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 4141–4150 of 4891 papers

TitleStatusHype
Neural Architecture Optimization with Graph VAE—0
The Nyström method for convex loss functions—0
FREEtree: A Tree-based Approach for High Dimensional Longitudinal Data With Correlated FeaturesCode0
An Extended Integral Unit Commitment Formulation and an Iterative Algorithm for Convex Hull Pricing—0
On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron—0
Faster Wasserstein Distance Estimation with the Sinkhorn Divergence—0
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks—0
GP3: A Sampling-based Analysis Framework for Gaussian Processes—0
AlgebraNetsCode0
Fast Maximum Likelihood Estimation and Supervised Classification for the Beta-Liouville Multinomial—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified