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Paper Exaplained — Discovering Symbolic Models from Deep Learning with Inductive Biases

Paper Exaplained — Discovering Symbolic Models from Deep Learning with Inductive Biases

Original Paper
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Nicola Bernini
A Paper in 5 mins — FactorVAE

A Paper in 5 mins — FactorVAE

Learning a disentangled representation is more or less like learning a “code” where each digit represents a specific “factor of variance”…
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Nicola Bernini
Keras in depth tutorial — CNN and MNIST

Keras in depth tutorial — CNN and MNIST

This is the Part 1 of a series of in depth tutorials about Keras Framework: it is not just focused on the how-to but more on the why and…
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Nicola Bernini
Andrew Zisserman — Self Supervised Learning 2018 — Comments

Andrew Zisserman — Self Supervised Learning 2018 — Comments

Some comments about Andrew Zisserman’s Notes on Self Supervised Learning
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Nicola Bernini
[Paper Readthrough] — Variational Bayesian Monte Carlo

[Paper Readthrough] — Variational Bayesian Monte Carlo

Overview
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Nicola Bernini
[Paper Readthrough] Visualizing Deep Networks by Optimizing with Integrated Gradients

[Paper Readthrough] Visualizing Deep Networks by Optimizing with Integrated Gradients

Overview
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Nicola Bernini
Read Through and Summarization — On Evaluating Adversarial Robustness

Read Through and Summarization — On Evaluating Adversarial Robustness

Disclaimer: this is intended to be a living document, not a traditional one which is written once and never updated.
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Nicola Bernini
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