Seminar Deep Learning Theory & Applications
Time and place:
Information regarding the online teaching will be added to the studon course
- Wed 10:15-11:45, Room 01.019
Fields of study
- WPF INF-MA from SEM 1
- WPF MT-MA-BDV from SEM 1
- WPF CE-MA-TA-MT from SEM 1
Deep Neural Networks or so-called deep learning has attracted significant attention in the recent years.
They have had a transformative influence on Natural Language Processing (NLP) and Artificial Intelligence (AI), with numerous success stories recent claims of superhuman learning performance in certain tasks.
According to Young et al. (2017), more than 70% of the papers presented at recent NLP conferences made use of deep learning techniques.
Interestingly, the concept of Neural Networks inspired researchers already over generations since Minky's famous book (cf. http://en.wikipedia.org/wiki/SocietyofMind ).
Yet again, this technology brings researchers to the believe that Neural Networks will eventually be able to learn everything (cf. http://www.ted.com/talks/jeremyhoward_the_wonderful_and_terrifying_implications_of_computers_that_canlearn ).
This year's main topic is: "Multi-Task Learning for Document Analysis",
i.e. we will analyze Documents of different nature (text, images, etc.)
by means of multi-task learning using different techniques, such as
Natural Language Processing, Handwriting recognition, etc.
- Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, Pascal Vincent, Arxiv, 2012. - Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press - Goldberg, Yoav (2017). Neural Network Methods for Natural Language Processing. Number 37 in Synthesis Lectures on Human Language Technologies. Morgan & Claypool. - Young, Tom; Hazarika, Devamanyu; Poria, Soujanya; Cambria, Erik (2017). Recent trends in deep learning based natural language processing. CoRR, abs/1708.02709. http://arxiv.org/abs/1708.02709 - Gradient-Based Learning Applied to Document Recognition, Yann Lecun, 1998 - Dropout: A Simple Way to Prevent Neural Networks from Overfitting, Srivastava et al. 2014 - Greedy layer-wise training of deep networks, Bengio, Yoshua, et al. Advances in neural information processing systems 19 (2007): 153. - Reducing the dimensionality of data with neural networks, Hinton et al. Science 313.5786 (2006): 504-507. - Training Deep and Recurrent Neural Networks with Hessian-Free Optimization, James Martens and Ilya Sutskever, Neural Networks: Tricks of the Trade, 2012. - Deep Boltzmann machines, Hinton et al. - Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Pascal Vincent et al. - A fast learning algorithm for deep belief nets, Hinton et al., 2006 - ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton, NIPS 2012 - Regularization of Neural Networks using DropConnect, Wan et al., ICML - OverFeat: Integrated recognition, localization and detection using convolutional networks. Computing Research Repository, abs/1312.6229, 2013. - http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial - http://deeplearning.net/tutorial/ - Deep Learning Course on Coursera by Hinton - DL platform with GPU support: caffe, lasagne, torch etc. - Stanford University CS 224: Deep Learning for NLP (http://cs224d.stanford.edu ) - University of Oxford: Deep Natural Language Processing (https://github.com/oxford-cs-deepnlp-2017/lectures )
Keywords: deep learning; neural networks; machine learning; pattern recognition; natural language processing
Expected participants: 10