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

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Showing 2911–2920 of 4891 papers

TitleStatusHype
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse HypergraphsCode0
Semantic Draw Engineering for Text-to-Image Creation—0
Kernel Heterogeneity Improves Sparseness of Natural Images RepresentationsCode0
Balancing Privacy, Robustness, and Efficiency in Machine Learning—0
Digital twin-assisted three-dimensional electrical capacitance tomography for multiphase flow imaging—0
A Novel ML-driven Test Case Selection Approach for Enhancing the Performance of Grammatical Evolution—0
Machine learning and domain decomposition methods -- a survey—0
ZeroShape: Regression-based Zero-shot Shape ReconstructionCode0
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)Code0
CRD: Collaborative Representation Distance for Practical Anomaly Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified