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 3611–3620 of 4891 papers

TitleStatusHype
Feature subset selection for kernel SVM classification via mixed-integer optimization—0
Will Bilevel Optimizers Benefit from Loops—0
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping—0
Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics—0
Linear Algorithms for Robust and Scalable Nonparametric Multiclass Probability EstimationCode0
Randomly Initialized One-Layer Neural Networks Make Data Linearly Separable—0
Spatial Attention-based Implicit Neural Representation for Arbitrary Reduction of MRI Slice Spacing—0
Approximate Message Passing with Parameter Estimation for Heavily Quantized MeasurementsCode0
Diverse super-resolution with pretrained deep hiererarchical VAEs—0
How Useful are Gradients for OOD Detection Really?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified