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 861–870 of 4891 papers

TitleStatusHype
Implicit Generative Prior for Bayesian Neural NetworksCode0
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
Identification of stormwater control strategies and their associated uncertainties using Bayesian OptimizationCode0
Implicit and Parametric Avatar Pose and Shape Estimation From a Single Frontal Image of a Clothed HumanCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
A Comparative Study of Efficient Initialization Methods for the K-Means Clustering AlgorithmCode0
ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAMCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified