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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.
Pages
Casual Causality
About me
Posts
Rotating Features For Object Discovery
Published:
Structure is a useful but underleveraged inductive bias for representation learning.
The Machine Learning Interview Checklist
Published:
Cover your bases.
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.
publications
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).
talks
teaching
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.