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Articles by Joseph Rocca
Understanding Diffusion Probabilistic Models (DPMs)
Building, step by step, the reasoning that leads to DPMs. This post was co-written with . Views and opinions expressed are solely those of the authors and do not express the views or opinions of their employers. Unless otherwise noted, all images are by the authors. Modelling complex probability distributions is a central problem in machine learning.
Understanding Diffusion Probabilistic Models (DPMs)
Credit: Laura Angot and Philippe Rocca (used with permission) This post was co-written with Baptiste Rocca. Views and opinions expressed are solely those of the authors and do not express the views or opinions of their employers. Unless otherwise noted, all images are by the authors. Modelling complex probability distributions is a central problem in machine learning.
The exploration-exploitation dilemma
Credit: Joshua Woroniecki on Pixabay This post was co-written with Baptiste Rocca. “Should I go for the decision that seems to be optimal, assuming that my current knowledge is reliable enough? Or should I go for a decision that seems to be sub-optimal for now, making the assumption that my knowledge could be inaccurate and that gathering new information could help me to improve it?”. In very simple terms, this is how we could summarise the exploration-exploitation dilemma.
Understanding Variational Autoencoders (VAEs)
Credit: Free-Photos on Pixabay This post was co-written with Baptiste Rocca. In the last few years, deep learning based generative models have gained more and more interest due to (and implying) some amazing improvements in the field. Relying on huge amount of data, well-designed networks architectures and smart training techniques, deep generative models have shown an incredible ability to produce highly realistic pieces of content of various kind, such as images, texts and sounds.
Understanding Generative Adversarial Networks (GANs)
Building, step by step, the reasoning that leads to GANs. This post was co-written with Baptiste Rocca. Yann LeCun described it as “the most interesting idea in the last 10 years in Machine Learning”. Of course, such a compliment coming from such a prominent researcher in the deep learning area is always a great advertisement for the subject we are talking about! And, indeed, Generative Adversarial Networks (GANs for short) have had a huge success since they were introduced in 2014 by Ian J.
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