Retrospectives: A Venue for Self-Reflection in ML Research
Ryan Lowe · Yoshua Bengio · Joelle Pineau · Michela Paganini · Jessica Forde · Shagun Sodhani · Abhishek Gupta · Joel Lehman · Peter Henderson · Kanika Madan

Fri Dec 13th 08:00 AM -- 06:00 PM @ West 114 + 115
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The NeurIPS Workshop on Retrospectives in Machine Learning will kick-start the exploration of a new kind of scientific publication, called retrospectives. The purpose of a retrospective is to answer the question:

“What should readers of this paper know now, that is not in the original publication?”

Retrospectives provide a venue for authors to reflect on their previous publications, to talk about how their intuitions have changed, to identify shortcomings in their analysis or results, and to discuss resulting extensions that may not be sufficient for a full follow-up paper. A retrospective is written about a single paper, by that paper's author, and takes the form of an informal paper. The overarching goal of retrospectives is to improve the science, openness, and accessibility of the machine learning field, by widening what is publishable and helping to identifying opportunities for improvement. Retrospectives will also give researchers and practitioners who are unable to attend top conferences access to the author’s updated understanding of their work, which would otherwise only be accessible to their immediate circle.

09:00 AM Opening Remarks (Opening remarks)
09:15 AM Invited talk #1 (Talk)
09:35 AM Invited talk #2 (Talk)
09:55 AM Invited talk #3 (Talk)
10:15 AM Coffee break + poster set-up (break)
10:30 AM Invited talk #4 (Talk)
10:50 AM Panel discussing how to increase transparency and dissemination of ‘soft knowledge’ in ML (Panel)
12:00 PM Lunch break (Break)
01:50 PM Retrospective: An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution (Talk) Rosanne Liu
01:55 PM Retrospective: Learning the structure of deep sparse graphical models (Talk) Zoubin Ghahramani
02:00 PM Retrospective: Lessons Learned from The Lottery Ticket Hypothesis (Talk) Jonathan Frankle
02:05 PM Retrospective: FiLM: Visual Reasoning with a General Conditioning Layer (Talk)
02:10 PM Retrospective: Deep Ptych: Subsampled Fourier Ptychography via Generative Priors (Talk)
02:15 PM Retrospective: Markov games that people play (Talk) Michael Littman
02:20 PM Retrospective: DLPaper2Code: Auto-Generation of Code from Deep Learning Research Papers (Talk) Anush Sankaran
02:25 PM Retrospective; Deep Reinforcement Learning That Matters (Talk) Riashat Islam
02:30 PM Smarter prototyping for neural learning (Talk) Prabhu Pradhan
02:35 PM Advances in deep learning for skin cancer detection (Talk)
02:40 PM Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization (Talk) Jürgen Schmidhuber
02:45 PM Posters + Coffee Break (Posters)
04:10 PM Invited talk #5 (Talk)
04:30 PM Retrospectives brainstorming session: how do we produce impact? (Structured group brainstorming)

Author Information

Ryan Lowe (McGill University / OpenAI)
Yoshua Bengio (Mila)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.

Joelle Pineau (McGill University)

Joelle Pineau is an Associate Professor and William Dawson Scholar at McGill University where she co-directs the Reasoning and Learning Lab. She also leads the Facebook AI Research lab in Montreal, Canada. She holds a BASc in Engineering from the University of Waterloo, and an MSc and PhD in Robotics from Carnegie Mellon University. Dr. Pineau's research focuses on developing new models and algorithms for planning and learning in complex partially-observable domains. She also works on applying these algorithms to complex problems in robotics, health care, games and conversational agents. She serves on the editorial board of the Journal of Artificial Intelligence Research and the Journal of Machine Learning Research and is currently President of the International Machine Learning Society. She is a recipient of NSERC's E.W.R. Steacie Memorial Fellowship (2018), a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Senior Fellow of the Canadian Institute for Advanced Research (CIFAR) and in 2016 was named a member of the College of New Scholars, Artists and Scientists by the Royal Society of Canada.

Michela Paganini (Facebook AI Research)
Jessica Forde (Brown University)
Shagun Sodhani (MILA, University of Montreal)
Abhishek Gupta (Microsoft)
Joel Lehman (Uber AI)
Peter Henderson (McGill University)
Kanika Madan (University of Toronto)

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