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 3241–3250 of 4891 papers

TitleStatusHype
Robust DNN Surrogate Models with Uncertainty Quantification via Adversarial Training—0
Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learning—0
Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge DistillationCode2
Truthful Transaction Protocol for E-Commerce Networks Based on Double Auction—0
A kinetic approach to consensus-based segmentation of biomedical images—0
Active Learning with Tabular Language Models—0
A Neural Network Subgrid Model of the Early Stages of Planet Formation—0
Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia AlarmsCode1
A Filtering-based General Approach to Learning Rational Constraints of Epistemic Graphs—0
Fast Quasi-Optimal Power Flow of Flexible DC Traction Power Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified