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 981–990 of 4891 papers

TitleStatusHype
Approximate XVA for European claims—0
CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints—0
ConDiSim: Conditional Diffusion Models for Simulation Based Inference—0
Conditional Expectation Propagation—0
Confidence-Aware Learning Optimal Terminal Guidance via Gaussian Process Regression—0
Configurable Foundation Models: Building LLMs from a Modular Perspective—0
Conformal testing: binary case with Markov alternatives—0
STC-ViT: Spatio Temporal Continuous Vision Transformer for Weather Forecasting—0
Online Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy—0
Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified