Seminar Computational Intelligence A (708.111)

Content of the seminar:

This year, we will discuss in Seminar A scientific articles about Deep Learning. Deep learning has emerged in the last years as one very powerful method for training deep neural architectures. It can overcome many problems of traditional neural network approaches and shows excellent practical performance.
Each student of this seminar will be able to choose a paper of his or her interest and, after preparation and discussion with Robert Legenstein, present it in a seminar talk.
Alternatively, students can also choose to present one of the recently published papers from our institute.

Slides from first session:

Doodle Poll for Talks

Update

Talks:

Date	Speaker	Paper
Mar 28, 2012	Robert Legenstein	A quick introduction to Boltzmann Machines
Apr 25, 2012	Daniel Markl	Reducing the dimensionality of data with neural networks, Slides
May 23, 2012	Stefan Grabuschnig	Learning Deep Architectures for AI (1)
May 23, 2012	Teresa Klatzer	Learning Deep Architectures for AI (2)
Jun 6, 2012	Florian Hubner	Unsupervised learning of image transformations
Jun 6, 2012	Dan Stoica	Discovering Binary Codes for Fast Document Retrieval by Learning Deep Generative Models
Jun 13, 2012	Markus Eger	The Recurrent Temporal Restricted Boltzmann Machine
Jun 13, 2012	Philipp Singer	Learning to Learn with Compound Hierarchical-Deep Models
Jun 20, 2012	Gernot Griesbacher	Neural sampling: A model for stochastic computation in recurrent networks of spiking neurons
Jun 20, 2012	Michael Rath	Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons

Papers on Deep Learning :

Basic papers:

Applications:

Extensions of deep networks:

Cognitive-Science Applications:

Misc:

IGI-Papers: