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 2611–2620 of 4891 papers

TitleStatusHype
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
Retraction-Free Decentralized Non-convex Optimization with Orthogonal Constraints—0
Causal Customer Churn Analysis with Low-rank Tensor Block Hazard ModelCode0
Deep Penalty Methods: A Class of Deep Learning Algorithms for Solving High Dimensional Optimal Stopping Problems—0
RSDehamba: Lightweight Vision Mamba for Remote Sensing Satellite Image Dehazing—0
Federated Hybrid Model Pruning through Loss Landscape ExplorationCode0
Moreau Envelope for Nonconvex Bi-Level Optimization: A Single-loop and Hessian-free Solution Strategy—0
Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementarity—0
Neuromorphic Robust Estimation of Nonlinear Dynamical Systems Applied to Satellite Rendezvous—0
Scalable Subsampling Inference for Deep Neural Networks—0
Show:102550
← PrevPage 262 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified