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 2151–2160 of 4891 papers

TitleStatusHype
Global-local Fourier Neural Operator for Accelerating Coronal Magnetic Field ModelCode0
Learning Causal Dynamics Models in Object-Oriented EnvironmentsCode0
A Foundation Model for the Earth SystemCode3
Federated Learning for Time-Series Healthcare Sensing with Incomplete ModalitiesCode0
Efficient Multi-agent Reinforcement Learning by PlanningCode1
Shallow Recurrent Decoder for Reduced Order Modeling of Plasma DynamicsCode1
Retraction-Free Decentralized Non-convex Optimization with Orthogonal Constraints—0
Deep Penalty Methods: A Class of Deep Learning Algorithms for Solving High Dimensional Optimal Stopping Problems—0
Causal Customer Churn Analysis with Low-rank Tensor Block Hazard ModelCode0
LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-DiffusionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified