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 …
[View More]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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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'…
[View More]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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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
…
[View More]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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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.…
[View More]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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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, …
[View More]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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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 …
[View More]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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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
…
[View More]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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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 …
[View More]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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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 …
[View More]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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