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

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Showing 1341–1350 of 4891 papers

TitleStatusHype
Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement LearningCode0
Deep Learning for Early Alzheimer Disease Detection with MRI ScansCode0
Deep Learning Evidence for Global Optimality of Gerver's SofaCode0
A Targeted Accuracy Diagnostic for Variational ApproximationsCode0
KernelDNA: Dynamic Kernel Sharing via Decoupled Naive AdaptersCode0
Kernel Heterogeneity Improves Sparseness of Natural Images RepresentationsCode0
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware FusionCode0
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep LearningCode0
Flexible Robust Optimal Bidding of Renewable Virtual Power Plants in Sequential MarketsCode0
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse HypergraphsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified