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 3121–3130 of 4891 papers

TitleStatusHype
Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits—0
Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction—0
Multi-stage Deep Learning Artifact Reduction for Pallel-beam Computed Tomography—0
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning—0
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing—0
Robust Networked Federated Learning for Localization—0
Robust GAN inversion—0
Reduced Simulations for High-Energy Physics, a Middle Ground for Data-Driven Physics Research—0
Stochastic Graph Bandit Learning with Side-Observations—0
A Generalization of Continuous Relaxation in Structured Pruning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified