SOTAVerified

Computational Efficiency

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.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4271–4280 of 4891 papers

TitleStatusHype
Online Learning of Dynamical Systems: An Operator Theoretic Approach—0
A Refined Equilibrium Generative Adversarial Network for Retinal Vessel Segmentation—0
Meta-Learning for Variational Inference—0
Deep Auto-Deferring Policy for Combinatorial Optimization—0
Extreme Value k-means Clustering—0
A Non-asymptotic comparison of SVRG and SGD: tradeoffs between compute and speed—0
GraphQA: Protein Model Quality Assessment using Graph Convolutional NetworkCode0
Low Rank Training of Deep Neural Networks for Emerging Memory Technology—0
Non-negative Tensor Patch Dictionary Approaches for Image Compression and Deblurring Applications—0
The column measure and Gradient-Free Gradient Boosting—0
Show:102550
← PrevPage 428 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified