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

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Showing 1871–1880 of 4891 papers

TitleStatusHype
The Open Source Advantage in Large Language Models (LLMs)—0
A Comprehensive Survey of Action Quality Assessment: Method and Benchmark—0
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers—0
Deployment Pipeline from Rockpool to Xylo for Edge Computing—0
Pretrained Event Classification Model for High Energy Physics Analysis—0
A Diagrammatic Approach to Improve Computational Efficiency in Group Equivariant Neural Networks—0
Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and RegressionCode0
Reliable and superior elliptic Fourier descriptor normalization and its application software ElliShape with efficient image processing—0
Enhancing Off-Grid One-Bit DOA Estimation with Learning-Based Sparse Bayesian Approach for Non-Uniform Sparse Array—0
Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified