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 3211–3220 of 4891 papers

TitleStatusHype
xTrimoABFold: De novo Antibody Structure Prediction without MSA—0
Lie Group Forced Variational Integrator Networks for Learning and Control of Robot SystemsCode1
Towards More Robust Interpretation via Local Gradient AlignmentCode0
Machine Learning Accelerated PDE Backstepping Observers—0
Fast-SNARF: A Fast Deformer for Articulated Neural FieldsCode2
Condensed Gradient BoostingCode0
Mutual Guidance and Residual Integration for Image Enhancement—0
Spatial-Spectral Transformer for Hyperspectral Image DenoisingCode1
Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification—0
MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified