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 3631–3640 of 4891 papers

TitleStatusHype
NIDA-CLIFGAN: Natural Infrastructure Damage Assessment through Efficient Classification Combining Contrastive Learning, Information Fusion and Generative Adversarial Networks—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers—0
SE(3) Equivariant Graph Neural Networks with Complete Local FramesCode1
Computational Efficiency in Multivariate Adversarial Risk Analysis Models—0
Neural Flows: Efficient Alternative to Neural ODEsCode1
Exploiting Redundancy: Separable Group Convolutional Networks on Lie GroupsCode1
Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties—0
CvT-ASSD: Convolutional vision-Transformer Based Attentive Single Shot MultiBox DetectorCode0
Scalable Smartphone Cluster for Deep Learning—0
SOFT: Softmax-free Transformer with Linear ComplexityCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified