Summary of Exact, Fast and Expressive Poisson Point Processes Via Squared Neural Families, by Russell Tsuchida and Cheng Soon Ong and Dino Sejdinovic
Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families
by Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
First submitted to arxiv on: 14 Feb 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 The proposed Squared Neural Poisson Point Processes (SNEPPPs) model parameterizes the intensity function using a two-layer neural network, allowing for additional flexibility when the hidden layer is learned. By fixing the hidden layer and having a single neuron in the second layer, SNEPPPs resembles previous uses of squared Gaussian processes or kernel methods. The integrated intensity function can be computed in quadratic time with respect to the number of hidden neurons in many cases of interest. The approach is more memory- and time-efficient than naive implementations of squared or exponentiated kernel methods or Gaussian processes. Maximum likelihood and maximum a posteriori estimates can be obtained by solving a strongly convex optimization problem using projected gradient descent. SNEPPPs are demonstrated on real and synthetic benchmarks, with a software implementation available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Squared Neural Poisson Point Processes (SNEPPPs) is a new way to model complex data. It uses a special kind of neural network to create an intensity function that helps us understand patterns in the data. This approach is more flexible than previous methods and can be used to solve many problems efficiently. By using optimization techniques, we can find the best possible solution for our model. SNEPPPs are tested on real-world and fake data sets and a software program is available online. |
Keywords
* Artificial intelligence * Gradient descent * Likelihood * Neural network * Optimization
