https://github.com/tommyip/mamba2-minimal
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/towhee-io/towhee
MoViNets: Mobile Video Networks for Efficient Video Recognition
https://github.com/trichtu/ConvLSTM-RAU-net
https://github.com/trichtu/Recurrent_Attention_U_net
https://github.com/tsetimmy/significance_tests_for_neural_networks
https://github.com/tugrulhkarabulut/K-Means-Clustering
A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm
https://github.com/tutelalabs/tutela-app
https://github.com/typoverflow/decision-rwkv
https://github.com/uclaml/Frank-Wolfe-AdvML
https://github.com/ultralytics/ultralytics
https://github.com/unconst/MACH
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
https://github.com/urw7rs/torch_skeleton
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
https://github.com/uzh-rpg/ssms_event_cameras
https://github.com/vankhoa21991/medicalImgSEg
https://github.com/verified-intelligence/alpha-beta-crown
Efficient Neural Network Robustness Certification with General Activation Functions
https://github.com/videonetworks/laps-transformer
https://github.com/vinits5/PointNetLK
PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
https://github.com/vinits5/learning3d
PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
https://github.com/vladislavzh/cotic
https://github.com/vonguyenleduy/parametric_selective_inference_changepoint
Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming
https://github.com/vonguyenleduy/parametric_si_optimal_cp_dp
Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming
https://github.com/vvvm23/mamba-jax
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/wandahangfy/yolov11-rgbt
YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
https://github.com/wangyongjie-ntu/Learn-to-pay-attention
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
https://github.com/waynezhanghk/gacluster
Graph Degree Linkage: Agglomerative Clustering on a Directed Graph
https://github.com/waynezhanghk/gactoolbox
Graph Degree Linkage: Agglomerative Clustering on a Directed Graph
https://github.com/webis-de/small-text
Similarity Search for Efficient Active Learning and Search of Rare Concepts
https://github.com/weiwang31/icemamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/weiyadi/dlm_sgp
Direct loss minimization algorithms for sparse Gaussian processes
https://github.com/wenj/GoMAvatar
GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-Mesh
https://github.com/whisperspeech/whisperspeech
https://github.com/wireless911/span-aste
Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction
https://github.com/wjcheon/MedicalImageSegmentation_Pytorch
https://github.com/wonderren/public_cppmomapf
Subdimensional Expansion for Multi-objective Multi-agent Path Finding
https://github.com/wonderren/public_pymomapf
Subdimensional Expansion for Multi-objective Multi-agent Path Finding
https://github.com/wwrechard/weierstrass
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.