[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.

[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).

[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.

[30 November, 11:00] Let's Talk ML

Filip Paulů - Analogové umělé neuronové sítě (slides)

Výkon dnes používaných neuronových sítí přestává stačit. Jak můžeme jejich rychlost zvýšit? Je nadále cesta počítat složité a rozsáhlé struktury neuronových sítí na digitálních procesorech a grafických kartách? Je možné jít jinou cestou? O tom všem si budeme povídat.

Václav Ostrožlík - Capsule Networks (slides)

Geoffrey Hinton, jeden z "otců deep learningu", nedávno publikoval dva články představující novou architekturu neuronových sítí, nazvanou Capsule Networks. V přednášce ukážu princip fungování těchto sítí, jak je možné je trénovat a jaké nové možnosti přinášejí.

Follow Us

Copyright (c) Data Science Laboratory @ FIT CTU 2014–2016. All rights reserved.