https://github.com/iversonicter/Learn-to-pay-attention
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
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/izhangrui/paper_to_read
PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
https://github.com/jack-willturner/batchnorm-pruning
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers
https://github.com/jacquelinelala/GFN
Gated Fusion Network for Joint Image Deblurring and Super-Resolution
https://github.com/jainpankul/Augmented-Random-Search
Simple random search provides a competitive approach to reinforcement learning
https://github.com/james77777778/keras-image-models
https://github.com/jaychoi12/LG_KD
https://github.com/jeroaranda/thesis
https://github.com/jeromerony/adversarial-library
https://github.com/jerryji1993/dnabert
DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome
https://github.com/jianhao2016/online_cvxMF
Online Convex Matrix Factorization with Representative Regions
https://github.com/jimmylihui/OpenGenome
DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome
https://github.com/jimmylihui/genbench
DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome
https://github.com/jindongjiang/slotssms
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/jindongwang/transferlearning
Easy Transfer Learning By Exploiting Intra-domain Structures
https://github.com/jl-er/rwkv-peft
Balancing LoRA Performance and Efficiency with Simple Shard Sharing
https://github.com/jlyang1990/loggle
https://github.com/jlyang1990/loggle_test
https://github.com/jmtomczak/vae_householder_flow
https://github.com/jmtomczak/vae_vpflows
https://github.com/john-hewitt/conditional-probing
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
https://github.com/johncwok/IntegerIB.jl
https://github.com/johnma2006/mamba-minimal
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/jooyae/NVIDIA_STYLEGAN
https://github.com/jplumail/learning-image-restoration
On learning optimized reaction diffusion processes for effective image restoration
https://github.com/jsie7/ssm-benchmark
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/jsuarez5341/Efficient-Dynamic-Batching
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
https://github.com/julians89/PointNetLK-Tensorflow
PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
https://github.com/justld/CCNet_paddle
https://github.com/kaamka/cars-classification-deep-learning
https://github.com/kaichen-z/rad
Manydepth2: Motion-Aware Self-Supervised Multi-Frame Monocular Depth Estimation in Dynamic Scenes
https://github.com/kalifou/robotics-rl-srl
Simple random search provides a competitive approach to reinforcement learning
https://github.com/kangks/aws-greengrass-mxnet-inception
https://github.com/kanuore/RRPN
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
https://github.com/kaviezhang/meshmamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/kayuksel/pytorch-ars
Simple random search provides a competitive approach to reinforcement learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.