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 811–820 of 4891 papers

TitleStatusHype
A Fast Effective Greedy Approach for MU-MIMO Beam Selection in mm-Wave and THz Communications—0
WaveMamba: Spatial-Spectral Wavelet Mamba for Hyperspectral Image Classification—0
Confidence-Aware Learning Optimal Terminal Guidance via Gaussian Process Regression—0
A pseudo-likelihood approach to community detection in weighted networks—0
A Neuromorphic Proto-Object Based Dynamic Visual Saliency Model with an FPGA Implementation—0
A computational framework for optimal and Model Predictive Control of stochastic gene regulatory networks—0
PropEnc: A Property Encoder for Graph Neural Networks—0
A Fast Approximate AIB Algorithm for Distributional Word Clustering—0
CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints—0
8-bit Numerical Formats for Deep Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified