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 2821–2830 of 4891 papers

TitleStatusHype
Multi-Fidelity Methods for Optimization: A Survey—0
Exploiting Estimation Bias in Clipped Double Q-Learning for Continous Control Reinforcement Learning Tasks—0
Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional SystemsCode0
Oracle-Efficient Differentially Private Learning with Public Data—0
Accelerating Distributed Deep Learning using Lossless Homomorphic CompressionCode0
Contextual Multinomial Logit Bandits with General Value Functions—0
On Computationally Efficient Multi-Class Calibration—0
Differentially Private Training of Mixture of Experts Models—0
Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization—0
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm SurveillanceCode0
Show:102550
← PrevPage 283 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified