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 841–850 of 4891 papers

TitleStatusHype
Input Convex Lipschitz RNN: A Fast and Robust Approach for Engineering TasksCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer Architectures for Computing Marginals of Probability DistributionsCode0
A Comparison of Deep Learning Methods for Cell Detection in Digital CytologyCode0
Implicit Regularization for Optimal Sparse RecoveryCode0
Improving Generalization of Medical Image Registration Foundation ModelCode0
Image Classification with CondenseNeXt for ARM-Based Computing PlatformsCode0
IHNet: Iterative Hierarchical Network Guided by High-Resolution Estimated Information for Scene Flow EstimationCode0
Image Super-Resolution Using Dense Skip ConnectionsCode0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified