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 3461–3470 of 4891 papers

TitleStatusHype
A Low-rank Projected Proximal Gradient Method for Spectral Compressed Sensing—0
AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for Efficient Sample Selection in Solving Partial Differential Equations—0
Alternates, Assemble! Selecting Optimal Alternates for Citizens' Assemblies—0
A Machine Learning Based Classification Approach for Power Quality Disturbances Exploiting Higher Order Statistics in the EMD Domain—0
A Machine Learning-Enhanced Benders Decomposition Approach to Solve the Transmission Expansion Planning Problem under Uncertainty—0
A machine learning framework for data driven acceleration of computations of differential equations—0
A Marginalized Particle Gaussian Process Regression—0
A Materials Foundation Model via Hybrid Invariant-Equivariant Architectures—0
A Memetic Walrus Algorithm with Expert-guided Strategy for Adaptive Curriculum Sequencing—0
American option pricing using generalised stochastic hybrid systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified