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 2121–2130 of 4891 papers

TitleStatusHype
MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering—0
Modelling Concurrent RTP Flows for End-to-end Predictions of QoS in Real Time Communications—0
LightFusionRec: Lightweight Transformers-Based Cross-Domain Recommendation Model—0
Learning-to-Defer for Extractive Question Answering—0
A Kernelization-Based Approach to Nonparametric Binary Choice Models—0
Interweaving Insights: High-Order Feature Interaction for Fine-Grained Visual RecognitionCode0
Wave (from) Polarized Light Learning (WPLL) method: high resolution spatio-temporal measurements of water surface waves in laboratory setups—0
DST-TransitNet: A Dynamic Spatio-Temporal Deep Learning Model for Scalable and Efficient Network-Wide Prediction of Station-Level Transit Ridership—0
Learning to Control the Smoothness of Graph Convolutional Network Features—0
Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified