https://github.com/lili-chen/SEER
공식 코드Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
Papers with Code 아카이브
Computational Efficiency
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 1
Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lim0606/pytorch-generative-multisensory-network
공식 코드https://github.com/limaoum/fastlexrank
공식 코드FastLexRank: Efficient Lexical Ranking for Structuring Social Media Posts
https://github.com/limengran98/arb
공식 코드https://github.com/lindermanlab/S5
공식 코드https://github.com/line/sacpo
공식 코드Stepwise Alignment for Constrained Language Model Policy Optimization
https://github.com/linfanz/nac-test
공식 코드https://github.com/linyizun2024/ASMCVaR
공식 코드https://github.com/lipingyi/fedssa
공식 코드https://github.com/lirongwu/mape-ppi
공식 코드https://github.com/lirongx/SubNN
공식 코드https://github.com/lironui/Multistage-Attention-ResU-Net
공식 코드Multi-stage Attention ResU-Net for Semantic Segmentation of Fine-Resolution Remote Sensing Images
https://github.com/list17/gridformer
공식 코드GridFormer: Point-Grid Transformer for Surface Reconstruction
https://github.com/litianliu/fdbd-ood
공식 코드https://github.com/liufeng2317/DispFormer
공식 코드https://github.com/liuqh16/mazero
공식 코드https://github.com/liushangnoname/maximum-optimality-margin
공식 코드https://github.com/liuzelei13/gtg-shapley
공식 코드GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning
https://github.com/ljc-fvnr/structural-aligned-mixture-of-var
공식 코드https://github.com/ljwztc/clip-driven-universal-model
공식 코드https://github.com/ljxw88/deephgcn
공식 코드DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional Networks
https://github.com/ljzycmd/scd
공식 코드Consistent Human Image and Video Generation with Spatially Conditioned Diffusion
https://github.com/llmkvsys/rethink-kv-compression
공식 코드Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving
https://github.com/locuslab/ect
공식 코드https://github.com/longkangli/pfss-il
공식 코드Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning
https://github.com/longvideohaystack/tstar
공식 코드Re-thinking Temporal Search for Long-Form Video Understanding
https://github.com/lorenzrichter/PDE-backward-solver
공식 코드Solving high-dimensional parabolic PDEs using the tensor train format
https://github.com/lorifranke/SlicerTMS
공식 코드SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health Treatment
https://github.com/louisfaury/logistic_bandit
공식 코드Jointly Efficient and Optimal Algorithms for Logistic Bandits
https://github.com/lpj-github-io/MWCNNv2
공식 코드https://github.com/lpj0/MWCNN
공식 코드https://github.com/lronkitty/ssumamba
공식 코드SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising
https://github.com/ltdung/snt-sentry
공식 코드Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models
https://github.com/lu-group/mixed-precision-sciml
공식 코드Speeding up and reducing memory usage for scientific machine learning via mixed precision
https://github.com/luca-parisi/m-arcsinh_scikit-learn_TensorFlow_Keras/blob/master/_fastica.py
공식 코드https://github.com/lucapton/ICLabel-Train
공식 코드ICLabel: An automated electroencephalographic independent component classifier, dataset, and website
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.