Summary of Deep Random Features For Scalable Interpolation Of Spatiotemporal Data, by Weibin Chen et al.
Deep Random Features for Scalable Interpolation of Spatiotemporal Data
by Weibin Chen, Azhir Mahmood, Michel Tsamados, So Takao
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A scalable approach is needed to interpolate remote-sensing observations as earth observation systems rapidly grow. Gaussian processes (GPs) are a promising choice, but their poor scalability hinders expressivity. Deep GPs can capture complex patterns, but training and inference require crude approximations. This paper proposes Bayesian deep learning with random feature expansions to capture high-frequency patterns in data while allowing for mini-batched gradient descent for large-scale training. The approach is tested on various remote sensing datasets at local/global scales, producing competitive or superior results with well-calibrated uncertainties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Earth observation systems are growing rapidly, and we need a way to interpolate remote-sensing observations. One idea is to use Gaussian processes (GPs), which can capture patterns in the data. However, GPs aren’t very good at handling large amounts of data. This paper suggests using deep learning instead. It’s like building a neural network that captures complex patterns in the data. The approach is tested on different types of remote-sensing data and shows promising results. |
Keywords
» Artificial intelligence » Deep learning » Gradient descent » Inference » Neural network
