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 3751–3760 of 4891 papers

TitleStatusHype
ZoPE: A Fast Optimizer for ReLU Networks with Low-Dimensional Inputs—0
Ex uno plures: Splitting One Model into an Ensemble of Subnetworks—0
Fast and More Powerful Selective Inference for Sparse High-order Interaction Model—0
Obtaining Better Static Word Embeddings Using Contextual Embedding ModelsCode1
End-to-end reconstruction meets data-driven regularization for inverse problemsCode0
Stochastic filtering for multiscale stochastic reaction networks based on hybrid approximationsCode0
Learning Routines for Effective Off-Policy Reinforcement Learning—0
Hierarchical Temperature Imaging Using Pseudo-Inversed Convolutional Neural Network Aided TDLAS Tomography—0
Nonuniform Defocus Removal for Image Classification—0
Gradient Boosted Binary Histogram Ensemble for Large-scale Regression—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified