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 2801–2810 of 4891 papers

TitleStatusHype
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning—0
Robust GAN inversion—0
Robust Networked Federated Learning for Localization—0
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing—0
Reduced Simulations for High-Energy Physics, a Middle Ground for Data-Driven Physics Research—0
Stochastic Graph Bandit Learning with Side-Observations—0
NSF: Neural Surface Fields for Human Modeling from Monocular Depth—0
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decompositionCode1
A Generalization of Continuous Relaxation in Structured Pruning—0
EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified