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 4071–4080 of 4891 papers

TitleStatusHype
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian ProcessesCode1
VINNAS: Variational Inference-based Neural Network Architecture Search—0
A Computational Separation between Private Learning and Online Learning—0
Dynamic Group Convolution for Accelerating Convolutional Neural NetworksCode1
Optimization from Structured Samples for Coverage Functions—0
Meta-Learning Divergences of Variational Inference—0
Local Grid Rendering Networks for 3D Object Detection in Point Clouds—0
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks—0
High Dimensional Bayesian Optimization Assisted by Principal Component AnalysisCode0
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified