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Wydział Matematyki, Informatyki i Mechaniki Uniwersytetu Warszawskiego

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Seminarium "Uczenie maszynowe"

Lista referatów

  • 2022-06-09, godz. 12:15,

    Shadi Shafighi (Uniwersytet Warszawski)

    Tumoroscope: a probabilistic graphical model for mapping tumor clones in cancerous tissue

    Tumor cell populations are highly heterogeneous and form clones with different genotypes. Geographically distinct parts of the tumor have different genetic and phenotypic compositions. Elucidating tumor heterogeneity is hampered by the fact that there is no techn...

  • 2022-06-02, godz. 12:15, 3140

    Sebastian Jaszczur (Uniwerystet Warszawski)

    Conditional Computation in Transformers

    Note: seminar will be in person with a follow-up lunch.   Transformer architecture is widely used in Natural Language Processing to get state of the art results. Unfortunately, such model quality is usually only possible by using extremely large models, which require significant resources dur...

  • 2022-05-26, godz. 12:15,

    Nabil Kahouadji (Northeastern Illinois University)

    Chicago structural violence and its effects on predicting colorectal adenoma in patients receiving colonoscopy

    In our retrospective study, we evaluate associations between neighborhood-level indicators of structural violence and colorectal adenoma using University of  Illinois Health electronic medical record (EMR) data obtained from patients receiving screening colonoscopy between the year 2015 and ...

  • 2022-04-07, godz. 12:15, 3140

    Piotr Kozakowski (Uniwerystet Warszawski)

    Entropy-Regularized Planning

    Recent works have shown the effectiveness of entropy regularization in Monte Carlo Tree Search (MCTS). In this presentation I will first introduce the framework of Maximum Entropy Reinforcement Learning and show how it can be applied to MCTS. Then I will present various variants of entropy regulariz...

  • 2022-03-31, godz. 12:15,

    Adam Izdebski (Uniwerystet Warszawski)

    Generative Modelling with Optimization for Molecule Discovery

    In recent years, discovering novel drug-like molecules become a common application of generative models. However, it is much harder to generate novel molecules that are at the same time optimized for being promising drug candidates. During the talk, I will give a snapshot of one of the many ...

  • 2022-03-17, godz. 12:15,

    Spyros Mouselinos (University of Warsaw)

    Measuring CLEVRness: Black-box Testing of Visual Reasoning Models

    How can we measure the reasoning capabilities of intelligence systems? Visual question answering provides a convenient framework for testing the model's abilities by interrogating the model through questions about the scene. However, despite scores of various visual QA datasets and architectures...

  • 2022-03-03, godz. 12:15,

    Michał Zawalski (Uniwerystet Warszawski)

    Gentle introduction to multi-agent reinforcement learning

    Recent studies show some impressive applications of reinforcement learning algorithms in sequential decision-making problems. In my talk, I will focus on problems that involve controlling a group of agents, i.e. multi-agent reinforcement learning. Though that domain shares clear similarities with th...

  • 2022-01-27, godz. 12:15,

    Jakub Świątkowski (Uniwerystet Warszawski)

    Tutorial on deep learning generative models for speech synthesis

    Speech synthesis has important applications in virtual assistants, voice interfaces, and accessibility. There has been rapid progress in the quality of speech synthesis systems in recent years thanks to deep learning generative models....

  • 2021-12-16, godz. 12:15,

    Jan Ludziejewski (Uniwerystet Warszawski)

    Towards Generative Music

    The OpenAI Jukebox was a groundbreaking model in sound generation and is still considered to be the state-of-the-art in the music modeling task. It consists of two separate networks, Vector Quantization Variational Autoencoder, which strongly compresses the raw waveform into a series of discret...

  • 2021-12-02, godz. 12:15,

    Piotr Tempczyk (Uniwersytet Warszawski)

    LIDL: Local Intrinsic Dimension estimation using approximate Likelihood

    Understanding how neural networks work is one of the most important questions in machine learning research. Their performance is connected with the shape of the data manifold. The structure of this manifold can be explored with local intrinsic dimension (LID) estimat...