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 4351–4360 of 4891 papers

TitleStatusHype
The Chan-Vese Model with Elastica and Landmark Constraints for Image Segmentation—0
Scaling Video Analytics on Constrained Edge NodesCode0
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components AnalysisCode0
The Stabilized Explicit Variable-Load Solver with Machine Learning Acceleration for the Rapid Solution of Stiff Chemical Kinetics—0
Compression with Flows via Local Bits-Back CodingCode0
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional NetworksCode1
Prototypical Bregman Networks—0
Dynamic Neural Network Channel Execution for Efficient Training—0
Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks—0
Dynamically Expanded CNN Array for Video Coding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified