Seminář strojového učení a modelování / Machine Learning and Modelling Seminar

čtvrtek / Thursday 14:00
posluchárna / room S8
Malostranské náměstí 2

Společný seminář katedry teoretické informatiky a matematické logiky MFF UK a oddělění umělé inteligence ústavu informatiky AV ČR

Organized jointly by the Department of Theoretical Computer Science and Mathematical Logic, Faculty of Mathemeatics and Physics of the Charles University, and by the Department of Artificial Intelligence, Institute of Computer Science of the Czech Academy of Sciences

Kontakt: / Contact:
Martin Holeňa
www.cs.cas.cz/~martin
+420 266 052 921
martin@cs.cas.cz
skype martinholena

 

Na posledním semináři akademického roku 2024/2025, ve čtvrtek 22. května budeme mít hosta z Paříže. / At the last seminar of the academic year 2024/2025, on Thursday, May 25, we shall have a guest from Paris.

Program:

Victor Letzelter. Learning under ambiguity through multiple hypotheses and quantization

Victor’s talk will show how Winner-takes-all training can effectively address ambiguous machine learning tasks, and how this approach connects to a quantization objective

 

Přehled seminářů v roce 2025 / List of seminars in 2025

22.  5.

Victor Letzelter

Learning under ambiguity through multiple hypotheses and quantization

24.  4.

Jelle Hüntelmann

Learning to be uncertain: Of soft labels and soft losses

html

10.  4.

Marcel Kühn

Anti-Correlated Noise in Epoch-Based Stochastic Gradient Descent and its Implications

pdf

27.  3.

Andreas Opedal

Systematic Analysis of the Arithmetic Reasoning Capabilities of LLMs

pdf

13.  3.

Antoni Kowalczuk

Image Autoregressive Models Leak More Training Data Than Diffusion Models

pdf

27.  2.

Thomas Kleine Buening

Strategic Interactive Decision-Making

pdf

 

Přehled seminářů v roce 2024 / List of seminars in 2024

19. 12.

Peter Blohm

Probably Approximately Global Robustness Certification

pdf

 5. 12.

Vlad Yorsh

Structured State-Space Neural-Network Models

pdf

21. 11.

Jonas Hübotter

Transductive Active Learning for Fine-Tuning Large (Language) Models

pdf

 7. 11.

Krzysztof Kacprzyk

AI4Science: Discovering Governing Equations and Beyond

pdf

10. 10.

Ondřej Tichý

Bayesian Regression and Its Application to Atmospheric Emissions Estimation

pdf

23.  5.

Pierre Nicolay

Kinematic-Aware Neural Network for Dynamics Modeling

pdf

25.  4.

Balint Gyevnar

How Do We make Explainable AI Work for People?

pptx

11.  4.

Alex Goodall

Theory and Applications of Approximate Model-based Shielding for Safe Reinforcement Learning

pdf

28.  3.

Simon Rittel

Bayesian Causal Structure Learning

pdf

14.  3.

Oliver Sutton

Can Adversarial Robustness Be Certified for Classifiers Learning High Dimensional Data?

pdf

29.  2.

Michal Znalezniak

Contrastive Hierarchical Clustering

pdf

 1.  2.

Tomáš Pevný

Tractable Probabilistic Models for Hierarchical Data

pdf

Archiv: / Archive: 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023