https://github.com/kentaroy47/DatasetCulling
Training Domain Specific Models for Energy-Efficient Object Detection
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/khfs/DuplexMamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/khhaliil/avatario
https://github.com/khurram702/StyleBasedGAN
https://github.com/kn-joshua/Modality-Balanced-Multispectral-Pedestrian-Detection
Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems
https://github.com/knowlab/bi-weekly-paper-presentation
TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones
https://github.com/ktaebum/AttentionedDeepPaint
https://github.com/kushagragpt99/MCMC
Bayesian inference for logistic models using Polya-Gamma latent variables
https://github.com/lab-cosmo/pet-mad
The dark side of the forces: assessing non-conservative force models for atomistic machine learning
https://github.com/lab-emi/cleanumamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/lbareiro/Image_Segmentation-master
https://github.com/lbechberger/LearningPsychologicalSpaces
https://github.com/lemonxiaohei/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/lennijusten/GradCam-InceptionV3
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/davit
https://github.com/levanpon98/inception_v3
https://github.com/lim0606/multisensory-embodied-3D-scene-environment
https://github.com/linkedin/fasttreeshap
Fast TreeSHAP: Accelerating SHAP Value Computation for Trees
https://github.com/liu-tianchi/nes2net
Attentive Merging of Hidden Embeddings from Pre-trained Speech Model for Anti-spoofing Detection
https://github.com/liutongkun/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/liutongyang/GraphGAN-pytorch
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
https://github.com/liuxinkai94/Graph-embedding
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
https://github.com/liuzhening111/bisic
Checkerboard Context Model for Efficient Learned Image Compression
https://github.com/lixiaoyu2000/fastpoly
RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework
https://github.com/lizzylizzylizzy/evolvingattention
A Transformer-based Framework for Multivariate Time Series Representation Learning
https://github.com/lmd0311/pointmamba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/loevlie/DL_Project_PSMNet
https://github.com/longshaocong/dgmamba
https://github.com/lorenzomammana/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/luca-parisi/m-arcsinh_scikit-learn_TensorFlow_Keras
https://github.com/luca-parisi/m_arcsinh_scikit_learn
https://github.com/lucidrains/agent-attention-pytorch
Agent Attention: On the Integration of Softmax and Linear Attention
https://github.com/lucidrains/iTransformer
xVal: A Continuous Numerical Tokenization for Scientific Language Models
https://github.com/lukasadam/piVAE
$π$VAE: a stochastic process prior for Bayesian deep learning with MCMC
https://github.com/lukoucky/image_recommendation
https://github.com/lululxvi/deepxde
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.