CSCI 8945: Advanced Representation Learning (Fall 2019)

Instructor Sheng Li
E-mail: sheng.li@uga.edu
Time and Location of Lecture TR: 11:00 am - 12:15 pm Boyd GSRC 306
W: 11:15 pm - 12:05 pm Boyd GSRC 306
Office Hours and Location Wednesday: 1:00 pm - 2:00 pm or by an email appointment.
Boyd GSRC 804

Course Description

This course presents a rigorous overview of advanced representation learning algorithms in machine learning, from the traditional subspace learning models to the recent deep representation learning models. Applications in the fields of computer vision, data mining, and natural language processing will be covered.

Please refer to the course syllabus for more details.

Required Prerequisite

CSCI 6380 (Data Mining), CSCI 6550 (Artificial Intelligence) or Permission of Department

Grading

Section Portion Description
Paper Review 20% Write comprehensive paper reviews for assigned papers
Paper Presentation 20% Present assigned papers and lead discussion
Team Project 45% Project proposal (10%); Progress review (5%); Final presentation (15%) and report (15%)
In-class Participation 15%

Grade Conversion Table:

Letter Grade A A- B+ B B- C+ C C- D+ D D- F
Range [93,100] [90,92] [87,89] [83,86] [80,82] [77,79] [73,76] [70,72] [67,69] [63,66] [60,62] [0,59]

Academic Honesty

We will strictly follow UGA’s Academic Honesty Policy. Dishonest behavior will not be tolerated and may result into failing the course. Please contact the instructor if you have any concerns regarding this issue.

Paper Review and Presentation

Note: Please sign up for paper presentations (Deadline: 11:59pm, August 16, 2019)

Paper Review: Students will be required to review 10 papers over the semester and submit the review to eLC by 11:59PM of the due date shown in class schedule below. The reviewed paper should be chosen from the papers that will be presented in the following classes (Refer to class schedule for more details). The reviewed papers should not contain the papers you’re going to present in the class. The review should summarize the main idea and contributions of the paper, describe the major experimental results, and discuss the strengths and weaknesses of the paper. The students are also encouraged to check the follow-up works on the topic of the assigned paper (e.g., search the latest papers that cite the assigned paper), summarize the state-of-the-art methods and results, and discuss possible future research directions.

Pape Presentation: Each student or group (2 students) will be required to present two research papers over the semester. Each presenter should prepare slides for a 25 minutes talk on the paper. Slides for the talk must be emailed to the instructor by midnight (12:00am) before the class. The talk should clearly address the following points: (1) motivation and problem statement; (2) related work; (3) methodology; (4) experiments; (4) conclusions; and (5) at least 3 questions for discussion. The presenter will need to lead another 10 minutes discussion during or after the talk. The presenter should prepare discussion questions that lead to a deeper analysis of the paper’s content, strengths, weaknesses, and future works.

Class Schedule (Tentative)

Week Date Topic Papers Presenters Notes
1 08/14 (W) Course Overview N/A Instructor N/A
08/15 (R) An Overview of Machine Learning (I) N/A Instructor
2 08/20 (T) An Overview of Machine Learning (II) N/A Instructor
08/21 (W) Subspace Learning N/A Instructor Review-1 Due for papers: 08/22, 08/27
08/22 (R) Subspace Learning (1) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. PAMI, 2004.
(2) Two-Dimensional Linear Discriminant Analysis, NIPS 2004
Students
3 08/27 (T) Subspace Learning (1) Subclass Discriminant Analysis. IEEE Trans. PAMI, 2006
(2) General Tensor Discriminant Analysis and Gabor Features for Gait Recognition. IEEE Trans. PAMI, 2007
Students
08/28 (W) Manifold Learning N/A Instructor Review-2 Due for papers: 08/29, 09/03, 09/04
08/29 (R) Manifold Learning (1) Graph Embedding and Extensions: A General Framework for Dimensionality Reduction. IEEE Trans. PAMI, 2006
(2) Clustering and Dimensionality Reduction on Riemannian Manifolds, CVPR 2008
Students
4 09/03 (T) Manifold Learning (1) Adaptive Manifold Learning, IEEE Trans. PAMI, 2011
(2) Robust Multiple Manifolds Structure Learning, ICML 2012
Students
09/04 (W) Deep Auto-Encoders (1) Extracting and composing robust features with denoising autoencoders, ICML 2008 Instructor and Student Review-3 Due for papers: 09/05, 09/10, 09/11
09/05 (R) Deep Auto-Encoders (1) Contractive Auto-Encoders: Explicit Invariance During Feature Extraction, ICML 2011
(2) Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions, EMNLP 2011
Students
5 09/10 (T) Deep Auto-Encoders (1) k-Sparse Autoencoders, ICLR 2014
(2) Semantic Autoencoder for Zero-Shot Learning, CVPR 2017
Students
09/11 (W) Deep Auto-encoders (1) Auto-Encoding Variational Bayes, ICLR 2014
(2) Variational Autoencoders for Collaborative Filtering, WWW 2018
Students Review-4 Due for papers: 09/12, 09/17, 09/18
09/12 (R) Convolutional Networks (1) ImageNet Classification with Deep Convolutional Neural Networks, NIPS 2012
(2) Very deep convolutional networks for large-scale image recognition, ICLR 2015
Students
6 09/17 (T) Convolutional Networks (1) Going Deeper with Convolutions, CVPR 2015
(2) Deep Residual Learning for Image Recognition, CVPR 2016
Students
09/18 (W) Convolutional Networks/td> (1) Densely Connected Convolutional Networks, CVPR 2017
(2) Res2Net: A New Multi-scale Backbone Architecture, IEEE TPAMI, 2019
Students Review-5 Due for papers: 09/19, 09/24
09/19 (R) Convolutional Networks (1) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, NIPS 2015
(2) Mask R-CNN, ICCV 2017
Students
7 09/24 (T) Convolutional Networks (1) Learning Spatiotemporal Features with 3D Convolutional Networks, ICCV 2015
(2) Xnor-net: Imagenet classification using binary convolutional neural networks, ECCV 2016
Students
09/25 (W) Project Proposal Presentation (I) N/A Students
09/26 (R) Project Proposal Presentation (II) N/A Students
8 10/01 (T) Project Proposal Presentation (III) N/A Students
10/02 (W) Recurrent Networks Instructor Review-6 Due for papers: 10/03, 10/08, 10/09
10/03 (R) Recurrent Networks Students
9 10/08 (T) Recurrent Networks Students
10/09 (W) Recurrent Networks Students Review-7 Due for papers: 10/10, 10/15
10/10 (R) Recurrent Networks Students
10 10/15 (T) Recurrent Networks Students
10/16 (W) Adversarial Networks Instructor Review-8 Due for papers: 10/17, 10/22, 10/23, 10/24
10/17 (R) Adversarial Networks Students
11 10/22 (T) Adversarial Networks Students
10/23 (W) Adversarial Networks Students
10/24 (R) Adversarial Networks Students
12 10/29 (T) Project Progress Update Students
10/30 (W) Graph Neural Networks Instructor Review-9 Due for papers: 10/31, 11/05, 11/06
10/31 (R) Graph Neural Networks Students
13 11/05 (T) Graph Neural Networks Students
11/06 (W) Graph Neural Networks Students Review-10 Due for papers: 11/07, 11/12, 11/14, 11/19
11/07 (R) Graph Neural Networks Students
14 11/12 (T) Graph Neural Networks Students
11/13 (W) AutoML Instructor
11/14 (R) AutoML Students
15 11/19 (T) AutoML Students
11/20 (W) Course Project--Final Presentation (I) Students
11/21 (R) Course Project--Final Presentation (II) Students
16 11/26 (T) Course Project--Final Presentation (III) Students
11/27 (W) No Class; Thanksgiving Break
11/28 (R) No Class; Thanksgiving Break
17 12/03 (T) Course Review Instructor