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AISys Reading Group
Welcome to AISys reading group!
The paper reading group meets weekly during the Fall, Spring, and Summer semesters to discuss papers in artificial intelligence, machine learning, systems, and software engineering. Participation is open to all, guests are always welcome; if you are interested in receiving invitations contact the organizer.
Each week we will discuss a different paper. The paper to discuss is announced about one week in advance by the organizer. All participants are expected to read the paper before the meeting. It is recommended to take notes about insights, questions, and other points potentially worth discussing.
The goals of the reading group are:
- Critical reflection on scientific work
- Practice reading and argumentation strategies
- Exposure to a broad range of research topics
- Practice leading group discussions
We follow these simple rules:
- Please read the paper before the meeting; set a 1-2h reminder on your schedule
- Engage in the discussions (ask questions, answer questions, discuss insights, discuss writing style)
- Volunteer for moderating paper
- A 2-min feedback form after each session
Guideline for selecting papers:
- Make sure you know the interests of the group, look at previous papers, and select a paper that lies in the interests of the group
- Make sure the paper is solid with a very high quality preferably published on top venues but could be a preprint from a top researcher which is not published yet; or classic papers in AI/ML(examples).
- Do not select your own paper or by an author from the group. We only read papers from others.
- If you are not sure about the quality or relevance of a paper, please feel free to contact the organizer.
- Preferably select a paper in your area and you are confident that you can understand a good part of the paper and can discuss it in depth.
- You do not necessarily need to prepare slides for your presentation, I would suggest walking through the paper, but if you prefer to prepare slides, that is also fine.
Topics of Interests:
- Causal AI: e.g., Causal Representation Learning, Causal Inference, Structure Learning
- Optimization: e.g., Convex, non-Convex, multi-objective, blackbox, active learning
- Systems: Computer Architecture, Distributed Systems, Serverless, Systems for ML, ML for Systems, AI/ML Hardware, Autonomous Systems
- Robotics: Robot Learning
- Cognitive Psychology: Child Learning, anything related to AI/ML
- AI Systems: Fairness, Trusts, Bias
- Classical AI: Search, Planning
- Reinforcement Learning
- Deep Learning: Theory, Architecture
- Theory of ML
- Security of ML: Adversarial ML
- Software Engineering
Good Resources:
- Arxiv Sanity: a great resource for finding the most relevant papers published on Arxiv.
- Arxiv Vanity: Arxiv Vanity renders Arxiv PDFs in a mobile-friendly HTML format.
- Depth First Learning: Typically tackling papers that require more background knowledge, DFL is a great resource for very high-quality explanations of ML research concepts.
- Distill.pub: Typically tackling papers that require more background knowledge, DFL is a great resource for very high-quality blogs and explanations of ML research concepts.
- Berkeley AI Research Blog: A great resource for high-quality blogs written by Berkeley ML researchers.
- r/MachineLearning: Reddit community for discussions on ML.
The discussion is limited to 60-90 min. The discussion is lead by a moderator, who may also set a focus for the discussion. The moderator will kick off the meeting by giving a summary of the paper and raising a few points for discussion. The moderator should try to incorporate all participants into the discussion. The moderator role rotates through all participants. The moderator is encouraged to help with the selection of a paper that week.
**Time and location: Friday 11:00 AM (EST)
- Organizer: Pooyan Jamshidi ([email protected])
- Contact person: Fatemeh ([email protected])
Subscribe for announcements:
-
mailing list:
[email protected]by sending an email to the organizer.
Slack for the reading group (join to communicate with the group members):
'Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks'
Moderator: Abhinav; email: [email protected]
'Multiple Randomization Designs'
Moderator: Morteza; email: [email protected]
'Dynamic Causal Bayesian Optimization'
Moderator: Shahriar; email: [email protected]
'Causal Markov Decision Processes: Learning Good Interventions
Efficiently'
Moderator: Sonam; email: [email protected]
Brainstorming and Discussion
Moderator: Pooyan; email: [email protected]
'Rethinking Co-design of Neural Architectures and Hardware Accelerators
'
Moderator: Elaheh; email: [email protected]
'Enchanted Determinism: Power without Responsibility in Artificial Intelligence
'
Moderator: Gartley; email: [email protected]
'Causal Navigation by
Continuous-time Neural Networks
'
Moderator: Morteza; email: [email protected]
'Using Self-Supervised Learning Can Improve Model
Robustness and Uncertainty
'
Moderator: Fatemeh; email: [email protected]
'Neurips 2021 discussion: All to read: *** Key Papers ***
Awards: https://blog.neurips.cc/2021/11/30/announcing-the-neurips-2021-award-recipients/
A summary post-conference:'
Moderators: Biplav and Pooyan; emails: [email protected], [email protected]
'Explainable Reinforcement Learning through a Causal Lens'
Moderator: Jianhai; email: [email protected]
'Contrastive Learning with Adversarial Examples'
Moderator: Fatemeh; email: [email protected]
'TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning'
Moderator: Nasrin; email: [email protected]
'Chameleon: Adaptive code optimization for expedited deep neural network compilation'
Moderator: Hossein; email: [email protected]
'Generalizable Machine Learning in Neuroscience Using Graph Neural Networks'
Moderator: Chrisogonas; email: [email protected]
'Where and What? Examining Interpretable Disentangled Representations'
Moderator: Zahra; email: [email protected]
'Argus: Debugging Performance Issues in Modern Desktop Applications with Annotated Causal Tracing'
Moderator: Shahriar; email: [email protected]
'Neural Bootstrapper'
Moderator: Minsuk Shin; email: [email protected]
'Accelerating Reinforcement Learning with Learned Skill Priors'
Moderator: Jianhai; email: [email protected]
'A Simple Framework for Contrastive Learning of Visual Representations'
Moderator: Fatemeh; email: [email protected]
'Desiderata for Representation Learning: A Causal Perspective'
Moderator: Mohsen Rezapour; email: [email protected]
'Desiderata for Representation Learning: A Causal Perspective'
Moderator: Mohsen Rezapour; email: [email protected]
'Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models'
Moderator: Kimia; email: [email protected]
'Vision-based estimation of driving energy for planetary rovers using deep learning and terram'
Shoya Higa , Yumi Iwashita , Kyohei Otsu , Masahiro Ono, Olivier Lamarre, Annie Didier, and Mark Hoffmann
Moderator: Abir; email: [email protected]
'Neural Network Attributions: A Causal Perspective'
Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N Balasubramanian
Moderator: Mahdi; email: [email protected]
'Pretrained Transformers as Universal Computation Engines'
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
Moderator: Kallol & Modar; email: [email protected]
'The Causal-Neural Connection: Expressiveness, Learnability, and Inference'
Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim
Moderator: Mohammad, Pooyan, Mahdi; email: [email protected]
'Decision Transformer: Reinforcement Learning via Sequence Modeling'
Moderator: Bharat; email: [email protected]
'The Causal-Neural Connection: Expressiveness, Learnability, and Inference'
Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim
*Moderator: Mohammad, Pooyan, Mahdi; email: [email protected]
'DEEP LEARNING FOR SYMBOLIC MATHEMATICS'
Moderator: Kimia; email: [email protected]
'Adversarial Robustness through the Lens of Causality'
Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, and Kun Zhang
Moderator: Fatemeh; email: [email protected]
How to read a paper
Moderator: Pooyan; email: [email protected]
'Relational Reinforcement Learning'
Moderator: Forest; email: [email protected]
'Discussion regarding the group reading'
'Recording Video'
Moderator: Pooyan; email: [email protected]
'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks'
'slides'
Patrick Lewis, Ethan Perez,Aleksandra Piktus, et al.
Moderator: Manas Gaur; email: [email protected]
'Towards causal generative scene models via competition of experts'
Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler, Matthias Bethge, Bernhard Schölkopf
Moderator: Hamed; email: [email protected]
'Representing Partial Programs with Blended Abstract Semantics'
Maxwell Nye, Yewen Pu, Matthew Bowers, Jacob Andreas, Joshua B. Tenenbaum, Armando Solar-Lezama
Moderator: Jianhai; email: [email protected]
'CAUSAL DISCOVERY WITH REINFORCEMENT LEARNING'
Shengyu Zhu, Ignavier Ng, Zhitang Chen
Moderator: Musfiq; email: [email protected]
'Learning Independent Causal Mechanisms'
Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Scholkopf
Moderator: Pooyan; email: [email protected]
'Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence'
Atoosa Kasirzadeh
Moderator: Marilyn; email: [email protected]
'Representation Learning via Invariant Causal Mechanisms; 2020'
Moderator: Pooyan; email: [email protected]
"Total Nitrogen Estimation in Agricultural Soils via Aerial Multispectral Imaging and LIBS"
Moderator: Abir; email: [email protected]
"Heart rate variability: An index of brain processing in vegetative state? An artificial intelligence, data mining study"
Moderator: Koppel; email: [email protected]
"Causal inference by using invariant prediction: identification and confidence intervals"
Moderator: Mohammad Ali Javidian; email: [email protected]
"Markov Chain Monte Carlo and Variational Inference: Bridging the Gap"
Tim Salimans, Algoritmica, Diederik P. Kingma, and Max Welling
Moderator: Kimia; email: [email protected]
##Cancelled
“Improving Adversarial Robustness Requires Revisiting Misclassified Examples”
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu
Moderator: Ying; email: [email protected]
"Approximating discrete probability distributions with dependence trees"
C.K.Chow, C.N.Liu
Moderator: Fatemeh; email: [email protected]
"On the Relationship between Sum-Product Networks and Bayesian Networks"
Han Zhao, Mazen Melibari,Pascal Poupart
Moderator: Shahriar; email: [email protected]
"Deep Reinforcement Learning with Dynamic Optimism"
Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel
Moderator: Qi Zhang; email: [email protected]
"Learning the Parameters of Bayesian Networks from Uncertain Data"
Segev Wasserkrug, Radu Marinescu, Sergey Zeltyn, Evgeny Shindin, Yishai Feldman
Moderator: Marco; email: [email protected]
"Drug discovery with explainable artificial intelligence"
José Jiménez-Luna, Francesca Grisoni, and Gisbert Schneider
Moderator: Forest; email: [email protected]
"Entropic Causal Inference"
Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath, Babak Hassibi
Moderator: Mohammad Ali Javidian
"How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods"
Jeya Vikranth Jeyakumar, Joseph Noor, Yu-Hsi Cheng, Luis Garcia, Mani Srivastava
Moderator: Biplav Srivastava
"Reconsidering Generative Objectives For Counterfactual Reasoning"
Danni Lu, Chenyang Tao, Junya Chen, Fan Li, Feng Guo, Lawrence Carin
Moderator: Shuge Lei
"Underspecification Presents Challenges for Credibility in Modern Machine Learning"
Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley
Moderator: Bharat
"Neural Adaptive Video Streaming with Pensieve"
Hongzi Mao, Ravi Netravali, Mohammad Alizadeh
Moderator: Nawras
"Theoretically Principled Trade-off between Robustness and Accuracy"
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan
Talk
Moderator: Ying
"Value-Decomposition Networks For Cooperative Multi-Agent Learning"
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, Thore Graepel
Moderator: Nawras
Happy New Year! 🎆
Merry Christmas! 🎅 🎄
"Learning Differentiable Programs with Admissible Neural Heuristics"
Ameesh Shah, Eric Zhan, Jennifer J. Sun, Abhinav Verma, Yisong Yue, Swarat Chaudhuri
Moderator: Forest
"On Sampled Metrics for Item Recommendation"
Walid Krichene, Steffen Rendle
Moderator: Shuge
"Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data"
Lisa Anne Hendricks, Subhashini Venugopalan, Marcus Rohrbach, Raymond Mooney, Kate Saenko, and Trevor Darrell
Moderator: Kimia
Happy Thanksgiving! 🦃
"LAMBDANETWORKS: MODELING LONG-RANGE INTERACTIONS WITHOUT ATTENTION" (Continue)
code
additional materials: material 1, material 2
Paper under double-blind review (Under review as a conference paper at ICLR 2021)
Moderator: Bharat
"LAMBDANETWORKS: MODELING LONG-RANGE INTERACTIONS WITHOUT ATTENTION"
code
additional materials: material 1, material 2
Paper under double-blind review (Under review as a conference paper at ICLR 2021)
Moderator: Bharat
"Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting"
Xingjian Shi, Zhourong Chen, Hao Wang, and Dit-Yan Yeung
Moderator: Shuge
"Law and Adversarial Machine Learning"
Ram Shankar Siva Kumar, David R. O'Brien, Kendra Albert, Salome Vilojen.
Moderator: Marilyn
"YOLACT: Real-time Instance Segmentation"
Daniel Bolya, Chong Zhou, Fanyi Xiao, Yong Jae Lee.
Moderator: Rabab
"Causal Bayesian Optimization"
Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González.
Moderator: Shahriar
"Symbolic pregression: Discovering Physical Laws from Raw Distorted Video."
Silviu-Marian Udrescu, Max Tegmark
Moderator: Kimia
"Deep Flow-Guided Video Inpainting"
Rui Xu, Xiaoxiao Li, Bolei Zhou, Chen Change Loy.
Moderator: Shuge
"Towards Robust Neural Networks via Random Self-ensemble"
Xuanqing Liu, Minhao Cheng, Huan Zhang, and Cho-Jui Hsieh.
Moderator: Ying
"DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning" (part 4)
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum.
Moderator: Pooyan
"DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning" (part 3)
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum.
Moderator: Pooyan
"Perspective Plane Program Induction from a Single Image"
Yikai Li, Jiayuan Mao, Xiuming Zhang, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu.
Moderator: Bharat
"DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning" (part 2)
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum.
Moderator: Pooyan
The Computational Limits of Deep Learning
Neil C. Thompson, Kristjan Greenewald, Keeheon Lee, Gabriel F. Manso.
Moderator: Biplav
"DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning" (part 1)
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum.
Moderator: Pooyan
"Improving Adversarial Robustness via Promoting Ensemble Diversity"
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu.
Moderator: Ying
"Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider"
Mohammad Shahrad, Rodrigo Fonseca, Íñigo Goiri, Gohar Chaudhry, Paul Batum, Jason Cooke, Eduardo Laureano, Colby Tresness, Mark Russinovich, and Ricardo Bianchini.
Moderator: Shahriar
"Spatially Transformed Adversarial Examples"
Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, Dawn Song.
Moderator: Fatemeh
"A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION"
PADIDEH DANAEE, REZA GHAEIN.
Moderator: MAHMUDUL
"Human-level concept learning through probabilistic program induction"
Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum
Moderator: Jianhai
"Taskonomy: Disentangling Task Transfer Learning"
Amir Zamir, Alexander Sax, William Shen, Leonidas Guibas, Jitendra Malik, Silvio Savarese
Project Page: http://taskonomy.stanford.edu/
Moderator: Bharat
"Scalable Neural Bootstrapper"
Minsuk Shin, Hyungjoo Cho, Sungbin Lim
Moderator: Minsuk
“Concept Learning in Deep Reinforcement Learning”
Diego Gomez, Nicanor Quijano, Luis Felipe Giraldo
Moderator: Nawras
"Shortcut Learning in Deep Neural Networks"
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
Moderator: Ying
"Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions"
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
Moderator: Mohammad
"Transfer in Deep Reinforcement Learning Using Successor Features and Generalized Policy Improvement"
Andre Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Zidek, Remi Munos
Moderator: Jianhai
“Sympson’s Paradox in Covid-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects”
Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf
Moderator: Marco
"Counterfactuals and Their Applications" - chapter 4 of the book, “Causal Inference in Statistics: A Primer”
Judea Pearl, Madelyn Glymour and Nicholas Jewel
Moderator: Mohammad
“Successor Features for Transfer in Reinforcement Learning”
André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, Hado van Hasselt, David Silver
Moderator: Pooyan
"Deep Learning: Chapter 16.3 onwards" MIT press, 2016.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Mohammad
"Deep Learning: Chapter 16.1 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Mohammad
“On Learning Invariant Representations for Domain Adaptation”(Continue)
Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon
Moderator: Jianhai
“On Learning Invariant Representations for Domain Adaptation”
Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon
Moderator: Jianhai
“Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations”
Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen
Moderator: Ying
"The Three Pillars of Machine Programming"
Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, et al
Moderator: Pooyan
“Invariant Risk Minimization”(Continue)
Martin Arjovsky, L´eon Bottou, Ishaan Gulrajani, David Lopez-Paz
Moderator: Mohammad
“Invariant Risk Minimization”
Martin Arjovsky, L´eon Bottou, Ishaan Gulrajani, David Lopez-Paz
Moderator: Mohammad
“A Programmable Approach to Model Compression”
Vinu Joseph, Saurav Muralidharan, Animesh Garg, Michael Garland, Ganesh Gopalakrishnan
Moderator: Shahriar
"Deep Learning: Chapter 12, section 1.4 to section 1.6" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Jianhai
"Deep Learning: Chapter 12, section 1.1 to section 1.3" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Bharat
"Deep Learning: Chapter 11.5 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Bharat
"Deep Learning: Chapter 11 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Shahriar
"Deep Learning: Chapter 9.10 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Ying
"Deep Learning: Chapter 9.6 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Jianhai
"Deep Learning: Chapter 9.4 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Shahriar
"Deep Learning: Chapter 9 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Bharat
"Deep Learning: Section 8.7 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Shahriar
"Deep Learning: Section 8.6 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Jianhai
"Deep Learning: Section 8.4 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Shahriar
"Deep Learning: Section 8.3 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Ahmed
"Deep Learning: Section 7.9 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Chris
"Deep Learning: Section 7.6 onwards (Semi-supervised, multi-task learning, early stopping)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Dr. Jamshidi
"Deep Learning: chapter 7.2 - 7.5" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Shahriar
"Deep Learning: chapter 7" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Ahmed
"Deep Learning: chapter 6" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Ying
"Deep Learning: chapter 6" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Ying
"Deep Learning: chapter 6" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Ying
"Deep Learning: chapter 5 (ML basics)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Joshua
"Deep Learning: chapter 5 (ML basics)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Nawras
"Deep Learning: chapter 5 (ML basics)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Chrisogonas
"Deep Learning: chapter 5 (ML basics)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Chrisogonas
"Deep Learning: chapter 3 (Probability and Information Theory)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Shahriar
"Deep Learning: chapter 2 (Linear Algebra)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Jianhai
"Learning Bayesian networks: chapter 8 (Sections 8.1-8.2)"
Neapolitan, Richard E
Moderator: Marco
"Modeling concept drift: A probabilistic graphical model based approach"
In International Symposium on Intelligent Data Analysis, pp. 72-83. Springer, Cham, 2015
Borchani, Hanen, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón et al
Moderator: Emad
"Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach"
In The 22nd International Conference on Artificial Intelligence and Statistics (AIStats), pp. 2096-2105. 2019
Sen, Rajat, Kirthevasan Kandasamy, and Sanjay Shakkottai
Moderator: Jianhai
"Hybrid reward architecture for reinforcement learning"
In Advances in Neural Information Processing Systems, pp. 5392-5402. 2017
Van Seijen, Harm, Mehdi Fatemi, Joshua Romoff, Romain Laroche, Tavian Barnes, and Jeffrey Tsang
Moderator: Yuxiang
"Evaluating the Search Phase of Neural Architecture Search"
Sciuto, Christian, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann
Moderator: Rui
"Guiding deep learning system testing using surprise adequacy"
Proceedings of the 41th International Conference on Software Engineering (ICSE), 2019
Kim, Jinhan, Robert Feldt, and Shin Yoo
Moderator: Ying
"The seven tools of causal inference, with reflections on machine learning"
Communications of the ACM 62, no. 3 (2019): 54-60
Pearl, Judea
Moderator: Marco Valtorta
"Designing neural network architectures using reinforcement learning"
International Conference on Learning Representations (ICLR), 2017
Baker, Bowen, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar
Moderator: Jianhai Su
"Dynamic gpgpu power management using adaptive model predictive control"
In 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 613-624. IEEE, 2017
Majumdar, Abhinandan, Leonardo Piga, Indrani Paul, Joseph L. Greathouse, Wei Huang, and David H. Albonesi
Moderator: Shahriar
"The Art of Getting Deep Neural Networks in Shape"
ACM Transactions on Architecture and Code Optimization (TACO) 15, no. 4 (2019): 62
Mammadli, Rahim, Felix Wolf, and Ali Jannesari
Moderator: RUI XIN
"PerfLearner: learning from bug reports to understand and generate performance test frames"
In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, pp. 17-28. ACM, 2018
Han, Xue, Tingting Yu, and David Lo
Moderator: Yang Ren
"Predictive Test Selection"
Machalica, Mateusz, Alex Samylkin, Meredith Porth, and Satish Chandra
Moderator: Alireza
"Curriculum learning of Bayesian network structures"
In Asian Conference on Machine Learning, pp. 269-284. 2016
Zhao, Yanpeng, Yetian Chen, Kewei Tu, and Jin Tian
Moderator: Mohammad Ali Javidian
"Performance Analysis of Cloud Applications"
In 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 18). USENIX Association, 2018
Ardelean, Dan, Amer Diwan, and Chandra Erdman
Moderator: Jianhai Su
"Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents"
Journal of Artificial Intelligence Research 61 (2018): 523-562
Machado, Marlos C., Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling
Moderator: Yuxiang Sun
"Massively Parallel Hyperparameter Tuning"
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Human-level intelligence or animal-like abilities?\ Commun. ACM 61, 10 (September 2018), 56-67
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"Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics"
In NSDI, pp. 363-378. 2016
Venkataraman, Shivaram, Zongheng Yang, Michael J. Franklin, Benjamin Recht, and Ion Stoica
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Negrinho, Renato, and Geoff Gordon
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Pei, Kexin, Yinzhi Cao, Junfeng Yang, and Suman Jana
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"Autonomous skill-centric testing using deep learning"
In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 95-102. IEEE, 2017
Hangl, Simon, Sebastian Stabinger, and Justus Piater
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Blasi, Arianna, Alberto Goffi, Konstantin Kuznetsov, Alessandra Gorla, Michael D. Ernst, Mauro Pezzè, and Sergio Delgado Castellanos
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"The care and feeding of wild-caught mutants"
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Brown, David Bingham, Michael Vaughn, Ben Liblit, and Thomas Reps
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Mishra, Nikita, Connor Imes, John D. Lafferty, and Henry Hoffmann
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