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 www.unofficialgoogledatascience.com
Using random effects models in prediction problems
| | www.fromthebottomoftheheap.net
Using random effects in GAMs with mgcv
2.7 parsecs away

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| | [AI summary] The text discusses the use of generalized additive models (GAMs) to represent random effects as smooths, enabling the testing of random effects against a null of zero variance. It compares this approach with traditional mixed-effects models (e.g., lmer) and highlights the advantages and limitations of each. Key points include: (1) Representing random effects as smooths in GAMs allows for efficient testing of variance components and compatibility with complex distributional models. (2) While GAMs can fit such models, they are computationally slower for large datasets with many random effects due to the lack of sparse matrix optimization. (3) The AIC values for models with and without random effects are similar, suggesting that the simpler model i...
| | sebastianraschka.com
Predictive modeling, supervised machine learning, and pattern classification
3.5 parsecs away

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| | I'm Sebastian: a machine learning & AI researcher, programmer, and author. As Staff Research Engineer Lightning AI, I focus on the intersection of AI research, software development, and large language models (LLMs).
| | www.huber.embl.de
Modern Statistics for Modern Biology - 4 Mixture Models
3.3 parsecs away

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| | If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book.
| | blog.otoro.net
Generating Large Images from Latent Vectors |
19.6 parsecs away

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| [AI summary] This text discusses the development of a system for generating large images from latent vectors, combining Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). It explores the use of Conditional Perceptual Neural Networks (CPPNs) to create images with specific characteristics, such as style and orientation, by manipulating latent vectors. The text also covers the ability to perform arithmetic on latent vectors to generate new images and the potential for creating animations by transitioning between different latent states. The author suggests future research directions, including training on more complex datasets and exploring alternative training objectives beyond Maximum Likelihood.