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 2171–2180 of 4891 papers

TitleStatusHype
A deep convolutional neural network model for rapid prediction of fluvial flood inundation—0
Energy-optimal Design and Control of Electric Vehicles' Transmissions—0
Energy-efficient and Robust Cumulative Training with Net2Net Transformation—0
CARROT: A Cost Aware Rate Optimal Router—0
Energy-based Preference Optimization for Test-time Adaptation—0
Careful Seeding for k-Medois Clustering with Incremental k-Means++ Initialization—0
Carbon-Aware Computing for Data Centers with Probabilistic Performance Guarantees—0
End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN—0
An even-load-distribution design for composite bolted joints using a novel circuit model and artificial neural networks—0
A Non-Parametric Bootstrap for Spectral Clustering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified