://github.com/architsharma97/earl_benchmark
공식 코드Autonomous Reinforcement Learning: Formalism and Benchmarking
https://github.com/arena-rosnav/arena-tools
공식 코드https://github.com/areuther/ai-accelerators
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.