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MIML talk, Matt Jacobs (UCLA) 11am Tuesday: acceleration of primal-dual algorithm with application to Wasserstein distance
by Adam Oberman 26 Nov '18

26 Nov '18

Hi, Please post the following talk, which may be of interest to the Lisa tea talk group: Matt Jacobs from UCLA will speak Tuesday (tomorrow) at 11:00am in Burnside 1214. (Burnside is the McGill mathematics building, located near the corner of University and Sherbrooke). https://dms.umontreal.ca/~mathapp/machinelearning/MattJacobs.html <https://dms.umontreal.ca/~mathapp/machinelearning/MattJacobs.html> He is speaking on a faster optimization algorithm applied to total variation denoising and the Wasserstein distance Please join for a (non-hosted) lunch after the seminar. Adam Adam Oberman | Professor | Department of Mathematics | McGill University | oberman.adam(a)gmail.com | http://www.adamoberman.net/

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[Tea Talk] Sarath Chander (Mila + brain) Fri November 23 2018 10:30 AA3195
by rim.assouel＠gmail.com 23 Nov '18

23 Nov '18

This week we have Sarath Chander from Mila + brain giving a talk on Fri November 23 2018 at 10:30 in room AA3195 Will this talk be streamed <https://mila.bluejeans.com/4255239897/webrtc>? Yes Recorded? Yes Likely To Deceive : short on puns this week (*meta-pun intended*) See you there! Rim and Sai TITLE RNNs, Long-term Dependencies, and Lifelong Learning ABSTRACT Part 1: Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies Modelling long-term dependencies is a challenge for recurrent neural networks. This is primarily due to the fact that gradients vanish during training, as the sequence length increases. Gradients can be attenuated by transition operators and are attenuated or dropped by activation functions. Canonical architectures like LSTM alleviate this issue by skipping information through a memory mechanism. We propose a new recurrent architecture (Non-saturating Recurrent Unit; NRU) that relies on a memory mechanism but forgoes both saturating activation functions and saturating gates, in order to further alleviate vanishing gradients. In a series of synthetic and real-world tasks, we demonstrate that the proposed model is the only model that performs among the top 2 models across all tasks with and without long-term dependencies when compared against a range of other architectures. Part 2: Training Recurrent Neural Networks for Lifelong Learning Capacity saturation and catastrophic forgetting are the central challenges of any parametric lifelong learning system. In this work, we study these challenges in the context of sequential supervised learning with an emphasis on recurrent neural networks. To evaluate the models in the life-long learning setting, we propose a curriculum-based, simple, and intuitive benchmark where the models are trained on a task with increasing levels of difficulty. As a step towards developing true lifelong learning systems, we unify Gradient Episodic Memory (a catastrophic forgetting alleviation approach) and Net2Net (a capacity expansion approach). Evaluation on the proposed benchmark shows that the unified model is more suitable than the constituent models for lifelong learning setting BIO Sarath Chandar is a 4th year Ph.D. Candidate at MILA working with Yoshua Bengio and Hugo Larochelle. His research interests include deep learning, reinforcement learning, and natural language processing. He is a recipient of the IBM Ph.D. Fellowship for 2018. For more details refer to http://sarathchandar.in/.

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[Tea Talk] Nicolas Loizou (FAIR) Fri November 16 2018 10:30 AM AA3195
by rim.assouel＠gmail.com 16 Nov '18

16 Nov '18

This week we have Nicolas Loizou from FAIR giving a talk on Fri November 16 2018 at 10:30 AM in room AA3195 Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? Yes Recorded? Yes And you can sign up to meet the speaker here: Getting lazy on Fridays ? FAIR enough, but Momentum is all you need ;) See you there! Rim and Sai TITLE Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods ABSTRACT In this paper we study several classes of stochastic optimization algorithms enriched with heavy ball momentum. Among the methods studied are: stochastic gradient descent, stochastic Newton, stochastic proximal point and stochastic dual subspace ascent. This is the first time momentum variants of several of these methods are studied. We choose to perform our analysis in a setting in which all of the above methods are equivalent. We prove global non-assymptotic linear convergence rates for all methods and various measures of success, including primal function values, primal iterates (in L2 sense), and dual function values. We also show that the primal iterates converge at an accelerated linear rate in the L1 sense. This is the first time a linear rate is shown for the stochastic heavy ball method (i.e., stochastic gradient descent method with momentum). Under somewhat weaker conditions, we establish a sublinear convergence rate for Cesaro averages of primal iterates. Moreover, we propose a novel concept, which we call stochastic momentum, aimed at decreasing the cost of performing the momentum step. We prove linear convergence of several stochastic methods with stochastic momentum, and show that in some sparse data regimes and for sufficiently small momentum parameters, these methods enjoy better overall complexity than methods with deterministic momentum. Finally, we perform extensive numerical testing on artificial and real datasets, including data coming from average consensus problems. BIO Nicolas is a final year PhD student at The University of Edinburgh in School of Mathematics. More specifically he is a member of the Operational Research and Optimization Group (ERGO) under the supervision of Dr. Peter Richtarik.Before he moved to Edinburgh he spent 4 years in Athens as undergraduate student in department of Mathematics at National and Kapodistrian University of Athens <http://en.uoa.gr/> and 1 year as postgraduate student at Imperial College London where he obtained an MSc in Computing (Computational Management Science) <http://www.imperial.ac.uk/computing/prospective-students/courses/pg/special…>. His research interests include (but are not limited to): Large Scale Optimization, Machine Learning, Deep Learning, Randomized numerical linear algebra, Randomized and Distributed Algorithms . Website : https://www.maths.ed.ac.uk/~s1461357/ <https://www.maths.ed.ac.uk/~s1461357/>

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[Room CHANGE] [mila-tous] [Tea Talk] Hugo Larochelle (Mila + Brain) Fri November 9 2018 10:30 AM AA 3195
by Rim Assouel 09 Nov '18

09 Nov '18

The room has changed ( I updated the calendar). This will happen in AA - 3195 : we will add as many chairs as possible. > Le 8 nov. 2018 à 18:07, Alex Lamb <alex6200(a)gmail.com> a écrit : > > "JC S1-111" > > This is jean-couteau on the udem campus? > > On Mon, Nov 5, 2018 at 9:33 AM <rim.assouel(a)gmail.com <mailto:rim.assouel@gmail.com>> wrote: > This week we have Hugo Larochelle from Mila + Brain giving a talk on Fri November 9 2018 at 10:30 AM in room JC S1-111 > > Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? Yes > Recorded? Yes > > Feel like learning to learn ? Hugo will teach you the good way, this Friday, one shot at a time ;) > > See you there! > Rim and Sai > > TITLE Few-Shot Learning with Meta-Learning: Progress Made and Challenges Ahead. > > KEYWORDS > > ABSTRACT > A lot of the recent progress on many AI tasks was enable in part by the availability of large quantities of labeled data. Yet, humans are able to learn concepts from as little as a handful of examples. Meta-learning is a very promising framework for addressing the problem of generalizing from small amounts of data, known as few-shot learning. In meta-learning, our model is itself a learning algorithm: it takes as input a training set and outputs a classifier. For few-shot learning, it is (meta-)trained directly to produce classifiers with good generalization performance for problems with very little labeled data. In this talk, I'll present an overview of the recent research that has made exciting progress on this topic (including my own) and will discuss the challenges as well as research opportunities that remain. > > BIO > Hugo Larochelle is Research Scientist at Google Brain and lead of the Montreal Google Brain team. He is also a member of Yoshua Bengio's Mila and an Adjunct Professor at the Université de Montréal. Previously, he was Associate Professor at the University of Sherbrooke. He also co-founded Whetlab, which was acquired in 2015 by Twitter, where he then worked as a Research Scientist in the Twitter Cortex group. From 2009 to 2011, he was also a member of the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. He obtained his Ph.D. at the Université de Montréal, under the supervision of Yoshua Bengio. His long time nemesis is Aaron Courville. Finally, he has a popular online course on deep learning and neural networks, freely accessible on YouTube. > > -- > You received this message because you are subscribed to the Google Groups "MILA Tous" group. > To unsubscribe from this group and stop receiving emails from it, send an email to mila-tous+unsubscribe(a)mila.quebec <mailto:mila-tous+unsubscribe@mila.quebec>.

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[Tea Talk] Hugo Larochelle (Mila + Brain) Fri November 9 2018 10:30 AM JC S1-111
by rim.assouel＠gmail.com 05 Nov '18

05 Nov '18

This week we have Hugo Larochelle from Mila + Brain giving a talk on Fri November 9 2018 at 10:30 AM in room JC S1-111 Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? Yes Recorded? Yes Feel like learning to learn ? Hugo will teach you the good way, this Friday, one shot at a time ;) See you there! Rim and Sai TITLE Few-Shot Learning with Meta-Learning: Progress Made and Challenges Ahead. KEYWORDS ABSTRACT A lot of the recent progress on many AI tasks was enable in part by the availability of large quantities of labeled data. Yet, humans are able to learn concepts from as little as a handful of examples. Meta-learning is a very promising framework for addressing the problem of generalizing from small amounts of data, known as few-shot learning. In meta-learning, our model is itself a learning algorithm: it takes as input a training set and outputs a classifier. For few-shot learning, it is (meta-)trained directly to produce classifiers with good generalization performance for problems with very little labeled data. In this talk, I'll present an overview of the recent research that has made exciting progress on this topic (including my own) and will discuss the challenges as well as research opportunities that remain. BIO Hugo Larochelle is Research Scientist at Google Brain and lead of the Montreal Google Brain team. He is also a member of Yoshua Bengio's Mila and an Adjunct Professor at the Université de Montréal. Previously, he was Associate Professor at the University of Sherbrooke. He also co-founded Whetlab, which was acquired in 2015 by Twitter, where he then worked as a Research Scientist in the Twitter Cortex group. From 2009 to 2011, he was also a member of the machine learning group at the University of Toronto, as a postdoctoral fellow under the supervision of Geoffrey Hinton. He obtained his Ph.D. at the Université de Montréal, under the supervision of Yoshua Bengio. His long time nemesis is Aaron Courville. Finally, he has a popular online course on deep learning and neural networks, freely accessible on YouTube.

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[Tea talk] [Canceled for Town hall]
by Rim Assouel 30 Oct '18

30 Oct '18

Reminder that there will not be any Tea Talk happening this Friday (2nd nov) because of the Town Hall! Rim and Sai

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[Tea Talk] Gauthier Gidel (Mila) Fri October 26 2018 11:00 PCM Z315
by Rim Assouel 26 Oct '18

26 Oct '18

This week we have our own Gauthier Gidel giving a talk on Fri October 26 2018 at 11:00 in room PCM Z315 Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? Yes Recorded? Yes you GAN learn a lot going to this talk (too easy?) See you there! Rim and Sai TITLE A Variational Inequality Perspective on Generative Adversarial Networks KEYWORDS GANs, variational inequality, mini-max optimization ABSTRACT Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train. One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods designed for this adversarial training. In this work, we cast GAN optimization problems in the general variational inequality framework. Tapping into the mathematical programming literature, we counter some common misconceptions about the difficulties of saddle point optimization and propose to extend methods designed for variational inequalities to the training of GANs. We apply averaging, extrapolation and a novel computationally cheaper variant that we call extrapolation from the past to the stochastic gradient method (SGD) and Adam. BIO Gauthier Gidel received the Diplôme de l’École Normale Supérieure in 2017 (ULM MPI2013) and the Master of Science MVA from École Normale supérieur Paris-Saclay in 2016. Gauthier is currently pursuing his PhD at Mila and DIRO from Université de Montréal under the supervision of Simon Lacoste-Julien.Gauthier’s PhD thesis topic revolves around saddle point optimization (a.k.a mini-max problems) for machine learning and more generally variational inequalities on which Gauthier has published several papers [Gidel et al. 2017, Gidel et al. 2018].

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[Tea Talk] Nick Pawlowski (ICL) Fri October 19 2018 10:30 JC S1-111
by Michael Noukhovitch 19 Oct '18

19 Oct '18

This week we have *Nick Pawlowski * from * ICL (interning at FAIR) * giving a talk on *Fri October 19 2018* at *10:30* in *Jean Coutu S1-111* Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? Yes Nick has to be back at FAIR at 2pm so I'd suggest if you want to meet with him then come out to lunch and you can arrange to meet Nick after. *Prior* to seeing this ad, I would have been very *uncertain* about coming. But now it seem like just what the doctor ordered! Michael *TITLE* Bayesian Deep Learning and Applications to Medical Imaging *KEYWORDS *bayesian deep learning, medical applications *ABSTRACT* Deep learning revolutionised the way we approach computer vision and medical image analysis. Regardless of improved accuracy scores and other metrics, deep learning methods tend to be overconfident on unseen data or even when predicting the wrong label. Bayesian deep learning offers a framework to alleviate some of these concerns by modelling the uncertainty over the weights generating those predictions. This talk will review some previous achievements of the field and introduce Bayes by Hypernet (BbH). BbH uses neural networks to parametrise the variational approximation of the distribution of the parameters. We present more complex parameter distribution, better robustness to adversarial examples, and improved uncertainties. Lastly, we present the use of Bayesian NNs for outlier detection in the medical imaging domain, particularly the application of Brain lesion detection. *BIO* Nick is a PhD student in the Biomedical Image Analysis group at Imperial College London, supervised by Ben Glocker. He works on methods to integrate and use uncertainty with deep learning methods. He focuses on Bayesian neural networks and their use for outlier detection. Nick is currently a Research Intern at FAIR Montreal and a main developer of DLTK, a toolkit for deep learning for medical imaging. During this summer he was a Machine Learning resident at Google X.

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[Tea Talk] Devon Hjelm (MSR Montreal + Mila) Fri October 12 2018 10:30 JC S1-111
by Michael Noukhovitch 12 Oct '18

12 Oct '18

This week we have our very own *Devon Hjelm * from * MSR Montreal x Mila * giving a talk on *Friday October 12 2018* at *10:30* in room *Jean Coutu S1-111* Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? No Will this talk be recorded? Yes Maximize your own information and come learn at this talk! Michael *TITLE* Learning representations with Deep InfoMax *KEYWORDS *representation learning, unsupervised learning, adversarial learning *ABSTRACT* In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality of the input to the objective can greatly influence a representation’s suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and competes with fully-supervised learning on several classification tasks. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation-learning objectives for specific end-goals (https://arxiv.org/abs/1808.06670) *BIO* Devon Hjelm is a researcher at Microsoft Research Montreal and an Adjunct Professor at MILA. He did his postdoc at MILA where he focused on adversarial learning and generative models. His current research focuses on using mutual information estimation objectives in representation learning for applications in computer vision, natural language, and RL.

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[Tea Talk] Viral Shah (Julia Computing) Fri September 14 2018 10:30 Z315
by Michael Noukhovitch 10 Sep '18

10 Sep '18

This week we have *Viral Shah * from * Julia Computing * giving a talk on *Fri September 14 2018* at *10:30* in room *Z315 (in Pavilion Claire McNicoll)*. It's not our usual room but it'll be a regular room for the next two months :) Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? Yes And you can sign up to meet the speaker here: if there's a tea talk, you should always GOTO it Michael *TITLE* On Machine Learning and Programming Languages *KEYWORDS *ML/DL infrastructure, Programming Languages *ABSTRACT* We ask, what might the ideal ML language of the future look like? Our thoughts are published in this blog post: https://julialang.org/blog/2017/12/ml&pl As programming languages (PL) people, we have watched with great interest as machine learning (ML) has exploded -- and with it, the complexity of ML models and the frameworks people are using to build them. State-of-the-art models are increasingly programs, with support for programming constructs like loops and recursion, and this brings out many interesting issues in the tools we use to create them -- that is, programming languages. While machine learning does not yet have a dedicated language, several efforts are effectively creating hidden new languages underneath a Python API (like TensorFlow) while others are reusing Python as a modeling language (like PyTorch). We'd like to ask -- are new ML-tailored languages required, and if so, why? Now that Julia 1.0 is released, we will also discuss how Julia evolved to get where it is today, and how it might evolve to taking on some of the challenges posed by machine learning *BIO* Dr. Viral Shah is a co-creator of the Julia project and Co-founder and CEO of Julia Computing. He has had a long-term track record of building open-source software. Apart from Julia, he is also co-creator of Circuitscape, an open-source program which borrows algorithms from electronic circuit theory for ecological conservation. In the Government of India, he was an early member of the country’s national ID project - Aadhaar, where his work on re-architecting India’s social security systems led to a significant increase in social and financial inclusion, while simultaneously saving the exchequer over a billion dollars in slippage. The experiences of implementing technology at such scale for a billion people are collected in his book: Rebooting India. Viral has a Ph. D. from the University of California at Santa Barbara, in Computer Science.

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[Tea Talk] Geoff Gordon (MSR Montreal) Fri September 7 2018 10:30 AA3195
by Michael Noukhovitch 07 Sep '18

07 Sep '18

This week we are super lucky to have *Geoff Gordon*, Research Director of * MSR Montreal * giving a talk on *Fri September 7 2018* at *10:30* in room *AA3195* Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? *No* Geoff will be available to meet in the afternoon! Sign up here <https://calendar.google.com/calendar/selfsched?sstoken=UVBVTVF0X25Nd09LfGRl…> Knowing that this talk is likely awesome and that you probably come to talks given they're awesome, you're probably going to the talk! Good choice! Michael *TITLE* Neural Networks and Bayes Rule *KEYWORDS* Deep Graphical Models, Reasoning with NNs *ABSTRACT* Relational or structured reasoning is an important current research challenge. The classical approach to this challenge is a templated graphical model: highly expressive, with well-founded semantics, but (at least naively) difficult to scale up, and difficult to combine with the most effective supervised learning methods. More recently, researchers have designed many different deep network architectures for structured reasoning problems, with almost the flip set of advantages and disadvantages. Can we get the best of both worlds? That is, can we design deep nets that look more like graphical models, or graphical models that look more like deep nets, so that we get a framework that is both practical and "semantic"? This talk will take a look at some progress toward such a hybrid framework. *BIO* Dr. Gordon is the Research Director of Microsoft Research Montreal. He is on leave as a Professor in the Department of Machine Learning at Carnegie Mellon University, where he has also served as Interim Department Head and as Associate Department Head for Education. His research interests include artificial intelligence, statistical machine learning, game theory, multi-robot systems, and planning in probabilistic, adversarial, and general-sum domains. His previous appointments include Visiting Professor at the Stanford Computer Science Department and Principal Scientist at Burning Glass Technologies in San Diego. Dr. Gordon received his B.A. in Computer Science from Cornell University in 1991, and his Ph.D. in Computer Science from Carnegie Mellon University in 1999.

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No [Tea Talk] this week
by Michael Noukhovitch 27 Aoû '18

27 Aoû '18

This week's tea talk will be cancelled in lieu of the NLP Workshop <http://www.crm.umontreal.ca/2018/Langue18/progNLP_en.html>. To all those attending, have fun! Michael

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[Tea Talk] David Rolnick (UPenn) August 24 2018 10:30 AA6214
by Michael Noukhovitch 24 Aoû '18

24 Aoû '18

Sorry for the delay but tomorrow we have *David Rolnick * visiting with Konrad Kording's lab at * UPenn * giving a talk on *August 24 2018* at *10:30* in room *AA6214* **Note the different room AA6214** Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? yes David will be free until only 1:30~2pm so if you'd like to go with him for lunch, stay after the talk and join in! Be sure to come and hear about the impactful, *profound* insights David has to share. Michael P.S. since David was so kind as to step in on short notice, I'll be making (or buying) cookies for early attendees ;) *TITLE* The impact of depth on expressivity and learning *KEYWORDS *Deep Learning Theory *ABSTRACT* Deeper networks are more powerful than shallow ones, but can be harder to train. In this talk, we will rigorously investigate why both of these statements are true. Specifically, we will prove that depth leads to an exponentially greater ability to express even simple polynomial functions. We will identify why some initializations and architectures impede learning in deeper networks, and demonstrate (both mathematically and empirically) several principles to bear in mind when designing an MLP/ConvNet/ResNet that will learn effectively. *BIO* David Rolnick completed his Ph.D. in Mathematics at MIT this year, co-advised by Nir Shavit, Max Tegmark, and Ed Boyden. His work focuses on the mathematical foundations of artificial and biological neural networks. A former NSF Graduate Research Fellow and Fulbright Scholar, David has also worked on machine learning research as an intern at Google and DeepMind. He will be joining Konrad Kording's group at UPenn as an NSF Mathematical Sciences Postdoctoral Research Fellow.

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[Visitor Talk] Gael Varoquaux, Friday 2pm
by Pascal Vincent 23 Aoû '18

23 Aoû '18

Hello all, this Friday, we will have the visit of *Gaël Varoquaux* from INRIA, who will be giving a talk at 2 p.m. in AA-3195. Gaël is famous for creating the highly successful scikit-learn software and for his past work on brain imaging and neuroscience. But he will be talking primarily about his current research interests, which are machine learning approaches applicable to more general types of data. Xavier Bouthillier is coordinating his Friday visit, please get in touch with him (xavier.bouthillier(a)gmail.com) if you want to set aside time to meet the speaker. *When:* this Friday, august 24th, at 2 p.m. *Where:* Room 3195, Pavillon André Aisenstadt *Title:* Simple representations for learning: factorizations and similarities *Abstract* Real-life data seldom comes in the ideal form for statistical learning. This talk will focus on high-dimensional problems for signals and discrete entities: when dealing with many, correlated, signals or entities, it is useful to extract representations that capture these correlations. Matrix factorization models provide simple but powerful representations. They are used for recommender systems across discrete entities such as users and products, or to learn good dictionaries to represent images. However they entail large computing costs on very high-dimensional data, databases with many products or high-resolution images. I will present an algorithm to factorize huge matrices based on stochastic subsampling that gives up to 10-fold speed-ups [1]. With discrete entities, the explosion of dimensionality may be due to variations in how a smaller number of categories are represented. Such a problem of "dirty categories" is typical of uncurated data sources. I will discuss how encoding this data based on similarities recovers a useful category structure with no preprocessing. I will show how it interpolates between one-hot encoding and techniques used in character-level natural language processing. [1] Stochastic subsampling for factorizing huge matrices A Mensch, J Mairal, B Thirion, G Varoquaux IEEE Transactions on Signal Processing 66 (1), 113-128 [2] Similarity encoding for learning with dirty categorical variables. P Cerda, G Varoquaux, B Kégl Machine Learning (2018): 1-18

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[Tea Talk] Abhishek Das (Georgia Tech) August 17 2018 10:30 AA3195
by Michael Noukhovitch 17 Aoû '18

17 Aoû '18

This week we have *Abhishek Das * from * Georgia Tech (and currently interning at FAIR Montreal) * giving a talk on *August 17 2018* at *10:30* in room *AA3195* Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? yes Want to meet the speaker? Sign up here <https://calendar.google.com/calendar/selfsched?sstoken=UVBVTVF0X25Nd09LfGRl…> ☕ talk ->🚶to 3195 Michael *TITLE* Connecting Vision and Language to Actions *KEYWORDS *language understanding, VQA, embodied agents *ABSTRACT* Building intelligent agents that possess the ability to perceive the rich visual environment around us, communicate this understanding in natural language to humans and other agents, and execute actions in a physical environment, is a long-term goal of Artificial Intelligence. In this talk, I will present some of my recent work at various points on this spectrum in connecting vision and language to actions; from Visual Dialog (CVPR17, ICCV17, HCOMP17) -- where we develop models capable of holding free-form visually-grounded natural language conversation towards a downstream goal and ways to evaluate them -- to Embodied Question Answering (CVPR18) -- where we augment these models to actively navigate in simulated environments and gather visual information necessary for answering questions. *BIO* Abhishek Das is a Computer Science PhD student at Georgia Institute of Technology, advised by Dhruv Batra, and working closely with Devi Parikh. He is interested in deep learning and its applications in building agents that can see (computer vision), talk (language modeling), act (reinforcement learning), and reason. He is a recipient of an Adobe Research Fellowship and a Snap Research Fellowship. He has held internship positions at Queensland Brain Institute (Winter 2013, Winter 2014), Virginia Tech (Fall 2015) and Facebook AI Research (Summer 2017, Winter 2018, Summer 2018). He graduated from Indian Institute of Technology Roorkee in 2015 with a Bachelor's degree in Electrical Engineering.

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Re: [Lisa_teatalk] Lisa_teatalk Digest, Vol 87, Issue 7
by Sanjib Manna 17 Aoû '18

17 Aoû '18

FYI - There are 3 of us on Blue Jeans waiting for the stream to start. ________________________________ From: Lisa_teatalk <lisa_teatalk-bounces(a)iro.umontreal.ca> on behalf of lisa_teatalk-request(a)iro.umontreal.ca <lisa_teatalk-request(a)iro.umontreal.ca> Sent: Tuesday, August 14, 2018 11:00 AM To: lisa_teatalk(a)iro.umontreal.ca Subject: Lisa_teatalk Digest, Vol 87, Issue 7 Send Lisa_teatalk mailing list submissions to lisa_teatalk(a)iro.umontreal.ca To subscribe or unsubscribe via the World Wide Web, visit https://mailman.iro.umontreal.ca/cgi-bin/mailman/listinfo/lisa_teatalk or, via email, send a message with subject or body 'help' to lisa_teatalk-request(a)iro.umontreal.ca You can reach the person managing the list at lisa_teatalk-owner(a)iro.umontreal.ca When replying, please edit your Subject line so it is more specific than "Re: Contents of Lisa_teatalk digest..." Today's Topics: 1. [Tea Talk] Abhishek Das (Georgia Tech) August 17 2018 10:30 AA3195 (Michael Noukhovitch) 2. Re: [Tea Talk] Abhishek Das (Georgia Tech) August 17 2018 10:30 AA3195 (Michael Noukhovitch) ---------------------------------------------------------------------- Message: 1 Date: Mon, 13 Aug 2018 16:54:44 -0400 From: Michael Noukhovitch <mnoukhov(a)gmail.com> To: mila-tous(a)mila.quebec, lisa_teatalk(a)iro.umontreal.ca, lisa_seminaires(a)iro.umontreal.ca, teatalk-orgs(a)lisa.iro.umontreal.ca Subject: [Lisa_teatalk] [Tea Talk] Abhishek Das (Georgia Tech) August 17 2018 10:30 AA3195 Message-ID: <CAMVr0Vxi7+wTnsG6Xziz--04ck5GoBQsvE9e2DbFsQoMruKdTA(a)mail.gmail.com> Content-Type: text/plain; charset="utf-8" This week we have *Abhishek Das * from * Georgia Tech (and currently interning at FAIR Montreal) * giving a talk on *August 17 2018* at *10:30* in room *AA3195* Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? yes Want to meet the speaker? Sign up here <https://calendar.google.com/calendar/selfsched?sstoken=UVBVTVF0X25Nd09LfGRl…> ☕ talk ->🚶to 3195 Michael *TITLE* Connecting Vision and Language to Actions *KEYWORDS *language understanding, VQA, embodied agents *ABSTRACT* Building intelligent agents that possess the ability to perceive the rich visual environment around us, communicate this understanding in natural language to humans and other agents, and execute actions in a physical environment, is a long-term goal of Artificial Intelligence. In this talk, I will present some of my recent work at various points on this spectrum in connecting vision and language to actions; from Visual Dialog (CVPR17, ICCV17, HCOMP17) -- where we develop models capable of holding free-form visually-grounded natural language conversation towards a downstream goal and ways to evaluate them -- to Embodied Question Answering (CVPR18) -- where we augment these models to actively navigate in simulated environments and gather visual information necessary for answering questions. *BIO* Abhishek Das is a Computer Science PhD student at Georgia Institute of Technology, advised by Dhruv Batra, and working closely with Devi Parikh. He is interested in deep learning and its applications in building agents that can see (computer vision), talk (language modeling), act (reinforcement learning), and reason. He is a recipient of an Adobe Research Fellowship and a Snap Research Fellowship. He has held internship positions at Queensland Brain Institute (Winter 2013, Winter 2014), Virginia Tech (Fall 2015) and Facebook AI Research (Summer 2017, Winter 2018, Summer 2018). He graduated from Indian Institute of Technology Roorkee in 2015 with a Bachelor's degree in Electrical Engineering. -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://mailman.iro.umontreal.ca/pipermail/lisa_teatalk/attachments/20180813…> ------------------------------ Message: 2 Date: Tue, 14 Aug 2018 11:06:07 -0400 From: Michael Noukhovitch <mnoukhov(a)gmail.com> To: mila-tous(a)mila.quebec, lisa_teatalk(a)iro.umontreal.ca, lisa_seminaires(a)iro.umontreal.ca, teatalk-orgs(a)lisa.iro.umontreal.ca Subject: Re: [Lisa_teatalk] [Tea Talk] Abhishek Das (Georgia Tech) August 17 2018 10:30 AA3195 Message-ID: <CAMVr0Vz-QOfFQ-BtL7Y3DGNOx6X=yE=FObhqKJZQB-uS3NhXYQ(a)mail.gmail.com> Content-Type: text/plain; charset="utf-8" For those wishing to meet with Abhishek: It seems that he will only be available until 1:30pm, so if you're keen to meet, there will be a group that will go out to lunch together after the talk. Please stay after the talk and join! On Mon, Aug 13, 2018 at 4:54 PM Michael Noukhovitch <mnoukhov(a)gmail.com> wrote: > This week we have *Abhishek Das * from * Georgia Tech (and currently > interning at FAIR Montreal) * giving a talk on *August 17 2018* at *10:30* > in room *AA3195* > > Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? > yes > Want to meet the speaker? Sign up here > <https://calendar.google.com/calendar/selfsched?sstoken=UVBVTVF0X25Nd09LfGRl…> > ☕ talk ->🚶to 3195 > Michael > > *TITLE* Connecting Vision and Language to Actions > > *KEYWORDS *language understanding, VQA, embodied agents > > *ABSTRACT* > Building intelligent agents that possess the ability to perceive the rich > visual environment around us, communicate this understanding in natural > language to humans and other agents, and execute actions in a physical > environment, is a long-term goal of Artificial Intelligence. In this talk, > I will present some of my recent work at various points on this spectrum in > connecting vision and language to actions; from Visual Dialog (CVPR17, > ICCV17, HCOMP17) -- where we develop models capable of holding free-form > visually-grounded natural language conversation towards a downstream goal > and ways to evaluate them -- to Embodied Question Answering (CVPR18) -- > where we augment these models to actively navigate in simulated > environments and gather visual information necessary for answering > questions. > > *BIO* > Abhishek Das is a Computer Science PhD student at Georgia Institute of > Technology, advised by Dhruv Batra, and working closely with Devi Parikh. > He is interested in deep learning and its applications in building agents > that can see (computer vision), talk (language modeling), act > (reinforcement learning), and reason. He is a recipient of an Adobe > Research Fellowship and a Snap Research Fellowship. He has held internship > positions at Queensland Brain Institute (Winter 2013, Winter 2014), > Virginia Tech (Fall 2015) and Facebook AI Research (Summer 2017, Winter > 2018, Summer 2018). He graduated from Indian Institute of Technology > Roorkee in 2015 with a Bachelor's degree in Electrical Engineering. > -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://mailman.iro.umontreal.ca/pipermail/lisa_teatalk/attachments/20180814…> ------------------------------ Subject: Digest Footer _______________________________________________ Lisa_teatalk mailing list Lisa_teatalk(a)iro.umontreal.ca https://mailman.iro.umontreal.ca/cgi-bin/mailman/listinfo/lisa_teatalk ------------------------------ End of Lisa_teatalk Digest, Vol 87, Issue 7 *******************************************

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[Tea Talk] Wengong Jin (MIT) August 10 2018 10:30 AA3195
by Michael Noukhovitch 10 Aoû '18

10 Aoû '18

This week we have *Wengong Jin * from * MIT * giving a talk on *August 10 2018* at *10:30* in room *AA3195* Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? yes Wengong is free to meet until 1:30pm so email me if you'd be interested in grabbing lunch with him or talking with him before the talk. If you're interested in the field, I'm certain the talk will get a great reaction! Michael *TITLE* Graph Representation Learning for Chemistry *KEYWORDS *computational chemistry, graph learning, generative models *ABSTRACT* Learning representation for molecules and reactions is crucial for computational chemistry. Traditional methods, based on expert designed molecular fingerprints or reaction templates, have lead to suboptimal predictive performance and poor scalability. In this talk, I will present our recent effort in dealing with these challenges. Particularly, I will demonstrate two novel approaches, showing how graph neural network could overcome these challenges, and how domain knowledge could help us design better graph neural network architectures. In the first part of the talk, I will describe a graph convolutional network (with self-attention) for predicting organic reaction outcomes. Our model significantly outperforms traditional template-based methods, approaching expert chemist performance while running 100x faster. In the second part of the talk, I will present a generative model called Junction Tree Variational Autoencoder for molecular graph generation, and show how it could be used for drug discovery. *BIO* Wengong Jin is currently a PhD student in Computer Science and Artificial Intelligence Lab (CSAIL) at MIT, working with Prof. Regina Barzilay and Tommi Jaakkola. Before he finished his Master of Science degree from MIT in 2018, and Bachelor of Engineering from Shanghai Jiao Tong University in 2016. His research interest lies in machine learning, with particular focus on deep learning methods for graphs. His recent research on molecular graph generation has been covered as spotlight news in MIT homepage. He is also honored with 2018 Ho-Ching and Han-Ching scholarship.

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Resting-state and Brain Connectivity 2018, in Montreal
by Amir Shmuel, Dr. 07 Aoû '18

07 Aoû '18

For Montreal’s machine learning experts who are interested in inspiration for their neural networks and/or applications to neuroscience: Resting State and Brain Connectivity 2018 in Montreal Educational course: September 24-25, 2018 Main conference: September 26-28, 2018 We have received over 285 submissions! Abstract submission will remain open for late submissions until 11:59 PM on August 11, 2018. Early registration fee applies until 11:59 PM on August 15, 2018. Amir Shmuel Chair of the organizing committee Resting-State and Brain Connectivity 2018 Montreal, QC, Canada http://restingstate.com/2018/ [http://restingstate.com/2018/wp-content/uploads/2017/06/resting-state-logo-…]

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[Extra Tea Talk] Berton Earnshaw (Recursion Pharmaceuticals) Mon Aug 6 12PM AA3195
by Michael Noukhovitch 06 Aoû '18

06 Aoû '18

Early next week we have an extra talk by *Berton Earnshaw*, Senior Director of Data Science Research at *Recursion Pharmaceuticals* giving a talk on *Monday August 6 2018* at *12pm - 1pm* in room *AA3195.* This will be a lunch talk and mingle with *food served!* Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? Yes Want to meet with Berton in the afternoon? https://calendar.google.com/calendar/selfsched?sstoken=UVBVTVF0X25Nd09LfGRl… Come for the food and machine learning, stay because the talk will be awesome. Michael *TITLE *Representation learning in human cellular biology *KEYWORDS* bioinformatics, representation learning *ABSTRACT* The automated labs at Recursion Pharmaceuticals currently run more than 100,000 experiments every week, and each experiment involves imaging the morphology of human cells under various biological and chemical perturbations. The resulting data set is the largest and most diverse of its kind, and is used to train networks that learn to predict which compounds treat which diseases, and why. In this talk, I will describe the challenges of learning on biological image sets, discuss some approaches we have taken to learning representations of these images, and describe how we use these representations to discover candidate treatments for disease. *BIO * Berton earned a PhD in math from the University of Utah in 2007, where he designed biophysical models of protein trafficking at synapses during episodes of learning and memory formation. In 2010, after two post-docs at Utah and Michigan State University spent developing the theory of non-autonomous master equations, Berton took some time off of academia and co-founded the call center software company Perfect Pitch where he led as CTO. In 2012 he joined the founding team of Red Brain Labs as Director of Operations and Data Science, where he worked to optimize call centers using machine learning and simulation. In 2014, Red Brain Labs was acquired by Savvysherpa where, as a Senior Scientist and Principal, Berton led various research projects applying machine learning to insurance claims and medical records with the goal of improving healthcare outcomes. In 2017, Berton joined Recursion Pharmaceuticals as Senior Director of Data Science Research, where he leads the research team in applying deep learning to millions of images of human cells in search of treatments for hundreds of diseases. *COMPANY BIO* At Recursion Pharmaceuticals, we have raised over $80M to apply machine learning to one of the most unique datasets in existence - tens of millions of images of cells under a huge number of biological and chemical perturbations, generated in our own labs - in order to find treatments for hundreds of diseases. Our long term mission is to decode biology to radically improve lives - we want to understand biology so well that we can fix most things that go wrong in our bodies.

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Petar Veličković presentation from August 3
by Sanjib Manna 06 Aoû '18

06 Aoû '18

Hello I do not find Petar's presentation "Keeping our graphs attentive" on the MILA Tea Talks page. Would it be possible to make that available. Regards, Sanjib ________________________________ From: Lisa_teatalk <lisa_teatalk-bounces(a)iro.umontreal.ca> on behalf of lisa_teatalk-request(a)iro.umontreal.ca <lisa_teatalk-request(a)iro.umontreal.ca> Sent: Friday, August 3, 2018 11:00 AM To: lisa_teatalk(a)iro.umontreal.ca Subject: Lisa_teatalk Digest, Vol 87, Issue 2 Send Lisa_teatalk mailing list submissions to lisa_teatalk(a)iro.umontreal.ca To subscribe or unsubscribe via the World Wide Web, visit https://mailman.iro.umontreal.ca/cgi-bin/mailman/listinfo/lisa_teatalk or, via email, send a message with subject or body 'help' to lisa_teatalk-request(a)iro.umontreal.ca You can reach the person managing the list at lisa_teatalk-owner(a)iro.umontreal.ca When replying, please edit your Subject line so it is more specific than "Re: Contents of Lisa_teatalk digest..." Today's Topics: 1. [Extra Tea Talk] Berton Earnshaw (Recursion Pharmaceuticals) Mon Aug 6 12PM AA3195 (Michael Noukhovitch) 2. Re: [Tea Talk] Petar Veličković (Cambridge / Mila) August 32018 10:30 AA3195 (Michael Noukhovitch) ---------------------------------------------------------------------- Message: 1 Date: Thu, 2 Aug 2018 21:53:11 -0400 From: Michael Noukhovitch <mnoukhov(a)gmail.com> To: mila-tous(a)mila.quebec, lisa_teatalk(a)iro.umontreal.ca, lisa_seminaires(a)iro.umontreal.ca, teatalk-orgs(a)lisa.iro.umontreal.ca Subject: [Lisa_teatalk] [Extra Tea Talk] Berton Earnshaw (Recursion Pharmaceuticals) Mon Aug 6 12PM AA3195 Message-ID: <CAMVr0VwfVewOL3E_CS8+zAN+BAd0V1zDA2+B+iGOvuk7hoFrBg(a)mail.gmail.com> Content-Type: text/plain; charset="utf-8" Early next week we have an extra talk by *Berton Earnshaw*, Senior Director of Data Science Research at *Recursion Pharmaceuticals* giving a talk on *Monday August 6 2018* at *12pm - 1pm* in room *AA3195.* This will be a lunch talk and mingle with *food served!* Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? Yes Want to meet with Berton in the afternoon? https://calendar.google.com/calendar/selfsched?sstoken=UVBVTVF0X25Nd09LfGRl… Come for the food and machine learning, stay because the talk will be awesome. Michael *TITLE *Representation learning in human cellular biology *KEYWORDS* bioinformatics, representation learning *ABSTRACT* The automated labs at Recursion Pharmaceuticals currently run more than 100,000 experiments every week, and each experiment involves imaging the morphology of human cells under various biological and chemical perturbations. The resulting data set is the largest and most diverse of its kind, and is used to train networks that learn to predict which compounds treat which diseases, and why. In this talk, I will describe the challenges of learning on biological image sets, discuss some approaches we have taken to learning representations of these images, and describe how we use these representations to discover candidate treatments for disease. *BIO * Berton earned a PhD in math from the University of Utah in 2007, where he designed biophysical models of protein trafficking at synapses during episodes of learning and memory formation. In 2010, after two post-docs at Utah and Michigan State University spent developing the theory of non-autonomous master equations, Berton took some time off of academia and co-founded the call center software company Perfect Pitch where he led as CTO. In 2012 he joined the founding team of Red Brain Labs as Director of Operations and Data Science, where he worked to optimize call centers using machine learning and simulation. In 2014, Red Brain Labs was acquired by Savvysherpa where, as a Senior Scientist and Principal, Berton led various research projects applying machine learning to insurance claims and medical records with the goal of improving healthcare outcomes. In 2017, Berton joined Recursion Pharmaceuticals as Senior Director of Data Science Research, where he leads the research team in applying deep learning to millions of images of human cells in search of treatments for hundreds of diseases. *COMPANY BIO* At Recursion Pharmaceuticals, we have raised over $80M to apply machine learning to one of the most unique datasets in existence - tens of millions of images of cells under a huge number of biological and chemical perturbations, generated in our own labs - in order to find treatments for hundreds of diseases. Our long term mission is to decode biology to radically improve lives - we want to understand biology so well that we can fix most things that go wrong in our bodies.

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[Tea Talk] Petar Veličković (Cambridge / Mila) August 32018 10:30 AA3195
by Michael Noukhovitch 03 Aoû '18

03 Aoû '18

This week we have *Petar Veličković * from * Cambridge/Mila * giving a talk on *August 3rd 2018* at *10:30* in room *AA3195* Will this talk be streamed <https://mila.bluejeans.com/1365483656/webrtc>? yes Come to the talk and make sure to pay attention, because this talk should be great to connect the dots! Michael *TITLE* Keeping our graphs attentive *KEYWORDS *graph structured, attention, computational biology *ABSTRACT* A multitude of important real-world datasets (especially in biology) come together with some form of graph structure: social networks, citation networks, protein-protein interactions, brain connectome data, etc. Extending neural networks to be able to properly deal with this kind of data is therefore a very important direction for machine learning research, but one that has received comparatively rather low levels of attention until very recently. Attentional mechanisms represent a very promising direction for extending the established convolutional operator on images to work on arbitrary graphs, as they satisfy many of the desirable features for a convolutional operator. Through this talk, I will focus on my work on Graph Attention Networks (GATs), where these theoretical properties have been further validated by solid results on transductive as well as inductive node classification benchmarks. I will also outline some of the earlier efforts towards deploying attention-style operators on graph structures, as well as very exciting recent work that expands on GATs and deploys them in more general circumstances (such as EAGCN, DeepInf, and applications to solving the Travelling Salesman Problem). Time permitting, I will also present some of the relevant related graph-based work in the computational biology and medical imaging domains that I have been involved in. *BIO* Petar Veličković is currently a final-year PhD student in Machine Learning and Bioinformatics at the Department of Computer Science and Technology of the University of Cambridge. He also holds a BA degree in Computer Science from Cambridge, having completed the Computer Science Tripos in 2015. In addition, he has been involved in research placements at Nokia Bell Labs (working with Nicholas Lane) and the Montréal Institute of Learning Algorithms (working with Adriana Romero and Yoshua Bengio). His current research interests broadly involve devising neural network architectures that operate on nontrivially structured data (such as graphs), and their applications in bioinformatics and medicine. He has published his work in these areas at both machine learning venues (ICLR, NIPS ML4H, ICML WCB) and biomedical venues and journals (Bioinformatics, PervasiveHealth)

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No [Tea Talk] This Week
by Michael Noukhovitch 24 Jul '18

24 Jul '18

EOM

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[Tea Talk] Glen Berseth (UBC) July 20 2018 10:30 AA3195
by Michael Noukhovitch 20 Jul '18

20 Jul '18

This week we have *Glen Berseth * from * UBC*, who will be interning with Chris Pal, giving a talk on *July 20 2018* at *10:30* in room *AA3195* Will this talk be streamed <https://mila.bluejeans.com/1365483656/webrtc>? Yes! Want to meet with Glen? Glen is only available until 2pm on Friday, so send him an email directly (gberseth(a)cs.ubc.ca) and perhaps invite him to lunch :) Given how many tea talks we've had, it should be a breeze to find your way by moving your legs and walking to come to this tea talk! Michael *P.S.* We still don't have a speaker for next week's talk, so if you're interested in giving a talk please slack or email me! *TITLE* Scalable Deep Reinforcement Learning for Physics-Based Motion Control *KEYWORDS *hierarchical RL, meta-learning, multi-task learning *ABSTRACT* Motion control in physics-based animation is challenging due to complex dynamics and discontinuous contacts. Many previous control methods that produce walking motions are very stiff, only work in particular environments and require significant manual tuning to get functioning. In this work, we progress the state-of-the-art in physics-based character animation in a number of directions using machine learning methods. We present three contributions that build upon the current research on motion control using deepRL. First, we show that decomposing tasks into a hierarchy increase learning efficiency by operating across multiple time scales on a complex locomotion and navigation task. Second, we investigate improved action exploration methods to sample more promising actions on robots and in simulation using forward dynamics distributions. This sampling strategy has been shown to improve sample efficiency for a number of problems, including many from the OpenAI Gym. Last, we consider a new algorithm to progressively learn and integrate new skills producing a robust and multi-skilled physics-based controller. This algorithm combines the skills of experts together and then applies transfer learning methods to initialize and accelerate the learning of new skills. *BIO* Glen Berseth is a PhD candidate at the Department of Computer Science at the University of British Columbia where he works on reinforcement learning, machine learning and motion planning. He received the BSc degree in Computer Science from York University in 2012 and his MSc from York University under the supervision of Petros Faloutsos in 2014 for optimization and authoring crowd simulations. He has published in a wide range of areas including computer animation, machine learning and robotics and is an NSERC scholarship award holder.

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No [Tea Talk] on July 13 2018
by Michael Noukhovitch 11 Jul '18

11 Jul '18

*No talk on* *July 13 2018* Have fun at ICML! Michael

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[Tea Talk] ICML Lightning Talks (Mila) July 6 2018 10:30AM AA1177
by Michael Noukhovitch 06 Jul '18

06 Jul '18

This week we have the second edition of the lightning talk tradition, ICML-edition, it will be on *Friday July 6 2018* at *10:30AM* in room *AA1177.* *Note the different room, AA1177* Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>? Yes, but certain speakers may not be streamed I found $10 on the ground today so I will be buying $10 worth of snacks for the talk to sweeten the deal. Don't miss this chance to see lightning strike twice! Michael *WHAT IT IS* Mila authors of ICML-accepted conference and workshop papers do quick, 5 minute presentations of their work *PAPERS SIGNED UP SO FAR (please sign up!)* Focused Hierarchical RNNs for Conditional Sequence Processing Nan Ke · Konrad Zolna · Alessandro Sordoni · Zhouhan Lin · Adam Trischler · Yoshua Bengio · Joelle Pineau · Laurent Charlin · Christopher Pal Neural Autoregressive Flows Chin-Wei Huang · David Krueger · Alexandre Lacoste · Aaron Courville Approximate Exploration through State Abstraction Adrien Ali Taiga, Aaron Courville, Marc Bellemare Negative Momentum for Improved Game Dynamics Gauthier Gidel Reyhane Askari Mohammad Pezeshki Gabriel Huang Rémi Lepriol Ioannis Mitliagkas Simon Lacoste-Julien Oríon : Experiment Version Control for Efficient Hyperparameter Optimization Christos Tsirigotis, Xavier Bouthillier, François Corneau-Tremblay, Peter Henderson, Reyhane Askari, Samuel Lavoie-Marchildon, Tristan Deleu, Dendi Suhubdy, Michael Noukhovitch, Frédéric Bastien, Pascal Lamblin Attentive cross-modal paratope prediction Andreea Deac, Petar Veličković, Pietro Sormanni

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