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 4331–4340 of 4891 papers

TitleStatusHype
Efficient Stochastic Gradient Hard Thresholding—0
Uncertainty-aware Efficient Subgraph Isomorphism using Graph Topology—0
Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques—0
Efficient Tensor Contraction via Fast Count Sketch—0
Efficient Test-time Adaptive Object Detection via Sensitivity-Guided Pruning—0
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators—0
Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions—0
Efficient training of physics-informed neural networks via importance sampling—0
Efficient Training of Physics-Informed Neural Networks with Direct Grid Refinement Algorithm—0
Efficient Training of Very Deep Neural Networks for Supervised Hashing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified