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no code implementations • 25 Jan 2024 • Jiri Nemecek, Tomas Pevny, Jakub Marecek

We show that the search for the most likely explanations satisfying many common desiderata for counterfactual explanations can be modeled using mixed-integer optimization (MIO).

1 code implementation • 11 Jun 2023 • Jiri Nemecek, Tomas Pevny, Jakub Marecek

Here, we train a shallow tree with the objective of minimizing the maximum misclassification error across each leaf node.

no code implementations • 12 Sep 2022 • Leah Chrestien, Tomas Pevny, Antonin Komenda, Stefan Edelkamp

Optimization of heuristic functions for the A* algorithm, realized by deep neural networks, is usually done by minimizing square root loss of estimate of the cost to goal values.

no code implementations • 3 Dec 2021 • Leah Chrestien, Tomas Pevny, Antonin Komenda, Stefan Edelkamp

Learning a well-informed heuristic function for hard task planning domains is an elusive problem.

no code implementations • 17 Aug 2021 • Maximilian Samsinger, Florian Merkle, Pascal Schöttle, Tomas Pevny

Adversarial machine learning, i. e., increasing the robustness of machine learning algorithms against so-called adversarial examples, is now an established field.

4 code implementations • 19 May 2021 • Simon Mandlik, Matej Racinsky, Viliam Lisy, Tomas Pevny

Learning from raw data input, thus limiting the need for manual feature engineering, is one of the key components of many successful applications of machine learning methods.

no code implementations • 19 Apr 2021 • Simon Mandlik, Tomas Pevny

Even though machine learning algorithms already play a significant role in data science, many current methods pose unrealistic assumptions on input data.

2 code implementations • 4 May 2020 • Tomas Pevny, Vasek Smidl, Martin Trapp, Ondrej Polacek, Tomas Oberhuber

In this work, we propose Sum-Product-Transform Networks (SPTN), an extension of sum-product networks that uses invertible transformations as additional internal nodes.

no code implementations • 21 Jun 2019 • Paul Prasse, Rene Knaebel, Lukas Machlica, Tomas Pevny, Tobias Scheffer

Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable.

no code implementations • 3 Jun 2019 • Tomas Pevny, Vojtech Kovarik

This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability measures.

no code implementations • 30 Jun 2018 • Vyacheslav Kungurtsev, Tomas Pevny

Machine Learning models incorporating multiple layered learning networks have been seen to provide effective models for various classification problems.

no code implementations • 30 Jun 2018 • Vyacheslav Kungurtsev, Tomas Pevny

Machine Learning models incorporating multiple layered learning networks have been seen to provide effective models for various classification problems.

3 code implementations • 7 Mar 2017 • Tomas Pevny, Petr Somol

We show the classifier to perform with very high precision, while the learned traffic patterns can be interpreted as Indicators of Compromise.

3 code implementations • 23 Sep 2016 • Tomas Pevny, Petr Somol

Many objects in the real world are difficult to describe by a single numerical vector of a fixed length, whereas describing them by a set of vectors is more natural.

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