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 4051–4060 of 4891 papers

TitleStatusHype
Global-and-Local Relative Position Embedding for Unsupervised Video Summarization—0
Graph signal processing for machine learning: A review and new perspectives—0
SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning—0
Prediction of hierarchical time series using structured regularization and its application to artificial neural networks—0
Fully Dynamic Inference with Deep Neural Networks—0
Intelligent Optimization of Diversified Community Prevention of COVID-19 using Traditional Chinese Medicine—0
Corner Proposal Network for Anchor-free, Two-stage Object DetectionCode1
Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications—0
Oblique Predictive Clustering TreesCode0
Langevin Monte Carlo: random coordinate descent and variance reduction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified