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 2551–2560 of 4891 papers

TitleStatusHype
ZeroShape: Regression-based Zero-shot Shape ReconstructionCode0
Machine learning and domain decomposition methods -- a survey—0
A Novel ML-driven Test Case Selection Approach for Enhancing the Performance of Grammatical Evolution—0
CRD: Collaborative Representation Distance for Practical Anomaly Detection—0
Enhancing Trade-offs in Privacy, Utility, and Computational Efficiency through MUltistage Sampling Technique (MUST)Code0
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements—0
Aggregating Multiple Bio-Inspired Image Region Classifiers For Effective And Lightweight Visual Place Recognition—0
Optimizing Distributed Training on Frontier for Large Language Models—0
Modeling non-linear Effects with Neural Networks in Relational Event ModelsCode0
EVI-SAM: Robust, Real-time, Tightly-coupled Event-Visual-Inertial State Estimation and 3D Dense Mapping—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified