Author: pkurpadn@iastate.edu

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

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.

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

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:

The Phase Problem: A Mathematical Tour from Norbert Wiener to Random Matrices and Convex Optimization

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!

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

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.

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)