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 3541–3550 of 4891 papers

TitleStatusHype
On Evaluation Metrics for Graph Generative ModelsCode1
Credit Valuation Adjustment with Replacement Closeout: Theory and Algorithms—0
Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint—0
Deep Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSAR—0
Sketch-and-Lift: Scalable Subsampled Semidefinite Program for K-means ClusteringCode0
Nonparametric Feature Selection by Random Forests and Deep Neural Networks—0
Deep convolutional neural network for shape optimization using level-set approachCode0
An MRC Framework for Semantic Role Labeling—0
Recursive Least Squares Advantage Actor-Critic Algorithms—0
Study of Frequency domain exponential functional link network filters—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified