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Data-free Knowledge Distillation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/gair-nlp/benbench
공식 코드https://github.com/gair-nlp/olympicarena
공식 코드OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI
https://github.com/gaotiexinqu/v2p-bench
공식 코드https://github.com/gatech-eic/longmamba
공식 코드LongMamba: Enhancing Mamba's Long Context Capabilities via Training-Free Receptive Field Enlargement
https://github.com/gauzias/sulcal_graphs_matching
공식 코드Population-wise Labeling of Sulcal Graphs using Multi-graph Matching
https://github.com/gayecolakoglu/layie-llm
공식 코드Problem Solved? Information Extraction Design Space for Layout-Rich Documents using LLMs
https://github.com/gbibbo/voice_anonymization
공식 코드The Sounds of Home: A Speech-Removed Residential Audio Dataset for Sound Event Detection
https://github.com/gbrltv/business_process_encoding
공식 코드https://github.com/gdmnl/spectral-gnn-benchmark
공식 코드Benchmarking Spectral Graph Neural Networks: A Comprehensive Study on Effectiveness and Efficiency
https://github.com/geek-ai/Texygen
공식 코드https://github.com/geflaspohler/poold
공식 코드https://github.com/gemcollector/learning-from-scratch
공식 코드On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
https://github.com/genentech/ringer
공식 코드https://github.com/genezc/structbias
공식 코드https://github.com/geoai-lab/EUPEG
공식 코드https://github.com/geometric-intelligence/TopoBench
공식 코드TopoBench: A Framework for Benchmarking Topological Deep Learning
https://github.com/georg-wolflein/histaug
공식 코드Benchmarking Pathology Feature Extractors for Whole Slide Image Classification
https://github.com/gersteinlab/medagents-benchmark
공식 코드MedAgentsBench: Benchmarking Thinking Models and Agent Frameworks for Complex Medical Reasoning
https://github.com/gestaltcogteam/basicts
공식 코드https://github.com/gfnorg/gfn-diffusion
공식 코드https://github.com/gglab-ku/turkish-plu
공식 코드Benchmarking Procedural Language Understanding for Low-Resource Languages: A Case Study on Turkish
https://github.com/giarcieri/assessing-the-influence-of-models-on-the-performance-of-reinforcement-learning-algorithms
공식 코드https://github.com/gidecarlo/vitagraph
공식 코드VitaGraph: Building a Knowledge Graph for Biologically Relevant Learning Tasks
https://github.com/gideonfr/emprox
공식 코드EmProx: Neural Network Performance Estimation For Neural Architecture Search
https://github.com/giganticode/throwbench
공식 코드ThrowBench: Benchmarking LLMs by Predicting Runtime Exceptions
https://github.com/gilesluo/dtr-bench
공식 코드https://github.com/giovannicatalani/mario
공식 코드Towards scalable surrogate models based on Neural Fields for large scale aerodynamic simulations
https://github.com/giovannimen/cpcad-bench
공식 코드CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods
https://github.com/gistairc/HS-SOD
공식 코드Hyperspectral Image Dataset for Benchmarking on Salient Object Detection
https://github.com/git-disl/EnsembleBench
공식 코드Promoting High Diversity Ensemble Learning with EnsembleBench
https://github.com/git-disl/LRBench
공식 코드Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks
https://github.com/github-usr-ano/temporal_graph_data_pdes
공식 코드Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs
https://github.com/giu-guarino/rho-pnn
공식 코드Zero-Shot Hyperspectral Pansharpening Using Hysteresis-Based Tuning for Spectral Quality Control
https://github.com/gjorgjinac/dynamorep_problem_classification
공식 코드https://github.com/glam-imperial/AudioXgen
공식 코드Audio Explanation Synthesis with Generative Foundation Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.