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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Correction for the streaming link https://mila.bluejeans.com/809027115/webrtc
On Tue, Jul 31, 2018, 15:58 Michael Noukhovitch mnoukhov@gmail.com wrote:
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)
Reminder this is now
On Tue, Jul 31, 2018, 16:12 Michael Noukhovitch mnoukhov@gmail.com wrote:
Correction for the streaming link https://mila.bluejeans.com/809027115/webrtc
On Tue, Jul 31, 2018, 15:58 Michael Noukhovitch mnoukhov@gmail.com wrote:
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)
I'd like to thank everyone for coming to the talk today! I hope you've found it useful.
You may find the slides from today on my webpage: https://www.cl.cam.ac.uk/~pv273/slides/MILA-attn.pdf and I'm happy to answer any offline questions you might have.
Thanks, Petar
On 2018-08-03 10:31, Michael Noukhovitch wrote:
Reminder this is now
On Tue, Jul 31, 2018, 16:12 Michael Noukhovitch mnoukhov@gmail.com wrote:
Correction for the streaming link https://mila.bluejeans.com/809027115/webrtc [2]
On Tue, Jul 31, 2018, 15:58 Michael Noukhovitch mnoukhov@gmail.com wrote:
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 [1]? 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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Links:
[1] https://mila.bluejeans.com/1365483656/webrtc [2] https://mila.bluejeans.com/809027115/webrtc
lisa_seminaires@iro.umontreal.ca
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Michael Noukhovitch -
Petar Veličković