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 891–900 of 4891 papers

TitleStatusHype
Image Classification with CondenseNeXt for ARM-Based Computing PlatformsCode0
Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language ModelsCode0
CloudFixer: Test-Time Adaptation for 3D Point Clouds via Diffusion-Guided Geometric TransformationCode0
An Interpretable and Efficient Sleep Staging Algorithm: DetectsleepNetCode0
Adjusted chi-square test for degree-corrected block modelsCode0
Image Super-Resolution Using Dense Skip ConnectionsCode0
HyP-DESPOT: A Hybrid Parallel Algorithm for Online Planning under UncertaintyCode0
A differentiable programming framework for spin modelsCode0
Hyperbolic Procrustes Analysis Using Riemannian GeometryCode0
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference AggregationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified