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 3901–3910 of 4891 papers

TitleStatusHype
COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction—0
Spectral Machine Learning for Pancreatic Mass Imaging Classification—0
ELF-VC: Efficient Learned Flexible-Rate Video Coding—0
An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning—0
BeamLearning: an end-to-end Deep Learning approach for the angular localization of sound sources using raw multichannel acoustic pressure data—0
Accelerating Coordinate Descent via Active Set Selection for Device Activity Detection for Multi-Cell Massive Random Access—0
Efficient training of physics-informed neural networks via importance sampling—0
Consistent and symmetry preserving data-driven interface reconstruction for the level-set method—0
Partitioning sparse deep neural networks for scalable training and inference—0
Landmark-Aware and Part-based Ensemble Transfer Learning Network for Facial Expression Recognition from Static images—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified