Machine Learning Reading Group

ML group is a machine learning reading group at Purdue ECE, coordinated by Prof Stanley Chan. The purpose of the group is to provide a platform for intellectual discussions on latest topics in machine learning.

The reading group consists of a weekly meeting and a mailing list. We welcome all faculty and students to participate.

Mailing List

Please subscribe our mailing list at

https://engineering.purdue.edu/ECN/mailman/listinfo/machine-learning

When registering the mailing list, please use your Purdue email. Non-Purdue emails will be rejected.

Schedule (Spring 2021)

Time: Every Wedneday 9:30am-10:30am
Zoom Meeting ID: We will email the meeting link through our mailing list

Apr 21, 2021 (Wednesday)
Time: 9:30am-10:30am
Paper 1: Zhiyuan Mao An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Apr 14, 2021 (Wednesday)
Time: 9:30am-10:30am
Paper 1: Sheikh Shams Azam AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Paper 2: Wonwoong Cho Meta-Learning with Implicit Gradients

Apr 7, 2021 (Wednesday)
Time: 9:30am-10:30am
Paper 1: Abhiram Proper Network Interpretability Helps Adversarial Robustness in Classification
Paper 2: Taeuk Jang Informative Dropout for Robust Representation Learning: A Shape-bias Perspective

Mar 31, 2021 (Wednesday)
Time: 9:30am-10:30am
Paper 1: Mridul Gupta DropNet: Reducing Neural Network Complexity via Iterative Pruning
Paper 2: Praneeth Medepalli Rethinking Bias-Variance Trade-off for Generalization of Neural Networks

Mar 24, 2021 (Wednesday)
Time: 9:30am-10:30am
Paper 1: Ruqi Bai Normalizing Flows for Probabilistic Modeling and Inference
Paper 2: Yipei Wang Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge

Mar 17, 2021 (Wednesday)
Time: 9:30am-10:30am
University stress-free week. No meeting.

Mar 10, 2021 (Wednesday)
Time: 9:30am-10:30am
Paper 1: Pranshul Sardana Rethinking Pre-training and Self-training
Paper 2: Rui Wang Shapley Explanation Networks

Mar 3, 2021 (Wednesday)
Time: 9:30am-10:30am
Paper 1: Madhuri Nagare Set Distribution Networks: a Generative Model for Sets of Images
Paper 2: Ziyu Gong Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form

Feb 24, 2021 (Wednesday)
Time: 9:30am-10:30am
Paper 1: Hamidreza Lotfalizadeh Dynamic Routing Between Capsules
Paper 2: Sean Kulinski Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests

Feb 17, 2021 (Wednesday)
Time: 9:30am-10:30am
University reading day. No meeting.

Feb 10, 2021 (Wednesday)
Time: 9:30am-10:30am
Paper 1: Adarsh Kosta Playing Atari with Deep Reinforcement Learning
Paper 2: Alain Chen Label-Noise Robust Domain Adaptation

Feb 3, 2021 (Wednesday)
Time: 9:30am-10:30am
Paper 1: Haoteng Yin Generalization and Representational Limits of Graph Neural Networks
Paper 2: Janos Horvath Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules

Jan 27, 2021 (Wednesday)
Time: 9:30am-10:30am
Canceled

Schedule (Fall 2020)

Time: Every Wedneday 9:30am-10:30am
Zoom Meeting ID: We will email the meeting link through our mailing list

December 2, 2020 (Wednesday)
Time: 9:30am-10:30am
Tutorial: Optimal Transport 4  —  Applying OT in Machine Learning
Presenter: Sean Kulinski and Kent Gauen
Reference 1, Reference 2, Reference 3

November 18, 2020 (Wednesday)
Time: 9:30am-10:30am
Tutorial: Optimal Transport 3  —  Applying OT in Machine Learning
Presenter: Sean Kulinski and Kent Gauen
Reference 1, Reference 2, Reference 3

November 11, 2020 (Wednesday)
Time: 9:30am-10:30am
Tutorial: Optimal Transport 2  —  How to compute OT
Presenter: Sean Kulinski and Kent Gauen
Reference 1, Reference 2, Reference 3

November 4, 2020 (Wednesday)
Time: 9:30am-10:30am
Tutorial: Optimal Transport 1  —  Introduction to Optimal Transport (OT) and Wasserstein Distances
Presenter: Sean Kulinski and Kent Gauen
Reference 1, Reference 2, Reference 3

October 28, 2020 (Wednesday)
Time: 9:30am-10:30am
Paper 1: Yipei Wang Adversarial Risk via Optimal Transport and Optimal Couplings
Paper 2: Mridul Agarwal What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?

October 21, 2020 (Wednesday)
Time: 9:30am-10:30am
Paper 1: Xiangyu Qu Semi-Supervised StyleGAN for Disentanglement Learning
Paper 2: Kent Gauen Coresets for Data-efficient Training of Machine Learning Models

October 14, 2020 (Wednesday)
Time: 9:30am-10:30am
Zoom Meeting ID: 973 7710 3589
Paper 1: Taeuk Jang Understanding and Mitigating the Tradeoff between Robustness and Accuracy
Paper 2: Sheikh Shams Azam Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets

October 7, 2020 (Wednesday)
Time: 9:30am-10:30am
Zoom Meeting ID: 973 7710 3589
Paper 1: Madhuri Nagare T-Basis: a Compact Representation for Neural Networks
Paper 2: Ruqi Bai Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization

September 30, 2020 (Wednesday)
Time: 9:30am-10:30am
Zoom Meeting ID: 973 7710 3589
Paper 1: Deboleena Roy Concise Explanations of Neural Networks using Adversarial Training
Paper 2: Ziyu Gong Fair Generative Modeling via Weak Supervision

September 23, 2020 (Wednesday)
Time: 9:30am-10:30am
Zoom Meeting ID: 973 7710 3589
Paper 1: Zeman Shao Implicit competitive regularization in GANs
Paper 2: Yue Han Distribution Augmentation for Generative Modeling

September 16, 2020 (Wednesday)
Time: 9:30am-10:30am
Zoom Meeting ID: 973 7710 3589
Paper 1: Justin Yang Representation Learning via Adversarially-Contrastive Optimal Transport
Paper 2: Runyu Mao A Simple Framework for Contrastive Learning of Visual Representations

September 9, 2020 (Wednesday)
Time: 9:30am-10:30am
Zoom Meeting ID: 973 7710 3589
Paper 1: Saima Sharmin The Implicit and Explicit Regularization Effects of Dropout
Paper 2: Evzenie Coupkova Estimating Generalization under Distribution Shifts via Domain-Invariant Representations

September 2, 2020 (Wednesday)
Time: 9:30am-10:30am
Zoom Meeting ID: 973 7710 3589
Paper 1: Sean Kulinski Understanding Self-Training for Gradual Domain Adaptation
Paper 2: Tanvi Sharma From ImageNet to Image Classification: Contextualizing Progress on Benchmarks

Spring 2020

April 28, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Canceled due to COVID-19

April 21, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Canceled due to COVID-19

April 14, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Canceled due to COVID-19

April 7, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Canceled due to COVID-19

March 31, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Canceled due to COVID-19

March 24, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Canceled due to COVID-19

March 17, 2020 (Tuesday)
Spring Break

March 10, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Paper 1: Yash Sanghvi Paper 4.1 Reconciling modern machine learning practice and the bias-variance trade-off
Paper 2: Madhuri Nagare Paper 4.4 Understanding GANs: the LQG Setting

March 3, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Faculty Meeting. Not for Public.

February 25, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Paper 1: Stanley Chan Paper 1.1 An overview of Plug-and-Play Priors
Paper 2: Diyu Yang Paper 1.1 Consensus Equilibrium

February 18, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Paper 1: Internal Talk.

February 11, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Presenter: Prof. David Inouye
Paper 1: Paper 1.4 Deep Density Destructors
Paper 2: Paper 1.5 Putting an End to End-to-End: Gradient-Isolated Learning of Representations

February 4, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Faculty Meeting. Not for Public.

January 28, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Paper: Stanley Chan Paper 4.3 Robustness May Be at Odds with Accuracy

January 21, 2020 (Tuesday)
Time: 2pm-3pm
Room: MSEE 239
Meeting Canceled.

Fall 2019

December 4, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: S. Shams, Deep Mutual Learning
Paper 2: Zhiyuan Mao, Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors

November 27, 2019
Thanksgiving.

November 20, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Henry Wang, Pattern classification with missing data: a review
Paper 2: Rehana Mahfaz, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

November 13, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Chang-Shen Lee, Collaborative Deep Learning for Recommender Systems
Paper 2: Yinghan Long, Non-local neural networks

November 6, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Junghoon Kim, Inference and Missing Data
Paper 2: Ankit Manerikar, Speed versus accuracy in visual search: Optimal performance and neural architecture

October 30, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Tony Allen, Geometric deep learning on graphs and manifolds using mixture model CNNs
Paper 2: Yash Sanghvi, Seeing into Darkness: Scotopic Visual Recognition

October 23, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Nick Chimitt, The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Paper 2: Madhuri Nagare, Image-to-Image Translation with Conditional Adversarial Nets

October 16, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper: Prof. Chris Brinton
Title: TBD

October 9, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Kent Gauen, Convolutional Sequence to Sequence Learning
Paper 2: Fangda Li, Large scale GAN training for high fidelity natural image synthesis

October 2, 2019
No meeting

September 25, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Speaker: Prof. Joy Wang
Title: Learning from Limited Labeled Data via Generative Adversarial Network

September 18, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Di Chen, Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Paper 2: Isha Garg, Opening the black box of Deep Neural Networks via Information

September 11, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Yue Cao, Deep Reinforcement Learning for Robotic Manipulation
Paper 2: Maliha Hossain, Loss Functions for Image Restoration with Neural Networks

September 4, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Speaker: Prof. David Inouye
Title: Deeper Understanding via Destructive Deep Learning and Counterfactual Explanations

August 28, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Intro: A 10-minute Introduction to ML Reading Group. Tips on Presentation by Prof. Buzzard
Talk 1: Stanley Chan, Detecting Photoshopped Faces by Scripting Photoshop
Talk 2: Abhiram Gnanasambandam, Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data

Summer 2019

August 7, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Abdu Alanazi, Deep Image Prior
Paper 2: canceled

July 31, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Tony Allen, Building Machines That Learn and Think Like People
Paper 2: Yiheng Chi, Theoretically Principled Trade-off between Robustness and Accuracy

July 24, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Hao Li, Neural Ordinary Differential Equations
Paper 2: Grant Bowman, A Neural Algorithm of Artistic Style

July 17, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Qingshuang Chen, Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
Paper 2: TBD, Tracking the World State with Recurrent Entity Networks

July 10, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Fernando Davis, Analysis of classifiers’ robustness to adversarial perturbations
Paper 2: He Liu, Improving drug discovery with high-content phenotypic screens by systematic selection of reporter cell lines

July 3, 2019
TBD

Jun 26, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Xiwen Zhang, Exploring Randomly Wired Neural Networks for Image Recognition
Paper 2: Haoyu Chen, Who Said What: Modeling Individual Labelers Improves Classification

Jun 19, 2019
Time: 2:30-3:30pm
Room: MSEE 239
Paper 1: Nick Chimitt, Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
Paper 2: Zhiyuan Mao, Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data

Spring 2019

Apr 24, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Kent Gauen, Understanding deep learning requires rethinking generalization
Paper 2: MeherChaitanya Pindiprolu and Pal Raktim, An Actor-Critic Algorithm for Sequence Prediction

Apr 17, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Beheshteh Rakhshan and Shixin Zheng, Explaining and harnessing adversarial examples
Paper 2: Ryan Dailey and Mridul Agarwal, Stronger generalization bounds for deep nets via a compression approach

Apr 10, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Dongqi Zheng and Amrit Nagarajan, Understanding deep convolutional networks
Paper 2: Xiaodong Huang and Yubo Wang, How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective

Apr 3, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Christopher Wright and Jacob Desmond, In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
Paper 2: Brian Page and Liyao Gao, Learning to Optimize Neural Nets

Mar 27, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Alex Carignan and Zachary Letterhos, Auto-Encoding Variational Bayes
Paper 2: Tony Allen and Gavin Glenn, Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

Mar 20, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Guanzhe Hong, Quantum machine learning
Paper 2: Yifan Wang and Yang Zhang, Improved Techniques for Training GANs

Mar 13, 2019
Spring Break

Mar 6, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Natat Sombuntham, Obfuscated Gradients Give a False Sense of Security
Paper 2: Omar Eldaghar and Alden Bradford, Audio Adversarial Examples: Targeted Attacks on Speech-to-Text

Feb 27, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Rehana Mahfuz, Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Paper 2: Yizhen Zhang and Yujie Chen, Attention Is All You Need

Feb 20, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Omar Elgendy, Image Super-Resolution Using Deep Convolutional Networks
Paper 2: Sthitapragyan Parida and Ziyu Gong, Empirical study of the topology and geometry of deep networks

Feb 13, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Ye Shi and Shiqi Zhang, Discriminative vs Informative Learning
Paper 2: Carlos Salinas and Max Ruby, Approximation by Superpositions of a Sigmoidal Function

Feb 6, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Jingshuang Chen and Xiaofeng Ou, Learning Functions: When Is Deep Better than Shallow?
Paper 2: Abhiram Gnanasambandam and Vineeth Ravi, Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems

Jan 30, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Xiangyu Qu, Mask R-CNN
Paper 2: Lakshay Kharbanda and Rahul Nanda, Machine Learning: The High-Interest Credit Card of Technical Debt

Jan 23, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Omkar Patil and Parameswaran Desigavinayagam, U-Net: Convolutional Networks for Biomedical Image Segmentation
Paper 2: Maliha Hossain and Balke Thilo, Deep Residual Learning for Image Recognition

Jan 16, 2019
Time: 2:30-3:30pm
Room: WTHR 320
Paper 1: Diyu Yang, A Survey of Model Compression and Acceleration for Deep Neural Networks
Paper 2: Omar Elgendy, A Review of Convolutional Neural Networks for Inverse Problems in Imaging

Jan 9, 2019
No Meeting

Fall 2018

Dec 5, 2018
No Reading Group. Final Exam

Nov 28, 2018
Time: 2:30-3:30pm
Room: MSEE 239
Title: TBD
Presenster: Jing Li
Reading 1:

Nov 21, 2018
Title: Thanksgiving Break

Nov 14, 2018
Time: 2:30-3:30pm
Room: POTR 234 Fu Room
Title: Perceptual Learning
Presenster: Prof Amy Reibman
Reading 1:

Nov 7, 2018
Time: 2:30-3:30pm
Room: MSEE 239
Title: Graph Convolutional Networks
Presenster: David Ho
Reading 1: https://arxiv.org/abs/1609.02907

Oct 31, 2018
Time: 2:30-3:30pm
Room: POTR 234 Fu Room
Title: Doing Deep Learning on ITaP Clusters
Presenster: Amiya Maji
Reading 1: Overview of RCAC

Oct 24, 2018
Time: 2:30-3:30pm
Room: MSEE 239
Title: Empirical Study of the Topology and Geometry of Deep Networks
Presenster: Xiangyu Qu
Reading 1: Fawzi et al. CVPR 2018

Oct 17, 2018
Time: 2:30-3:30pm
Room: POTR 234 Fu Room
Title: Wasserstein GAN
Presenster: Omar Elgendy
Reading 1: https://arxiv.org/abs/1701.07875

Oct 10, 2018
Time: 2:30-3:30pm
Room: POTR 234 Fu Room
Title: Linear Factor Analysis and Auto-encoder
Presenster: Rajeev Sahay
Reading 1: Deep Learning Book Ch. 13 and Ch. 14

Oct 3, 2018
Room: MSEE 239
Title: Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Presenster: Guanzhe Hong
Reading 1: https://arxiv.org/abs/1801.10578

Sep 26, 2018
Room: POTR 234 Fu Room
Title: Neural Network for Universal Approximation
Presenster: Sri Kalyan Yarlagadda
Reading 1: Multilayer Feedforward Networks are Universal Approximators

Sep 19, 2018
Time: 2:30-3:30pm
Room: POTR 234 Fu Room
Title: CNSIP Seminar: Signal and Information Processing in the Age of Massive Data: Exploiting the Blessings of Dimensionality
Presenster: Prof Yue M. Lu, Harvard John Paulson School of Engineering and Applied Sciences

Sep 12, 2018
Time: 2:30-3:30pm
Room: POTR 234 Fu Room
Title: Image-Based 3D Scene Understanding: From Camera Geometry to Deep Learning
Presenster: Shaobo Fang

Sep 5, 2018
Time: 2:30-3:30pm
Room: MSEE 239
Title: Practical Considerations of Deep Networks
Presenster: Diyu Yang
Reading 1: Deep Learning Textbook Ch.11

Aug 29, 2018
Time: 2:30-3:30pm
Room: POTR 234 Fu Room
Title: Deep Learning Research in Video and Image Processing Lab
Presenster: Prof. Edward Delp
Reading 1: VIPER website

Summer 2018

Aug 08, 2018
Deep Learning Summer Workshop 2018 Part 2

Aug 01, 2018
Title: Reinforcement Learning
Presenster: Prof. Charles Bouman
Reading 1: Useful links

July 25, 2018
Title: Adversarial Attack
Presenter: Prof. Stanley Chan
Reading 1: Explaining and Harnessing Adversarial Examples’’, by Goodfellow et al.

July 18, 2018
Title: Recurrent and Recursive Networks
Presenter: Prof. Aly El Gamal
Reading 1: Deep Learning Textbook, Chapter 10

July 11, 2018
Title: Feed Forward Network and Back Propagation
Presenter: Soumendu Majee

Reading 1: Deep Learning Textbook, Chapter 6

July 04, 2018
No Meeting.
Independence Day

June 27, 2018
Title: Convolutional Neural Networks
Presenter: Prof. Maggie Zhu
Reading 1: Stanford CS 231 Note
Reading 2: Deep Learning Textbook, Chapter 9

June 20, 2018
Deep Learning Summer Workshop 2018 Part 1

June 13, 2018
Title: TensorFlow Introduction
Presenter: Amir Ziabari
Reading 1: TensorFlow Tutorial

June 06, 2018
Title: PyTorch Introduction
Presenter: Xiangyu Qu
Reading 1: PyTorch Tutorial

May 30, 2018
Title: Deep Learning Optimization
Presenter: Prof. Aly El Gamal
Reading 1: Deep Learning Textbook Chapter 8

May 23, 2018
Title: Generative Adversarial Networks
Presenter: Prof. Stanley Chan
Reading 1: ‘‘NIPS 2016 Tutorial: Generative Adversarial Networks’’ by Goodfellow
Reading 2: ‘‘Generative Adversarial Nets’’ by Goodfellow et al.

May 16, 2018
Title: Overview of Deep Learning
Presenter: Prof. Greg Buzzard
Reading 1: Deep Learning Textbook Chapter 1