https://github.com/atomicarchitects/priceoffreedom
공식 코드The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products
Papers with Code 아카이브
Knowledge Distillation
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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/atrewin/signxmda
공식 코드Cross-modality Data Augmentation for End-to-End Sign Language Translation
https://github.com/atrey-a/t3aas-v1-benchmarking
공식 코드https://github.com/audio-westlakeu/audiossl
공식 코드Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks
https://github.com/audiollms/audiobench
공식 코드IFEval-Audio: Benchmarking Instruction-Following Capability in Audio-based Large Language Models
https://github.com/automl/DACBench
공식 코드DACBench: A Benchmark Library for Dynamic Algorithm Configuration
https://github.com/automl/arlbench
공식 코드https://github.com/automl/lmem-significance
공식 코드https://github.com/automl/nas_benchmarks
공식 코드NAS-Bench-101: Towards Reproducible Neural Architecture Search
https://github.com/automl/nasbench-1shot1
공식 코드NAS-Bench-1Shot1: Benchmarking and Dissecting One-shot Neural Architecture Search
https://github.com/automl/sawei
공식 코드Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
https://github.com/autonlab/aqua
공식 코드https://github.com/autonomousvision/carla_garage
공식 코드Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving
https://github.com/autonomousvision/navsim
공식 코드NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking
https://github.com/avouros/Code-KMeans-benchmark
공식 코드https://github.com/avramandrei/rolargesum
공식 코드https://github.com/avysogorets/effective-sparsity
공식 코드Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity
https://github.com/awaisrauf/awesome-cv-foundational-models
공식 코드Foundational Models Defining a New Era in Vision: A Survey and Outlook
https://github.com/awindmann/cps-robustness-benchmark
공식 코드https://github.com/awslabs/autogluon
공식 코드Benchmarking Multimodal AutoML for Tabular Data with Text Fields
https://github.com/awslabs/aws-cv-unique-information
공식 코드On information captured by neural networks: connections with memorization and generalization
https://github.com/awslabs/dgl-lifesci
공식 코드https://github.com/awslabs/fortuna
공식 코드Fortuna: A Library for Uncertainty Quantification in Deep Learning
https://github.com/awslabs/multi-table-benchmark
공식 코드4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs
https://github.com/ayaabdelsalam91/TS-Interpretability-Benchmark
공식 코드Benchmarking Deep Learning Interpretability in Time Series Predictions
https://github.com/ayanban011/graphkd
공식 코드https://github.com/ayeffkay/rubert-tiny
공식 코드Knowledge Distillation of Russian Language Models with Reduction of Vocabulary
https://github.com/aymanbegh/benchmarking-performance
공식 코드Benchmarking performance of object detection under image distortions in an uncontrolled environment
https://github.com/ayush-00/mimicgait
공식 코드https://github.com/bai-lab/kd-debias
공식 코드Invariant debiasing learning for recommendation via biased imputation
https://github.com/baobao0926/awesome-mamba-in-remote-sensing
공식 코드Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook
https://github.com/basarkutukcu/JExplore
공식 코드JExplore: Design Space Exploration Tool for Nvidia Jetson Boards
https://github.com/basiralab/BrainNet-ML-ToolBox
공식 코드https://github.com/basiralab/L2S-KDnet
공식 코드Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning
https://github.com/basiralab/falcon
공식 코드FALCON: Feature-Label Constrained Graph Net Collapse for Memory Efficient GNNs
https://github.com/basvanstein/ioh-explainer
공식 코드Explainable Benchmarking for Iterative Optimization Heuristics
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.