https://github.com/haiyang-w/tokenformer
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Papers with Code 아카이브
Computational Efficiency
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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/hannibal046/nanorwkv
https://github.com/haoheliu/audioldm-training-finetuning
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
https://github.com/haoheliu/audioldm_eval
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
https://github.com/harry24k/aepw-pytorch
https://github.com/havenhq/mamba-chat
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/henry1985/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/hieu28022000/PaddleOCR
https://github.com/hisfog/SfMNeXt-Impl
SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation
https://github.com/holarissun/rewardmodelingbeyondbradleyterry
Reviving The Classics: Active Reward Modeling in Large Language Model Alignment
https://github.com/hongzhenwang/RRPN-revise
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
https://github.com/hpc203/YOLObile-opencv-dnn
YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
https://github.com/huanzhang12/CROWN-IBP
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/huanzhang12/CertifiedReLURobustness
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/huanzhang12/RecurJac
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/huanzhang12/RecurJac-Jacobian-Bounds
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/huanzhang12/RecurJac-and-CROWN
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/huanzhang12/alpha-beta-CROWN
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/huanzhang12/sa_ppo
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/huchuanlu/15_7
Inner and Inter Label Propagation: Salient Object Detection in the Wild
https://github.com/huggingface/diffusers
AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
https://github.com/hunto/localmamba
https://github.com/hustvl/vim
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/huyphan168/PEER
https://github.com/hwzwx1190522/inceptionv3
https://github.com/hyeon95y/sparselinear
How Can We Be So Dense? The Benefits of Using Highly Sparse Representations
https://github.com/hyunjin72/GraphGAN-PyTorch
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
https://github.com/ibm/fungcn
https://github.com/idiap/gile
Multilingual Hierarchical Attention Networks for Document Classification
https://github.com/imagesegmentation2020/indoor
https://github.com/in1t3r/ARS
Simple random search provides a competitive approach to reinforcement learning
https://github.com/intel/neural-compressor
https://github.com/ipsitmantri/DiTASK
https://github.com/irmvlab/point-mamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/isaacschaal/SG_training
https://github.com/isosnovik/sesn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.