# Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Published:

# Score functions and principal flows

Published:

## Higgins et al. - Towards a Definition of Disentangled Representations

Published:

Disentanglement is a concept rooted in geometric deep learning.

## Where is the nature of the relationship expressed in causal models?

Published:

Graphs don’t tell about the nature of dependence, only about its (non-)existence.

## AMMI 3 Notes: Geometric priors I

Published:

In the previous post, we dived deep into abstract algebra to motivate why Geometric Deep Learning is an interesting topic. Now we begin the journey to show that it is also useful in practice. In summary, we know that symmetries constrain our hypothesis class, making learning simpler—indeed, they can make learning a tractable problem. How does this happen?

## Mathematical foundations for Geometric Deep Learning

Published:

Yes, abstract algebra is actually useful for machine learning.

## LaTeX tricks

Published:

Improve typesetting and save space in your submissions, who does not want that?

## Bayesian Statistics - Techniques and Models flashcards

Published:

It’s again a statistics deck.

## Pearls of Causality #11: Front- and Back-Door Adjustment

Published:

Two ways to shut the door before confounding enters the scene.

## Pearls of Causality #10: Interventions and Identifiability

Published:

Interventions in disguise.

## Pearls of Causality #9: Potential, Genuine, Temporal Causes and Spurious Association

Published:

Hitting the nail on its arrowhead, a.k.a. when does $X$ cause $Y$?

## Pearls of Causality #8: Inferred Causation, $IC$, and ${IC}^*$

Published:

We will talk about IC, $IC$, and ${IC}^*$ in this post. You get the difference.

## Pearls of Causality #7: Latent Structures and Stability

Published:

DAGs like to play hide-and-seek. But we are more clever.

## Pearls of Causality #6: Markov Conditions

Published:

The model zoo of Markovian conditions is fascinating confusing. Let there be light!

## Pearls of Causality #5: Statistical vs Causal Inference

Published:

What you won’t be able to find in this post are unconditional claims of superiority of causal inference.

## Bayesian Statistics - From Concept to Data Analysis flashcards

Published:

This blog discusses causal inference. What is this post about Bayesian Statistics then?

## Pearls of Causality #4: Causal Queries

Published:

Asking a causal question is not casual.

## Pearls of Causality: The Causal Dictionary

Published:

No one told me that I need a dictionary for learning causal inference. Indeed, there was none before. Now there is.

## Probabilistic Graphical Models 3 flashcards

Published:

Not just parameter learning, but learning about parameter learning got easier today.

## Pearls of Causality #3: The properties of d-separation

Published:

A top-secret guide to d-separation. We will go deep, ready?

## Pearls of Causality #2: Markov Factorization, Compatibility, and Equivalence

Published:

This post deliberately (wink) tries to confuse you about the grand scheme of DAG equivalence. What a good deal, isn’t it?

## Probabilistic Graphical Models 2 flashcards

Published:

If your goal is to be able to recall Sum-Product Belief Propagation even at 3a.m., this is the post you are looking for.

## Pearls of Causality #1: DAGs, d-separation, conditional independence

Published:

d-separation is the bread and butter for deciding about conditional independence in DAGs. What is a DAG, anyway?

## A post series on Pearl: Causality

Published:

A causality blog cannot exist without discussing Judea Pearl’s Causality book. Thus, I am paying my debt.

## Probabilistic Graphical Models 1 flashcards

Published:

To make learning probabilistic graphical models frictionless and more fun.

## Causality resources

Published:

Good resources matter, a lot.

## Welcome to my journey!

Published:

A PhD student’s casual journey with causal inference.

## Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks

Published in 5th International Conference on Learning, Optimization and Data Science, 2019

This paper introduces two widely-applicable regularization methods based on the direct modification of weight matrices.

Recommended citation: Reizinger P., Gyires-Tóth B. (2019) "Stochastic Weight Matrix-Based Regularization Methods for Deep Neural Networks.." Springer LNCS 1. 11943. https://arxiv.org/pdf/1909.11977

## Attention-based Curiosity in Multi-agent Reinforcement Learning Environments

Published in International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO), 2019

This paper introduces a simulation suite for multi-agent Deep Reinforcement Learning (DynEnv) and applies attention-based techniques to utilize exploration.

Recommended citation: M. Szemenyei and P. Reizinger. (2019). "Attention-Based Curiosity in Multi-Agent Reinforcement Learning Environments" International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO).

## Attention-based Curiosity-driven Exploration in Deep Reinforcement Learning

Published in 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020

This paper introduces the attention mechanism in actor-critic architectures in the framework of curiosity-driven exploration.

Recommended citation: P. Reizinger and M. Szemenyei. (2020). "Attention-Based Curiosity-Driven Exploration in Deep Reinforcement Learning" 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://arxiv.org/pdf/1910.10840.pdf

## Learning to Play Robot Soccer from Partial Observations

Published in 23rd International Symposium on Measurement and Control in Robotics (ISMCR), 2020

This paper investigates how to improve scene reconstruction in multi-agent Deep Reinforcement Learning

Recommended citation: M. Szemenyei and P. Reizinger. (2020). "Learning to Play Robot Soccer from Partial Observations" 23rd International Symposium on Measurement and Control in Robotics (ISMCR).

## Digital Design I Laboratory

Undergraduate course, Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, 2016

In this course, I helped students to understand the basics of digital circuits and Boolean algebra.

## Computer Vision Systems

Graduate course, Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, 2019

I have translated the lecture notes from Hungarian to English and I was involved in grading exams.

## Deep Learning in Visual Computing

Graduate course, Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, 2019

I assisted during the laboratory coding exercises (Python, PyTorch, Google Colab) and I graded homeworks.

## Control Engineering and Image Processing Laboratory I

Graduate course, Budapest University of Technology and Economics, DepartmentDepartment of Control Engineering and Information Technology, 2020

I have developed course materials for Python programming, computer vision, and control engineering.