Jian Tang

Biography

Jian Tang is an Associate professor at HEC's Department of Decision Sciences. He is also an Adjunct professor at the Department of Computer Science and Operations Research at University of Montreal and a Core Academic member at Mila - Quebec AI Institute. He is a Canada CIFAR AI Chair and the Founder of BioGeometry, an AI startup that focuses on generative AI for antibody discovery. Tang’s main research interests are deep generative models and graph machine learning, and their applications to drug discovery. He is an international leader in graph machine learning, and LINE, his node representation method, has been widely recognized and cited more than five thousand times. He has also done pioneering work on AI for drug discovery, such as developing the first open-source machine learning frameworks for drug discovery, TorchDrug and TorchProtein.

Current Students

Huiyu Cai

PhD - Université de Montréal

Collaborating researcher

Farzana Heidari

PhD - Université de Montréal

Principal supervisor :

Xixian Liu

PhD - Université de Montréal

Jiarui Lu

PhD - Université de Montréal

Chence Shi

PhD - Université de Montréal

Yu Tianshu

Independent visiting researcher - Chinese University of Hong Kong

Sophie Xhonneux

PhD - Université de Montréal

Principal supervisor :

Gauthier Gidel

Xinyu Yuan

PhD - Université de Montréal

Zhihao Zhan

PhD - Université de Montréal

PhD - HEC Montréal

PhD - Université de Montréal

Jianan Zhao

PhD - Université de Montréal

Publications

S UPPLEMENTARY M ATERIAL - L EARNING T O N AVIGATE T HE S YNTHETICALLY A CCESSIBLE C HEMICAL S PACE U SING R EINFORCEMENT L EARNING

Sai Krishna

Gottipati

B. Sattarov

Sufeng Niu

Yashaswi Pathak

Haoran Wei

Shengchao Liu

Karam M. J. Thomas

Simon R. Blackburn

Connor Wilson. Coley

Sarath Chandar

Yoshua Bengio

While updating the critic network, we multiply the normal random noise vector with policy noise of 0.2 and then clip it in the range -0.2 to… (see more) 0.2. This clipped policy noise is added to the action at the next time step a′ computed by the target actor networks f and π. The actor networks (f and π networks), target critic and target actor networks are updated once every two updates to the critic network.

Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures

Jean Michel Sellier

Generative models have achieved impressive results in many domains including image and text generation. In the natural sciences, generative … (see more)models have led to rapid progress in automated drug discovery. Many of the current methods focus on either 1-D or 2-D representations of typically small, drug-like molecules. However, many molecules require 3-D descriptors and exceed the chemical complexity of commonly used dataset. We present a method to encode and decode the position of atoms in 3-D molecules from a dataset of nearly 50,000 stable crystal unit cells that vary from containing 1 to over 100 atoms. We construct a smooth and continuous 3-D density representation of each crystal based on the positions of different atoms. Two different neural networks were trained on a dataset of over 120,000 three-dimensional samples of single and repeating crystal structures, made by rotating the single unit cells. The first, an Encoder-Decoder pair, constructs a compressed latent space representation of each molecule and then decodes this description into an accurate reconstruction of the input. The second network segments the resulting output into atoms and assigns each atom an atomic number. By generating compressed, continuous latent spaces representations of molecules we are able to decode random samples, interpolate between two molecules, and alter known molecules.

2019-09-03

ArXiv (preprint)

openreview.net

Contextualized Non-local Neural Networks for Sequence Learning

Pengfei Liu

Shuaichen Chang

Xuanjing Huang

Jackie Cheung

Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which selfattention, as exemplified by… (see more) the Transformer model, and graph neural networks (GNNs) have attracted much attention. In this paper, we propose an approach that combines and draws on the complementary strengths of these two methods. Specifically, we propose contextualized non-local neural networks (CN3), which can both dynamically construct a task-specific structure of a sentence and leverage rich local dependencies within a particular neighbourhood.Experimental results on ten NLP tasks in text classification, semantic matching, and sequence labelling show that our proposed model outperforms competitive baselines and discovers task-specific dependency structures, thus providing better interpretability to users.

2019-07-17

Proceedings of the AAAI Conference on Artificial Intelligence (published)

doi.org

Learning Powerful Policies by Using Consistent Dynamics Model

Sergey Levine

Model-based Reinforcement Learning approaches have the promise of being sample efficient. Much of the progress in learning dynamics models i… (see more)n RL has been made by learning models via supervised learning. But traditional model-based approaches lead to `compounding errors' when the model is unrolled step by step. Essentially, the state transitions that the learner predicts (by unrolling the model for multiple steps) and the state transitions that the learner experiences (by acting in the environment) may not be consistent. There is enough evidence that humans build a model of the environment, not only by observing the environment but also by interacting with the environment. Interaction with the environment allows humans to carry out experiments: taking actions that help uncover true causal relationships which can be used for building better dynamics models. Analogously, we would expect such interactions to be helpful for a learning agent while learning to model the environment dynamics. In this paper, we build upon this intuition by using an auxiliary cost function to ensure consistency between what the agent observes (by acting in the real world) and what it imagines (by acting in the `learned' world). We consider several tasks - Mujoco based control tasks and Atari games - and show that the proposed approach helps to train powerful policies and better dynamics models.

2019-06-11

ArXiv (preprint)

GMNN: Graph Markov Neural Networks

Meng Qu

Yoshua Bengio

This paper studies semi-supervised object classification in relational data, which is a fundamental problem in relational data modeling. The… (see more) problem has been extensively studied in the literature of both statistical relational learning (e.g. relational Markov networks) and graph neural networks (e.g. graph convolutional networks). Statistical relational learning methods can effectively model the dependency of object labels through conditional random fields for collective classification, whereas graph neural networks learn effective object representations for classification through end-to-end training. In this paper, we propose the Graph Markov Neural Network (GMNN) that combines the advantages of both worlds. A GMNN models the joint distribution of object labels with a conditional random field, which can be effectively trained with the variational EM algorithm. In the E-step, one graph neural network learns effective object representations for approximating the posterior distributions of object labels. In the M-step, another graph neural network is used to model the local label dependency. Experiments on object classification, link classification, and unsupervised node representation learning show that GMNN achieves state-of-the-art results.

2019-05-24

Proceedings of the 36th International Conference on Machine Learning (published)

proceedings.mlr.press

Session-Based Social Recommendation via Dynamic Graph Attention Networks

Weiping Song

Zhiping Xiao

Yifan Wang

Laurent Charlin

Ming Zhang

Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their … (see more)users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and context-dependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models.

2019-01-30

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (published)

doi.org