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 3881–3890 of 4891 papers

TitleStatusHype
Structured Dropout Variational Inference for Bayesian Neural Networks—0
Top-k eXtreme Contextual Bandits with Arm HierarchyCode0
Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks—0
Learning a Product Relevance Model from Click-Through Data in E-Commerce—0
Tight lower bounds for Dynamic Time WarpingCode0
Cerebral cortical communication overshadows computational energy-use, but these combine to predict synapse number—0
Dynamic Neural Networks: A Survey—0
Fast and Accurate Amplitude Demodulation of Wideband SignalsCode0
Refining a -nearest neighbor graph for a computationally efficient spectral clusteringCode0
An Efficient Active Set Algorithm for Covariance Based Joint Data and Activity Detection for Massive Random Access with Massive MIMO—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified