Weekly Seminar – 03/23/2018 and 03/30/2018 – Robust Subspace Clustering

For these two weeks, Praneeth will be presenting the paper “Robust Subspace Clustering” which is available at “https://arxiv.org/abs/1301.2603

This chalk on blackboard talk will mostly focus on the algorithm and an overview of the theoretical results presented in the paper.

Time: 12.10 – 1.00 PM, Friday, March 23.
Venue: 2222 Coover Hall
Notes for the first session is available here.
Notes for the second session is here.

Weekly Seminar – 03/02/2018 – Graph Convolutional Neural Networks

This week’s speaker for the DSRG seminar series is Rahul Singh. He will talk about Graph Convolutional Neural Networks. The abstract and references are as follows.

While classical CNNs have been very successful when dealing with signals such as speech, images, or video, in which there is an underlying Euclidean structure (regular grids), recently there has been a growing interest in trying to apply CNNs on non-Euclidean geometric data. Some examples of such data include

– In social networks, the characteristics of users can be modeled as signals on the vertices of the social graph
– In sensor networks, the sensor reading are modeled as time-dependent signals on the vertices
– In genetics, gene expression data are modeled as signals defined on the regulatory network

The non-Euclidean nature of such data implies that there are no such familiar properties as common system of coordinates, vector space structure, or shift-invariance. Consequently, basic operations like convolution and shifting that are taken for granted in the Euclidean case are even not well defined on non-Euclidean domains.

The talk will be about the recent efforts made towards the generalization of CNNs from low-dimensional regular grids to high-dimensional irregular domains such as graphs.”


Michaël Defferrard et al., “Convolutional neural networks on graphs with fast localized spectral filtering.” In Advances in Neural Information Processing Systems, pp. 3844-3852. 2016.

Thomas Kipf and Max Welling, ” Semi-supervised classification with graph convolutional networks.”  In Proceedings of International Conference on Learning Representations, 2017.

Michael Bronstein et al., “Geometric deep learning: going beyond euclidean data.” IEEE Signal Processing Magazine 34.4 (2017): 18-42.

Venue:2222 Coover Hall
Time: 12.00 – 1.00PM Friday, March 2nd.

Weekly Seminar – 02/23/2018 – Consensus-based distributed stochastic gradient descent method for fixed topology networks

This week Zhanhong Jiang of Mechanical Engineering will speak about Distributed Deep Learning Algorithms.
The abstract:
The focus of this talk is towards developing novel distributed deep learning algorithms in order to solve challenging learning problems in various domains such as robotic networks. Specifically, I will present a consensus-based distributed stochastic gradient descent method for fixed topology networks. While some previous work has been done on this topic, the data parallelism and distributed computation are still not sufficiently explored. Therefore, the proposed method can be used to tackle such issues.

Venue: 2222 Coover Hall

Time: 12.10 – 1.00PM Friday, 23 February.

Weekly Seminar – 02/16/2018 – Interpreting the Deep Learning Models

This week’s speaker for the DSRG seminar series is Aditya Balu from Mechanical Engineering.


Deep learning models are very accurate to give good prediction. However, unlike the traditional shallow models, interpreting such model’s performance is very difficult. In this talk, I shall discuss several approaches which people have used for interpreting the models. Also, we shall discuss about application of such methods on different domains such as manufacturing etc.

Location: 2222 Coover Hall

Time: 12.00 Noon, Feb. 16th.

Notes: Slides for this Seminar are available here.