Summary of Dense Relu Neural Networks For Temporal-spatial Model, by Zhi Zhang et al.
Dense ReLU Neural Networks for Temporal-spatial Model
by Zhi Zhang, Carlos Misael Madrid Padilla, Xiaokai Luo, Daren Wang, Oscar Hernan Madrid Padilla
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to fully connected deep neural networks using the Rectified Linear Unit (ReLU) activation function for nonparametric estimation. The authors derive non-asymptotic bounds that provide convergence rates, accounting for temporal and spatial dependencies in observed measurements. By modeling data on a manifold, the method tackles the curse of dimensionality, enhancing predictive performance and theoretical robustness. The technique is applied to neural networks in various general contexts, demonstrating its effectiveness for models with short-range dependence. Empirical simulations across synthetic response functions show that this approach outperforms established methods, highlighting the strong capabilities of dense neural networks (Dense NN) for temporal-spatial modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to use deep learning models, called Dense Neural Networks, to understand complex patterns in data. The authors found a better way to make predictions by accounting for how things are connected over time and space. This helps the model work better with real-world data that has many variables. They also showed how to deal with “dimensionality” problems that can occur when working with high-dimensional data. The results of their simulations show that this new approach performs well across different types of functions, outperforming existing methods. |
Keywords
» Artificial intelligence » Deep learning » Relu
