Summer Camp 2018

No summer plans yet?

Keen to work on interesting projects in areas of Artificial Intelligence, Machine Learning, Data Mining and Big Data?

Well, this is the right place to be!

DataLab introduce Summer Camp 2018 for students interested in Artificial Intelligence, Data Mining, Machine Learning and Big Data.

Pre-register here.

Important Dates:

29.6.2018 at 10:00 - Summer Camp 2018 Kick off meeting

Read more: Summer Camp 2018

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

Markéta Jůzlová: Neural Architecture Search with Reinforcement Learning (slides)
The paper uses reinforcement learning to automatically generate architecture of a neural network for given task. Architecture is represented as a variable-length string. A controller network is used to generate such a string. The controller is trained with to assign higher probability architectures with better validation accuracy.

Petr Nevyhoštěný: Learning to Rank Applied onto Fault Localization (slides)
Debugging is a very time-consuming and tedious task which is also a large part of software development lifecycle. There already exist several techniques which aim to identify root causes of failure automatically. I will explain some of these techniques and describe how they can be combined together using a learning to rank algorithm.

[18 October, 11:00] Let's Talk ML

Radek Bartyzal - HOP-Rec: High-Order Proximity for Implicit Recommendation (pdf) (slides)

Two of the most popular approaches to recommender systems are based on factorization and graph-based models. This recent paper introduces a method combining both of these approaches.

Ondra Bíža - Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning (pdf) (slides)

Skilled robotic manipulation benefits from complex synergies between pushing and grasping actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free. This paper presents a policy able to learn pushing motions that enable future grasps, while learning grasps that can leverage past pushes.

[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.

[2 May, 13:00] Let's Talk ML

Petr Nevyhoštěný - Deep Clustering with Convolutional Autoencoders (slides)

Deep clustering utilizes deep neural networks to learn feature representation that is suitable for clustering tasks. This paper proposes a clustering algorithm that uses convolutional autoencoders to learn embedded features, and then incorporates clustering oriented loss on embedded features to jointly perform feature refinement and cluster assignment.

Václav Ostrožlík - Learning to learn by gradient descent by gradient descent (slides)

We've seen many significant improvements when replacing hand-designed features with learned ones before. However, optimization algorithms are still designed by hand. In this work, authors describe that the optimization algorithm can be seen as learning problem itself allowing to get better performing, specialized optimizer.

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