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 3451–3460 of 4891 papers

TitleStatusHype
Reducing Catastrophic Forgetting in Neural Networks via Gaussian Mixture Approximation—0
ConvMAE: Masked Convolution Meets Masked AutoencodersCode2
Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review—0
FRC-TOuNN: Topology Optimization of Continuous Fiber Reinforced Composites using Neural Network—0
Scalable computation of prediction intervals for neural networks via matrix sketching—0
PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis—0
ImPosing: Implicit Pose Encoding for Efficient Visual Localization—0
DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation—0
Scalable Regularised Joint Mixture ModelsCode0
Optimal Thermal Management, Charging, and Eco-driving of Battery Electric Vehicles—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified