[21 March, 13:00] Let's Talk ML

Matus Zilinec - Machine text comprehension with BiDAF (slides)

I will talk about the Bi-Directional Attention Flow network for answering questions in natural language about an arbitrary paragraph. BiDAF is a multi-stage process that represents the context at different levels of granularity and uses attention mechanism to obtain a query-aware context representation without early summarization. The model achieves state-of-the-art results in Stanford Question Answering Dataset.

Radek Bartyzal - Objects that sound (slides)

A simple network architecture trained only from video is able to reach impressive results in localization of objects that produce a provided sound in a provided frame. This paper builds on an earlier work called 'Look Listen and Learn' and adds support of cross modal retrieval meaning that it can return an image for a provided sound and vice versa. I will present the new architecture and explain the advantages compared to the previous one.


[7 March, 13:00] Let's Talk ML

Markéta Jůzlová - Hyperband (slides)

Hyperband is a multi-armed bandit strategy proposed for hyper-parameter optimization of learning algorithms. Despite its conceptual simplicity, the authors report competitive results to state-of-the-art hyper-parameter optimization methods such as Bayesian optimization.
I will describe the main principle of the method and its possible extension.

Ondra Bíža - Visualizing Deep Neural Networks (slides)

Techniques for visualizing deep neural networks have seen significant improvements in the last year. I will explain a novel algorithm for visualizing convolutional filters and use it to analyze a deep residual network.

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[21 February, 13:00] Let's Talk ML

Radek Bartyzal - Born Again Neural Networks (slides)

Knowledge distillation is a process of training a compact model (student) to approximate the results of a previously trained, more complex model (teacher).
The authors of this paper have inspired themselves by this idea and tried training a student of same complexity as its teacher and found that the student surpasses the teacher in many cases. They also try to train a student that has a different architecture than the teacher with interesting results.

This will be one longer (40 min) talk where I will also describe the relevant architectures used in the paper. (DenseNet, Wide ResNet).

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[4 January, 11:00] Let's Talk ML

Martin Čejka - ToyArch: Czech take on general artificial intelligence

GoodAI is research company from Prague, which is doing experiments with GAI. In this presentation, I will talk about their biologically inspired approach, Toy architecture. How does it work and what is it capable of so far?

Petr Nevyhoštěný - Non-Linear Semantic Embedding

Non-Linear Semantic Embedding is a technique presented in a paper where it was used to learn an efficient mapping of instruments from a time-frequency representation to a low-dimensional space. It consists of automatic feature extraction using convolutional neural networks and a learning model which uses extrinsic information about similarity of training data.

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[14 December, 11:00] Let's Talk ML

Ondra Bíža - Overcoming catastrophic forgetting in neural networks (slides)

J. Kirkpatrick et al. (2017)
Artificial Neural Networks struggle with learning multiple different tasks due to a phenomenon known as catastrophic forgetting. In my talk, I will introduce catastrophic forgetting, describe a new learning algorithm called EWC that mitigates it and briefly mention neurobiological principles that inspired the creation of EWC.

Ondra Podsztavek - Deep Q-network (slides)

Deep Q-network (DQN) is a DeepRL system which combines deep neural networks with reinforcement learning and is able to master a diverse range of Atari 2600 games with only the raw pixels and score as input. It represents a general-purpose agent that is able to adapt its behaviour without any human intervention.

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