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

[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.

[18 April, 13:00] Let's Talk ML

Radek Bartyzal - Adversarial Network Compression (slides)

Knowledge distillation is a method of training a smaller student model using a previously trained larger teacher model to achieve a better classification accuracy than a normally trained student model.
This paper presents a new way of knowledge distillation by leveraging the recent advances in Generative Adversarial Networks.

Ondra Podsztavek - World Models (slides)

The paper explores building generative neural network models of popular reinforcement learning environments. A world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, it can train a very compact and simple policy that can solve the required task. It can even train an agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment.

[4 April, 13:00] Let's Talk ML

Ondra Bíža - Learning to grasp object with convolutional networks (slides)

Precise grasping of objects is essential in many applications of robotics such as assisting patients with motoric impairments. I will compare two approaches to learning how to grasp: Google's large-scale venture and a much smaller project carried out by the Northeastern University, which nevertheless achieved competitive results.

Václav Ostrožlík - Differentiable Neural Computer (slides)

Differentiable neural computer is a model based on neural network controller with external memory that is able to store and navigate complex data on its own. I'll go through its architectural details, compare it with Neural Turing Machines and show some interesting possibilities of using the model.

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