References:
Weekly Seminar – 3/24/2017 – Low Rank and Sparse Signal Processing #2
1. David I Shuman et al. “The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains”. In: Signal Processing Magazine, IEEE 30.3 (2013), pp. 83–98.
2. A. Sandryhaila and J.M.F. Moura. “Discrete Signal Processing on Graphs: Frequency Analysis”. In: Signal Processing, IEEE Transactions on 62.12 (2014), pp. 3042–3054.
3. David I Shuman, Benjamin Ricaud, and Pierre Vandergheynst. “Vertex-frequency analysis on graphs”. In: Applied and Computational Harmonic Analysis 40.2 (2016), pp. 260–291.
Slides: GSP
Weekly Seminar – 3/3/2017 – Low Rank and Sparse Signal Processing #1
Weekly Seminar – 2/24/2017 – Deep Learning #6
Weekly Seminar – 2/17/2017 – Deep Learning #5
We’re inching towards the end of our Deep Learning (DL) series. We just have two talks left after which we move on to our next batch of topics on Sparse and Low Rank Signal Processing. Having gone through four talks on varied topics from introduction to optimization to models such as CNN and RNN, under the umbrella of Deep Learning, we thought it would be apt to look at how these models are actually being implemented.
Manaswi Podduturi from Dr. Hegde’s group will be speaking this Friday, on applications of deep learning. She will give brief talk about how to get started with implementing deep learning and introduce some popular resources for the same. She will also compare the performance of various DL algorithms on a fixed dataset.
If you’re itching to utilize the concepts discussed in the last few talks, but don’t know how to get started, do come for this session! For others, there’s coffee as usual!
Date: Friday, 17th February
Venue: 2222, Coover Hall
Time: 3:00pm to 4:00pm
Slides: Applications of DL
Weekly Seminar – 2/10/2017 – Deep Learning #4
Thanh Nguyen, from Dr. Hegde’s group will be giving the fourth talk in the Deep Learning series, this Friday. He will introduce recurrent neural networks for modelling the sequences and discuss some background ideas related to it. He will also talk about how to train the recurrent networks with back-propagation for a specific application of Language Modelling.
There is a slight shift in the timings; we will convene at 3:15 pm, instead of the usual 3:00pm. The venue remains the same.
Date: Friday, 10th February
Venue: 2222, Coover Hall
Time: 3:15pm to 4:15pm
You can find the slides for the talk here: Recurrent Neural Networks
Weekly Seminar – 2/3/2017 – Deep Learning #3
The Phase Retrieval Problem
Some introductory material on phase retrieval, emphasizing on PhaseLift which is a popular phase retrieval technique. Several applications are also highlighted.
Slides by Thomas Strohmer:
Weekly Seminar – 1/27/2017 – Deep Learning #2
Weekly Seminar – 1/20/2017 – Deep Learning #1
We’re starting with our new format of talks, for the semester. The first mini-series of talks will be on Deep Learning (by popular vote), and will be initiated by Qi Xiao from Dr. Wang’s group.
She will be giving an introduction to deep learning, including typical models like feedforward neural networks and the back-propagation algorithm. She will also briefly talk about deep learning applications.
Slides: Introduction to Deep Learning
References:
1. Chapter 6, Deep Learning, by Ian Goodfellow and Yoshua Bengio and Aaron Courville
2. UFLDL Tutorial
The tentative plan for the next four weeks is to cover chapters 6,8,9,10,11 of the book. The schedule for speakers has been updated on the List of Talks page.
Date: 1/20/2017
Time: 3pm to 4pm
Venue: 3043 Coover Hall
Join us for a deep learning session on a topic that has been buzzing in our community for quite a while now! We also have medium roast coffee from Caribou Coffee to keep that buzz up. We’re pretty excited to see the way this new format of seminars shapes up, so your participation will be highly appreciated!