Weekly Seminar – 2/24/2017 – Deep Learning #6

Shiyang Li from Dr. Ajjarapu will be giving the final talk of the Deep Learning Series, on Generative Adversarial Networks. Please find the slides and reference material for the same.
Date: Friday, 24th Febraury
Time: 3:00pm to 4:00pm
Location: Coover, 2222
We will also be starting the next series of lectures on low rank and sparse signal processing. The schedule for the same has been updated on the list of talks page.

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.


The reference for the talk is Chapter 10 of the deep learning book and lecture notes from the Oxford Deep NLP course.

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

Continuing with the current theme of talks on Deep Learning, Jason Saporta, will be talking on Convolutional Neural Networks. Jason is jointly advised by Dr. Heike Hofmann and Dr. Alicia Carriquiry from the Department of Statistics. He will be discussing Chapter 9 of the Deep Learning book.
You can find the slides here.
You can also find additional references to his talk: A free online book and reference paper.
Details of the talk are as follows:
Date: Friday, 3rd February
Time: 3pm to 4pm
Venue: 2222, Coover Hall
Hoping to see you all there!

Weekly Seminar – 1/27/2017 – Deep Learning #2

After a great introductory talk on deep learning last week by Qi, Xiangyi Chen from Dr. Hong’s group will be presenting this week on optimization for neural networks (Chapter 8 of the Deep Learning book). The main focus of his talk will be on challenges in NN optimization, gradient based algorithms, the difference between training neural networks and pure optimization techniques.
Details for the seminar are as follows:
Date: Friday, 27th January 2017
Time: 3pm to 4pm
Venue: 2222, Coover Hall

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

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!

Weekly Seminar 1/13/17 + New developments

Welcome back, data science enthusiasts! We had an eventful seminar series last semester and have some new plans springing up for the new semester.

We’re kicking off our new batch of sessions with a talk by Binghui Wang from Dr. Neil Gong’s group, who will be presenting an introduction of Adversarial Machine Learning, a research filed that lies at the intersection of Machine Learning and Computer Security.

Date: Friday, 13th January, 2017
Time: 3pm to 4pm
Venue: 2222, Coover Hall

You can find the slides for the talk here: Slides

Some selected references:
We have a bunch of announcements regarding a new pattern of talks scheduled for this semester. We took a poll last month and decided to have 3-4 mini series of talks, each concentrating on a single topic. We will be discussing the speaker line-up as well as structuring for the same. Join us at 2222, Coover on Friday at 3pm to know more!
Also, beat the winter chill with some piping hot coffee that awaits you!

Weekly Seminar – 12/02/2016

Praneeth from Dr. Vaswani’s group will be the giving the final presentation for this semester for our data science reading group. Here are the details as forwarded by Praneeth for the following:

Abstract: I will motivate the problem of robust PCA through some examples, followed by a brief introduction to the existing approaches to solve this problem. I will describe the recently proposed Non-Convex algorithm in some detail. I also intend to go over the proof outline for demonstrating the performance guarantees by discussing a few key points in detail. If time permits I will show some results of the proposed algorithm obtained on simulated data.

Slides can be found here: RPCA
Paper available at: “https://arxiv.org/pdf/1410.7660v1.pdf“.

Date: 2nd December
Time: 3:00pm to 4:00pm
Venue: 3138, Coover Hall (2222 is unavailable this week)

Weekly Seminar – 11/18/2016

Gauri Jagatap, from Dr. Chinmay Hegde’s group will be presenting an overview of phase retrieval problems in signal processing, this Friday. She will primarily speak on a popular phase recovery strategy called AltMinPhase, based on the paper “Phase Retrieval Using Alternating Minimization” by Praneeth Netrapalli, Prateek Jain, and Sujay Sanghavi.

She will later also introduce a newer approach for phase retrieval of sparse signals, called “Efficient Compressive Phase Retrieval with Constrained Sensing Vectors“, by Sohail Bahmani, Justin Romberg.

Phase retrieval is essentially the problem of recovering the phase of a signal from magnitude measurements. In several applications in crystallography, optics, spectroscopy and tomography, it is harder or infeasible to record the phase of measurements, while recording the magnitudes is significantly easier.

Date: 11/18/2016

Time: 3:00pm – 4:00pm

Venue: 2222, Coover

Slides:Phase Retrieval (updated)