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.
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-…]
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.
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
________________________________
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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.
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)
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.
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
This week we have *Anne Churchland * from * Cold Spring Harbor Laboratory *
giving a talk on *June 29 2018* at *10:30* in room *AA3195*
This talk may be streamed as usual here
<https://mila.bluejeans.com/1365483656/webrtc>
And you can sign up to meet the speaker here
<https://calendar.google.com/calendar/selfsched?sstoken=UVBVTVF0X25Nd09LfGRl…>
Don't even think about it! Decide to come to the tea talk and let your feet
spontaneously guide the way.
Michael
*TITLE* Spontaneous movements dominate cortical activity during
sensory-guided decision making
*KEYWORDS *neuroscience, decision-making, neuro-imaging
*ABSTRACT*
Animals continually produce a wide array of spontaneous and learned
movements and undergo rapid internal state transitions. Most work in
neuroscience ignores this “internal backdrop” and instead focuses on
neural activity aligned to task-imposed variables such as sensory stimuli.
We sought to understand the joint effects of internal backdrop vs.
task-imposed variables. We measured neural activity using calcium imaging
via a widefield macroscope during decision-making. Surprisingly, the impact
of the internal backdrop dwarfed task-imposed sensory and
cognitive signals. This dominance was comparable in novice and expert
decision-makers and was even stronger in single neuron measurements from
frontal cortex. These results highlight spontaneous and learned movements
as the main determinant of large-scale cortical activity. By leveraging a
wide array of animal movements, our model offers a very general method for
separating the impact of internal backdrop from task-imposed
neural activity.
*BIO* Anne K. Churchland received her Ph.D. in neuroscience from the
University of California, San Francisco, advised by Dr. Stephen Lisberger.
She then did a postdoctoral fellowship with Dr. Michael Shadlen at the
University of Washington in the Physiology and Biophysics Department. Her
postdoctoral work focused on mechanisms of decision making in nonhuman
primates and included both experimental and theoretical work. The latter
was funded by a Pathways to Independence (K99) Award from the National Eye
Institute. In 2010, she became an assistant professor at Cold Spring Harbor
Laboratory. In starting her own laboratory, Professor Churchland began
studying decision making using rodent models to take advantage of emerging
tools for circuit dissection which are readily available in rodents. Since
then, her laboratory has been a major player in bringing behavioral
paradigms to rodents that have been successful in elucidating neural
mechanisms in primates. These include perceptual decision making and
multisensory integration. Most recently, she has teamed up with other 20
other researchers to for the International Brain Laboratory, a
collaboration between theorists and experimentalists aiming to understand
the neural computations supporting decision-making. Since joining Cold
Spring Harbor Laboratory, Professor Churchland has been the recipient of
awards from the McKnight Foundation, the Pew Charitable Trusts, the
Klingenstein-Simons Foundation, the Simons Collaboration on the Global
Brain, the John Merck Fund and the Chapman Foundations. In addition to her
scientific work, Professor Churchland runs science outreach activities at
public schools and co-directed the Undergraduate Research Program at Cold
Spring Harbor Laboratory. She also maintains a blog about neuroscience
research that is directed at a mainstream audience; its recent focus is on
reviewing papers on the preprint server. Professor Churchland also funded
anneslist.net, a resource highlighting women in systems and computational
neuroscience. She received the 2017 Louise Hanson Marshall Special
Recognition Award from the Society for Neuroscience for promoting diversity
in the field and the 2012 Janett Rosenberg Trubatch Career Development
Award for creativity in research prior to tenure.