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 3701–3710 of 4891 papers

TitleStatusHype
Bioinspired Cortex-based Fast Codebook Generation—0
Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound ImagesCode0
Self-Supervised Point Cloud Registration with Deep Versatile Descriptors—0
Learning Neural Contextual Bandits Through Perturbed Rewards—0
Ab Initio Nucleic Acid Folding Simulations Using a Physics-Based Atomistic Free Energy Model—0
Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint—0
Credit Valuation Adjustment with Replacement Closeout: Theory and Algorithms—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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified