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 3101–3110 of 4891 papers

TitleStatusHype
Resurrecting Recurrent Neural Networks for Long SequencesCode1
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG SignalsCode0
Scattering and Gathering for Spatially Varying Blurs—0
A pseudo-likelihood approach to community detection in weighted networks—0
Gaussian Max-Value Entropy Search for Multi-Agent Bayesian OptimizationCode0
Scaling Up 3D Kernels with Bayesian Frequency Re-parameterization for Medical Image SegmentationCode1
Agnostic PAC Learning of k-juntas Using L2-Polynomial Regression—0
A robust method for reliability updating with equality information using sequential adaptive importance sampling—0
Radio astronomical images object detection and segmentation: A benchmark on deep learning methods—0
Scatter-based common spatial patterns -- a unified spatial filtering framework—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified