https://github.com/skiteskopes/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/skonor/group_and_shuffle
Group and Shuffle: Efficient Structured Orthogonal Parametrization
https://github.com/smhall97/deep_dreaming_music
https://github.com/soc-ucsd/verification
https://github.com/srijansengupta/pairednetworkbootstrap
https://github.com/ssysarch/LightPure
LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion Models
https://github.com/stefkim/stylegan-batik
https://github.com/stoensin/IC
https://github.com/sudeepsalgia/gp_threds
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance
https://github.com/sulam-group/learned-proximal-networks
End-to-end reconstruction meets data-driven regularization for inverse problems
https://github.com/sungnyun/LG-knowledge-distillation
https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet
https://github.com/sunwei925/uiqa
https://github.com/sustcsonglin/flash-linear-attention
https://github.com/sustcsonglin/pytorch_linear_rnn
https://github.com/suvhradipghosh07/Augmented-Random-Search
Simple random search provides a competitive approach to reinforcement learning
https://github.com/sydney-machine-learning/pingala
Surrogate-assisted Bayesian inversion for landscape and basin evolution models
https://github.com/t22m003/EfficientAD
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
https://github.com/tahaemara/LiteSeg
LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation
https://github.com/taowenyin/PatchNetVLAD
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition
https://github.com/teddykoker/nequip-eqx
High-performance training and inference for deep equivariant interatomic potentials
https://github.com/tensorflow/models/tree/master/research/im2txt
https://github.com/tensorflow/models/tree/master/research/marco
https://github.com/tensorflow/models/tree/master/research/slim
https://github.com/thang86/thang86.github.io
https://github.com/thearkaprava/ms-temba
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
https://github.com/theeighthday/seekworld
REINFORCE++: A Simple and Efficient Approach for Aligning Large Language Models
https://github.com/thomasaunger/babyai
https://github.com/thudm/cogview4
CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion
https://github.com/thunlp/readin
Sub-Character Tokenization for Chinese Pretrained Language Models
https://github.com/tianrluo/MRphy.py
Joint Design of RF and gradient waveforms via auto-differentiation for 3D tailored excitation in MRI
https://github.com/timeseriesAI/tsai/blob/main/tsai/models/TST.py
A Transformer-based Framework for Multivariate Time Series Representation Learning
https://github.com/tinymilky/NeRD
https://github.com/tmjeong1103/RL_with_RAY
Simple random search provides a competitive approach to reinforcement learning
https://github.com/tml-tuebingen/nshap
https://github.com/tomer9080/stock-prediction-using-rwkv
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.