Study Group

Date Chapter & Materials Presenter1 Presenter2 Presenter3
Sept. 12th 6. Deep Feedforward Networks
feedforward_network.pdf dl_lecture2_backpropagation.pdf
Zhang Hongyi Shao Han Chan Houpong
Sept. 19th 7. Regularization for Deep Learning deep_learning_chap7_slides_demo.rar Jiao Wenxiang Bai Haoli Li Jingjing
Sept. 26th 8. Optimization for Training Deep Models
optimization code
Yu Xiaotian Chen Xixian Su Yuxin
Oct. 3rd 9. Convolutional Networks
demo cnn_basic cnn_slzhao.pdf
Zhao Shenglin Liu Pengpeng Gao Yifan
Oct. 10th 10. Sequence Modeling: Recurrent and Recursive Nets
Chen Wang Zhang Jiani Wang Yue
Oct. 17th 14. Autoencoders
autoencoder_part1.pdf autoencoder_part2.pdf
He Shilin Li Jian Wu Weibin
Oct. 24th 15. Representation Learning Gao Yifan Chen Wang Jiao Wenxiang
Oct. 31st 16. Structured Probabilistic Models for Deep Learning Chan Houpong Wang Yue Shao Han
Nov. 7th 17. Monte Carlo Methods He Shilin Li Jian Wu Weibin
Nov. 14th 18. Confronting the Partition Function Chen Xixian Zhang Jiani Su Yuxin & Yu Xiaotian ppt
Nov. 21st 19. Approximate Inference Bai Haoli Li Jingjing Zhao Shenglin
Nov. 28th 20. Deep Generative Models Curto Joachim Zarza Irene Liu Pengpeng


Conference Deadline 2018

Conference Date Site Abstract Deadline Submission Deadline
WSDM2018 Feb. 5-9, 2018 Los Angeles, California, USA August 4, 2017 August 11, 2017
AAAI2018 Feb 4-10, 2018 New Orleans, Lousiana, USA September 8, 2017 September 11, 2017
SDM2018 May 3, 2018 - May 5, 2018 San Diego, USA Oct 6, 2017 Oct 13, 2017
The Web Conference 2018 23 – 27 April 2018 Lyon, France 26 October 2017 31 October 2017
CVPR2018 June 18th - June 22nd Salt Lake City, Utah, USA November 8, 2017 November 15, 2017
IJCNN2018 July 08-13, 2018 Rio de Janeiro, Brazil January 15, 2018
KDD2018 London
ICML2018 July 10 – July 15, 2018 Stockholm, SWEDEN

Research Group Presentation Schedule

Term 1, Fall 2017-18

  • Day: Monday
  • Time: 10:30 am - 12:30 pm
  • Venue: Room 1021, HSB Engineering Building

Date Topic Presenter 1 Presenter 2 Slides Other material
Sept. 11 Welcome
Sept. 18 Xiaotian Yu Yue Wang attention_is_all_you_need.pptx
Sept. 25 Wang Chen Hongyi Zhang Convolutional encoder & Conv Seq2seq
Oct. 2 National Day Holiday
Oct. 9 Jichuan Zeng Albert Li
Oct. 16 Yuxin Su Shenglin Zhao decentralized.pptx nfm.pdf
Oct. 23 Haoli Bai Han Shao deep_bayesian_10-23.rar
Oct. 30 Xixian Chen Pengpeng Liu
Nov. 6 Ken Chan Wenxiang Jiao
Nov. 13 Jingjing Li Jiani Zhang
Nov. 20 Yifan Gao Weibin Wu
Nov. 27 Joachim Curto Irene Zarza

Term 2, Spring 2016-17

  • Day: Monday
  • Time: 3:30 pm - 5:00 pm
  • Venue: Room 1021, HSB Engineering Building

Date Topic Presenter 1 Presenter 2 Slides Other material
Jan. 9 Welcome
Jan. 16 Xiaotian Yu puzzle.pdf
Jan. 23 Tong Zhao
Jan. 30 Chinese New Year
Feb. 6 Xixian Chen
Feb. 13 Yue Wang slidespaper
Feb. 20 Wang Chen slides paper Words&Puzzle
Feb. 27 Hongyi Zhang slides paper Words&Puzzle
Mar. 6 Albert Li
Mar. 13
Mar. 20 Jiani Zhang norm.pptx puzzle_0320.pdf
Mar. 27 Han Shao Jiani Zhang
Apr. 3 Yuxin Su Asynchronous Distributed FW 20 wordsPuzzle
Apr. 10 Jichuan Zeng
Apr. 17 Ken Chan
Apr. 24 Pengpeng Liu

Term 1, Autumn 2016-17

  • Day: Tuesday
  • Time: 2:30 pm - 4:00 pm
  • Venue: Room 1021, HSH Engineering Building

Date Topic Presenter I Presenter II Slides Other material
Sep. 6 Welcome Xiaotian Yu
Sep. 13
Sep. 20 Deep Learning I Han Shao
Sep. 28 Deep Learning II Yue Wang dl-rnn1.pptx ELEG5040 Notes
RNN Tutorial
LSTM Tutorial
Oct. 4 Deep Learning III Jiani Zhang memory_networks.pptx
Oct. 11 Deep Learning IV Wang Chen Albert Li
Oct. 18 Deep Learning V Shenglin Zhao Hongyi Zhang introduction2drl.pdf alphago_ijcai.pdf
Oct. 25 Xixian Chen Jichuan Zeng attention_in_nlp.pptx
Nov. 1 Yuxin Su Tong Zhao deep_metric_learning.pdf
Nov. 8 Ken Chan Online Learning in Crowdsourcing
Nov. 15
Nov. 22

Term 2, Spring 2015-16

  • Day: Monday
  • Time: 2:30 pm - 3:30 pm
  • Venue: Room 1021, HSH Engineering Building

Date Presenter Topic Slides Other material
Jan. 11 Welcome
Jan. 18 Xiaotian Yu
Jan. 25 Tong Zhao
Feb. 1 Yuxin Su
Feb. 22 Haiqin Yang Fast Convergence of Regularized Learning in Games Slide
Feb. 29 Jiani Zhang
Mar. 7 Hongyi Zhang Community Detection Using Time-Dependent Personalized PageRank Slide
Mar. 14 Ken Chan online-rank-elicitation-for-plackett-luce-a-dueling-bandits-approach slide words Paragraph Puzzle
Mar. 21 Shenglin Zhao QuickScorer: a fast algorithm to rank documents with additive ensembles of regression trees sigir15bestpaperslides.pdf question_and_word.docx
Apr. 11 Xixian Chen
Apr. 18 Jichuan Zeng

Term 1, Autumn 2015-16

  • Day: Tuesday
  • Time: 1:30 pm - 2:30 pm
  • Venue: Room 1022, HSH Engineering Building

Date Topic Presenter Slides Other material
Sep. 7 Welcome
Sep. 14 Yuxin
Sep. 21
Sep. 29 Online Influence Maximization Tong slides
Oct. 6 Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach Hongyi slides
programming problem
Oct. 13 Xixian
Oct. 20 Rank-GeoFM Shenglin paper
Oct. 27 Xiaotian
Nov. 3
Nov. 10 Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows Ken Slides
Nov. 17 Jenny Slides
Nov. 24 Jichuan Slides
Puzzle Words
Dec. 1

Term 2, Spring 2014-15

  • Day: Wednesday
  • Time: 3:30 pm - 4:30 pm
  • Venue: Room 1022, HSH Engineering Building

Date Topic Presenter #1 Presenter #2 Notes
Jan. 14
Jan. 21 Zach
Jan. 28 Communication Limits
Feb. 4 kdd2014 Tong
Feb. 11 LUPI slide
LUPI Paper
Structure SVM With PI
Feb. 18 Public Holiday
Feb. 25
Mar. 4
Mar. 11 nomad slide paper JJ
Mar. 18 Overlapping Community Detection Using Seed Set Expansion (slides) Hongyi
Mar. 25 Xixian
Apr. 1 Presentation
A cost-effective recommender system for taxi drivers
Apr. 8 slides sigir14.pdf Shenglin
Apr. 15

Term 1, Fall 2014

  • Day: Tuesday
  • Time: 2:00 pm - 3:30 pm
  • Venue: Room 1022, HSH Engineering Building

Date Topic Presenter #1 Presenter #2 Notes
Aug. 19 Shouyuan
Aug. 26 Haiqin
Sep. 2 Yuxin
Sep. 9 Holiday
Sep. 16 Zach
ppt paper
Sep. 23 Tong
paper paper
Sep. 30 JJ
slides paper
Oct. 7 RecSys 2014
Oct. 28 Xixian
Nov. 4 CIKM 2014/ICONIP 2014
Nov. 11 Robbie
Nov. 18 Shenglin
slides paper
Nov. 25 Hongyi
slides paper
Dec. 2 Sophia
slide paper

Term 2, Spring 2014

  • Day: Monday
  • Time: 2:00 pm - 3:30 pm
  • Venue: Room 1027, HSH Engineering Building

Date Topic Presenter #1 Presenter #2 Notes
Jan. 13 Individual briefing and Logistics
Jan. 20 Yuxin
Jan. 27 Shouyuan
Feb. 3 Chinese New Year
Feb. 10 Tong
Feb. 17 Haiqin
Second order perceptron
Feb. 24 Zach
Mar. 3 JJ
Safe Screening for SVM
Mar. 10 Xixian
Mar. 17 Robbie
slides paper
Mar. 24 Negin Jamie
Mar. 31 Shenglin
slides paper
Apr. 7 Hongyi
slides paper
Apr. 14 Shuai Yuanyuan
Apr. 21 Easter Holiday
Apr. 28

Term 1, Fall 2013

  • Day: Monday
  • Time: 1:00 pm - 2:30 pm
  • Venue: Room 1022, HSH Engineering Building

Date Topic Presenter #1 Presenter #2 Notes
Sept. 9 Individual briefing and Logistics
Sept. 16 Tong JJ
Sept. 23 Hang Xixian
Sept. 30 Haiqin
Workshop Summary
Scalable Approximation of Kernel Fuzzy c-Means
Luo Chen
Oct. 7 IEEE Big Data
Oct. 14 Public Holiday
Oct. 21 Zach
Oct. 28 Shouyuan
Nov. 4 Conference
Nov. 11 Robbie
Nov. 18 Baichuan
Nov. 25 Shenglin
Dec. 2 Hongyi
WWW'13 Best Paper
Dec. 9 NIPS
Dec. 16
Dec. 23 Holiday

Big Data



Conferences and Workshops



  • Big Data@CSAIL, MIT, 23 nodes (projects), e.g., linked data, computer vision, natural language, machine learning, social, etc.
  • NASA tournament lab, big data challenge, Apply the process of open innovation to conceptualizing new and novel approaches to using “big data” information sets from various U.S. government agencies, e.g., health, energy and earth science.
  • Fastlab, Georgia Tech, make all textbook state-of-the-art machine learning methods computationally tractable on big datasets.
  • Noah'S ark lab, HK, their goal is to push the frontier of our understanding on how knowledge can be learned and intelligence can be realized from the Big Data.
  • Select Lab, CMU, the techniques they develop encompass a wide range of topics, including probabilistic graphical models, active learning and value of information, distributed algorithms, probabilistic inference, decision making under uncertainty, online learning and game theory.




  • Scaling Up Machine Learning-Parallel and Distributed Approaches, State-of-the-art platforms and algorithm choices, Hardware options (from FPGAs and GPUs to multi-core systems and commodity clusters), Programming frameworks (including CUDA, MPI, MapReduce, and DryadLINQ), Learning settings (e.g., semi-supervised and online learning), Example-driven, covering a number of popular algorithms (e.g., boosted trees, spectral clustering, belief propagation) and diverse applications (e.g., speech recognition and object recognition in vision)
  • Faster Learning for Massive Datasets by Alex Gray, The presentation describes new approaches for online learning and stochastic programming, which achieve both tighter theoretical bounds across the board and significant empirical gains over state-of-the-art approaches including stochastic gradient descent and mirror descent. It also presents a scheme for distributed online learning exhibiting first-of-a-kind theoretical and empirical gains. For nonlinear kernelized methods, kernel matrix multiplications and summations become a bottleneck. It shows fast algorithms which provably reduce computation times from quadratic to linear time, with corresponding empirical runtime results, demonstrated on over 10,000 cores.
  • Big Data Analytics: Applications and Opportunities in On-line Predictive Modeling, In this talk I will cover some of the basics in terms of infrastructure and design considerations for effective an efficient BigData. In many organizations, the lack of consideration of effective infrastructure and data management leads to unnecessarily expensive systems for which the benefits are insufficient to justify the costs. We will then pay specific attention to on-line data and the unique challenges and opportunities represented there. We cover examples of Predictive Analytics over Big Data with case studies in eCommerce Marketing, on-line publishing and recommendation systems, and advertising targeting: Special focus will be placed on the analysis of on-line data with applications in Search, Search Marketing, and targeting of advertising. We conclude with some technical challenges as well as the solutions that can be used to these challenges in social network data.

Blog and News Feed


  • IBM Watson deep Q&A, evidence-based decision support, deep question answering, Jeopardy!, Health care
  • Needleseek, mine open-domain semantic knowledge from web-scale data sources and answer user requests based on the mined semantic knowledge



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