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 3151–3160 of 4891 papers

TitleStatusHype
SCQPTH: an efficient differentiable splitting method for convex quadratic programmingCode0
Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance—0
Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks—0
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing—0
Shortcut-V2V: Compression Framework for Video-to-Video Translation based on Temporal Redundancy Reduction—0
SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays ClassificationCode0
Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions—0
ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting—0
Localization of DOA trajectories -- Beyond the grid—0
SEMI-CenterNet: A Machine Learning Facilitated Approach for Semiconductor Defect Inspection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified