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 3121–3130 of 4891 papers

TitleStatusHype
A Reinforcement Learning Approach for Scheduling Problems With Improved Generalization Through Order Swapping—0
U-Statistics for Importance-Weighted Variational Inference—0
Direct Estimation of Parameters in ODE Models Using WENDy: Weak-form Estimation of Nonlinear DynamicsCode0
Suitability of Quantized DEVS LIM Methods for Simulation of Power Systems—0
Efficient Ensemble for Multimodal Punctuation Restoration using Time-Delay Neural NetworkCode0
A Targeted Accuracy Diagnostic for Variational ApproximationsCode0
VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function ApproximationCode0
LightCTS: A Lightweight Framework for Correlated Time Series ForecastingCode1
Transformers in Single Object Tracking: An Experimental Survey—0
Refining a k-nearest neighbor graph for a computationally efficient spectral clusteringCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified