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 4101–4110 of 4891 papers

TitleStatusHype
DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks—0
Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and HypergraphsCode0
Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks—0
Global-and-Local Relative Position Embedding for Unsupervised Video Summarization—0
Graph signal processing for machine learning: A review and new perspectives—0
Prediction of hierarchical time series using structured regularization and its application to artificial neural networks—0
SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning—0
Fully Dynamic Inference with Deep Neural Networks—0
Intelligent Optimization of Diversified Community Prevention of COVID-19 using Traditional Chinese Medicine—0
Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified