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 1981–1990 of 4891 papers

TitleStatusHype
Comparative Study of Neural Network Methods for Solving Topological Solitons—0
Fast High-Quality Enhanced Imaging Algorithm for Layered Dielectric Targets Based on MMW MIMO-SAR System—0
Exploring Kolmogorov-Arnold Networks for Interpretable Time Series ClassificationCode0
A New Way: Kronecker-Factored Approximate Curvature Deep Hedging and its BenefitsCode0
Noise-Aware Ensemble Learning for Efficient Radar Modulation Recognition—0
Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data—0
Global spatio-temporal downscaling of ERA5 precipitation through generative AI—0
Fast Stochastic MPC using Affine Disturbance Feedback Gains Learned Offline—0
Learning Pore-scale Multi-phase Flow from Experimental Data with Graph Neural Network—0
Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified