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 3571–3580 of 4891 papers

TitleStatusHype
An Unconstrained Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks—0
An Unsupervised Approach to Ultrasound Elastography with End-to-end Strain Regularisation—0
A parsimonious, computationally efficient machine learning method for spatial regression—0
A partial likelihood approach to tree-based density modeling and its application in Bayesian inference—0
A Path Integral Approach for Time-Dependent Hamiltonians with Applications to Derivatives Pricing—0
A Pathway to Near Tissue Computing through Processing-in-CTIA Pixels for Biomedical Applications—0
A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations—0
A Physics-informed machine learning model for time-dependent wave runup prediction—0
A Point-Based Approach to Efficient LiDAR Multi-Task Perception—0
A posteriori Trading-inspired Model-free Time Series Segmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified