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 1351–1360 of 4891 papers

TitleStatusHype
A Targeted Accuracy Diagnostic for Variational ApproximationsCode0
Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle PhysicsCode0
Deep Coarse-to-fine Dense Light Field Reconstruction with Flexible Sampling and Geometry-aware FusionCode0
Flexible Robust Optimal Bidding of Renewable Virtual Power Plants in Sequential MarketsCode0
AI2STOW: End-to-End Deep Reinforcement Learning to Construct Master Stowage Plans under Demand UncertaintyCode0
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient NoiseCode0
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse HypergraphsCode0
DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional NetworksCode0
Accelerated Alternating Projections for Robust Principal Component AnalysisCode0
FinNet: Solving Time-Independent Differential Equations with Finite Difference Neural NetworkCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified