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 3801–3810 of 4891 papers

TitleStatusHype
The 5th AI City ChallengeCode1
Partitioning sparse deep neural networks for scalable training and inference—0
Consistent and symmetry preserving data-driven interface reconstruction for the level-set method—0
Landmark-Aware and Part-based Ensemble Transfer Learning Network for Facial Expression Recognition from Static images—0
A novel time-frequency Transformer based on self-attention mechanism and its application in fault diagnosis of rolling bearings—0
Consistent Accelerated Inference via Confident Adaptive TransformersCode1
Machine learning-assisted surrogate construction for full-core fuel performance analysis—0
Improving Hyper-Relational Knowledge Graph CompletionCode0
Uncertainty Surrogates for Deep Learning—0
The Butterfly Effect in Primary Visual Cortex—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified