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 3601–3610 of 4891 papers

TitleStatusHype
BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement—0
Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow—0
A Survey on Computationally Efficient Neural Architecture Search—0
Searching for COMETINHO: The Little Metric That Could—0
Hierarchically Constrained Adaptive Ad Exposure in Feeds—0
Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognitionCode0
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Exploring the Open World Using Incremental Extreme Value MachinesCode0
An unsupervised, open-source workflow for 2D and 3D building mapping from airborne LiDAR data—0
Laplace HypoPINN: Physics-Informed Neural Network for hypocenter localization and its predictive uncertainty—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified