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 3921–3930 of 4891 papers

TitleStatusHype
CCSNet: a deep learning modeling suite for CO_2 storage—0
An Energy-efficient Aerial Backhaul System with Reconfigurable Intelligent Surface—0
"Forget" the Forget Gate: Estimating Anomalies in Videos using Self-contained Long Short-Term Memory Networks—0
SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical ZoomCode0
Fast DCTTS: Efficient Deep Convolutional Text-to-Speech—0
Linear systems with neural network nonlinearities: Improved stability analysis via acausal Zames-Falb multipliersCode0
Training Sparse Neural Network by Constraining Synaptic Weight on Unit Lp Sphere—0
Joint User Association and Power Allocation in Heterogeneous Ultra Dense Network via Semi-Supervised Representation Learning—0
Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples—0
Backup Plan Constrained Model Predictive Control—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified