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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3591–3600 of 4891 papers

TitleStatusHype
Approximately Aligned Decoding—0
Approximate Model-Based Diagnosis Using Greedy Stochastic Search—0
Approximate Steepest Coordinate Descent—0
Approximate XVA for European claims—0
Approximating DTW with a convolutional neural network on EEG data—0
Approximating particle-based clustering dynamics by stochastic PDEs—0
A Predefined-Time Convergent and Noise-Tolerant Zeroing Neural Network Model for Time Variant Quadratic Programming With Application to Robot Motion Planning—0
PropEnc: A Property Encoder for Graph Neural Networks—0
A Neuromorphic Proto-Object Based Dynamic Visual Saliency Model with an FPGA Implementation—0
A pseudo-likelihood approach to community detection in weighted networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified