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 |