Jian Tang

farzaneh.heidari@mila.quebec

Biographie

Jian Tang est professeur agrégé au département de sciences de la décision de HEC. Il est aussi professeur associé au département informatique et recherche opérationnelle (DIRO) de l'Université de Montréal et un membre académique principal à Mila – Institut québécois d’intelligence artificielle. Il est titulaire d'une chaire de recherche en IA Canada-CIFAR et le fondateur de BioGeometry, une entreprise en démarrage spécialisée dans l'IA générative pour la découverte d'anticorps. Ses principaux domaines de recherche sont les modèles génératifs profonds, l'apprentissage automatique des graphes et leurs applications à la découverte de médicaments. Il est un leader international dans le domaine de l'apprentissage automatique des graphes, et son travail représentatif sur l'apprentissage de la représentation des nœuds, LINE, a été largement reconnu et cité plus de 5 000 fois. Il a également réalisé de nombreux travaux pionniers sur l'IA pour la découverte de médicaments, notamment le premier cadre d'apprentissage automatique à source ouverte pour la découverte de médicaments, TorchDrug et TorchProtein.

Étudiants actuels

Huiyu Cai

Doctorat - UdeM

huiyu.cai@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Rabusseau

Xixian Liu

Doctorat - Université de Montréal

xixian.liu@mila.quebec

Jiarui Lu

Doctorat - UdeM

jiarui.lu@mila.quebec

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Gauthier Gidel

lpxhonneux@gmail.com

Jinxi Yang

Collaborateur·rice de recherche - Wuhan University

jinxi.yang@mila.quebec

Xinyu Yuan

Doctorat - UdeM

xinyu.yuan@mila.quebec

Zhihao Zhan

Doctorat - UdeM

zhihao.zhan@mila.quebec

Zuobai Zhang

Doctorat - UdeM

zuobai.zhang@mila.quebec

Doctorat - HEC

ya-shi.zhang@mila.quebec

Jianan Zhao

Doctorat - UdeM

jianan.zhao@mila.quebec

Publications

CO emission predictions in municipal solid waste incineration based on reduced depth features and long short-term memory optimization

Runyu Zhang

Heng Xia

Xiaotong Pan

Wen Yu

JunFei Qiao

2024-01-07

Neural computing & applications (Print) (publié)

CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization

Wenzheng Hu

Ning Liu

Zhengping Che

Mingyang Li

Changshui Zhang

Jianqiang Wang

Deep convolutional neural networks are shown to be overkill with high parametric and computational redundancy in many application scenarios,… (voir plus) and an increasing number of works have explored model pruning to obtain lightweight and efficient networks. However, most existing pruning approaches are driven by empirical heuristics and rarely consider the joint impact of channels, leading to unguaranteed and suboptimal performance. In this article, we propose a novel channel pruning method via class-aware trace ratio optimization (CATRO) to reduce the computational burden and accelerate the model inference. Utilizing class information from a few samples, CATRO measures the joint impact of multiple channels by feature space discriminations and consolidates the layerwise impact of preserved channels. By formulating channel pruning as a submodular set function maximization problem, CATRO solves it efficiently via a two-stage greedy iterative optimization procedure. More importantly, we present theoretical justifications on convergence of CATRO and performance of pruned networks. Experimental results demonstrate that CATRO achieves higher accuracy with similar computation cost or lower computation cost with similar accuracy than other state-of-the-art channel pruning algorithms. In addition, because of its class-aware property, CATRO is suitable to prune efficient networks adaptively for various classification subtasks, enhancing handy deployment and usage of deep networks in real-world applications.

2023-12-31

IEEE Trans. Neural Networks Learn. Syst. (publié)

arxiv.org

Multi-reservoir ESN-based prediction strategy for dynamic multi-objective optimization

Cuili Yang

Danlei Wang

JunFei Qiao

Wen Yu

2023-12-31

Information Sciences (publié)

Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning

Heng Xia

Wen Yu

JunFei Qiao

Dioxin (DXN) is a persistent organic pollutant produced from municipal solid waste incineration (MSWI) processes. It is a crucial environmen… (voir plus)tal indicator to minimize emission concentration by using optimization control, but it is difficult to monitor in real time. Aiming at online soft-sensing of DXN emission, a novel fuzzy tree broad learning system (FTBLS) is proposed, which includes offline training and online measurement. In the offline training part, weighted k-means is presented to construct a typical sample pool for reduced learning costs of offline and online phases. Moreover, the novel FTBLS, which contains a feature mapping layer, enhance layer, and increment layer, by replacing the fuzzy decision tree with neurons applied to construct the offline model. In the online measurement part, recursive principal component analysis is used to monitor the time-varying characteristic of the MSWI process. To measure DXN emission, offline FTBLS is reused for normal samples; for drift samples, fast incremental learning is used for online updates. A DXN data from the actual MSWI process is employed to prove the usefulness of FTBLS, where the RMSE of training and testing data are 0.0099 and 0.0216, respectively. This result shows that FTBLS can effectively realize DXN online prediction.

2023-12-31

IEEE Transactions on Industrial Informatics (publié)

Tree Broad Learning System for Small Data Modeling.

Heng Xia

Wen Yu

JunFei Qiao

Broad learning system based on neural network (BLS-NN) has poor efficiency for small data modeling with various dimensions. Tree-based BLS (… (voir plus)TBLS) is designed for small data modeling by introducing nondifferentiable modules and an ensemble strategy to the traditional broad learning system (BLS). TBLS replaces the neurons of BLS with the tree modules to map the input data. Moreover, we present three new TBLS variant methods and their incremental learning implementations, which are motivated by deep, broad, and ensemble learning. Their major distinction is reflected in the incremental learning strategies based on: 1) mean square error (mse); 2) pseudo-inverse; and 3) pseudo-inverse theory and stack representation. Therefore, this study further explores the domain of BLS based on the nondifferentiable modules. The simulations are compared with some state-of-the-art (SOTA) BLS-NN and tree methods under high-, medium-, and low-dimensional benchmark datasets. Results show that the proposed method outperforms the BLS-NN, and the modeling accuracy is remarkably improved with the small training data of the proposed TBLS.

2023-12-31

IEEE Trans. Neural Networks Learn. Syst. (publié)

Giant Correlated Gap and Possible Room-Temperature Correlated States in Twisted Bilayer MoS_{2}.

Fanfan Wu

Qiaoling Xu

Qinqin Wang

Yanbang Chu

Li Li

Jieying Liu

Jinpeng Tian

Yiru Ji

Le Liu

Yalong Yuan

Zhiheng Huang

Jiaojiao Zhao

Xiaozhou Zan

Kenji Watanabe

Takashi Taniguchi

Dongxia Shi

Gangxu Gu

Yang Xu

Lede Xian … (voir 3 de plus)

Wei Yang

Luojun Du

Guangyu Zhang

Moiré superlattices have emerged as an exciting condensed-matter quantum simulator for exploring the exotic physics of strong electronic co… (voir plus)rrelations. Notable progress has been witnessed, but such correlated states are achievable usually at low temperatures. Here, we report evidence of possible room-temperature correlated electronic states and layer-hybridized SU(4) model simulator in AB-stacked MoS_{2} homobilayer moiré superlattices. Correlated insulating states at moiré band filling factors v=1, 2, 3 are unambiguously established in twisted bilayer MoS_{2}. Remarkably, the correlated electronic state at v=1 shows a giant correlated gap of ∼126 meV and may persist up to a record-high critical temperature over 285 K. The realization of a possible room-temperature correlated state with a large correlated gap in twisted bilayer MoS_{2} can be understood as the cooperation effects of the stacking-specific atomic reconstruction and the resonantly enhanced interlayer hybridization, which largely amplify the moiré superlattice effects on electronic correlations. Furthermore, extreme large nonlinear Hall responses up to room temperature are uncovered near correlated electronic states, demonstrating the quantum geometry of moiré flat conduction band.

2023-12-17

Physical Review Letters (publié)

Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing

Shengchao Liu

Weili Nie

Chengpeng Wang

Jiarui Lu

Zhuoran Qiao

Ling Liu

Chaowei Xiao

Animashree Anandkumar

There is increasing adoption of artificial intelligence in drug discovery. However, existing studies use machine learning to mainly utilize … (voir plus)the chemical structures of molecules but ignore the vast textual knowledge available in chemistry. Incorporating textual knowledge enables us to realize new drug design objectives, adapt to text-based instructions and predict complex biological activities. Here we present a multi-modal molecule structure–text model, MoleculeSTM, by jointly learning molecules’ chemical structures and textual descriptions via a contrastive learning strategy. To train MoleculeSTM, we construct a large multi-modal dataset, namely, PubChemSTM, with over 280,000 chemical structure–text pairs. To demonstrate the effectiveness and utility of MoleculeSTM, we design two challenging zero-shot tasks based on text instructions, including structure–text retrieval and molecule editing. MoleculeSTM has two main properties: open vocabulary and compositionality via natural language. In experiments, MoleculeSTM obtains the state-of-the-art generalization ability to novel biochemical concepts across various benchmarks. Machine learning methods in cheminformatics have made great progress in using chemical structures of molecules, but a large portion of textual information remains scarcely explored. Liu and colleagues trained MoleculeSTM, a foundation model that aligns the structure and text modalities through contrastive learning, and show its utility on the downstream tasks of structure–text retrieval, text-guided editing and molecular property prediction.

2023-12-17

Nature Machine Intelligence (inconnu)

arxiv.org

A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs

Ming Zhang

Maxime Gazeau

Reasoning on large-scale knowledge graphs has been long dominated by embedding methods. While path-based methods possess the inductive capac… (voir plus)ity that embeddings lack, their scalability is limited by the exponential number of paths. Here we present A*Net, a scalable path-based method for knowledge graph reasoning. Inspired by the A* algorithm for shortest path problems, our A*Net learns a priority function to select important nodes and edges at each iteration, to reduce time and memory footprint for both training and inference. The ratio of selected nodes and edges can be specified to trade off between performance and efficiency. Experiments on both transductive and inductive knowledge graph reasoning benchmarks show that A*Net achieves competitive performance with existing state-of-the-art path-based methods, while merely visiting 10% nodes and 10% edges at each iteration. On a million-scale dataset ogbl-wikikg2, A*Net not only achieves a new state-of-the-art result, but also converges faster than embedding methods. A*Net is the first path-based method for knowledge graph reasoning at such scale.

2023-12-11

Neural Information Processing Systems (Accept (poster))

DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing

Yangtian Zhang

Zuobai Zhang

Bozitao Zhong

Sanchit Misra

Proteins play a critical role in carrying out biological functions, and their 3D structures are essential in determining their functions. Ac… (voir plus)curately predicting the conformation of protein side-chains given their backbones is important for applications in protein structure prediction, design and protein-protein interactions. Traditional methods are computationally intensive and have limited accuracy, while existing machine learning methods treat the problem as a regression task and overlook the restrictions imposed by the constant covalent bond lengths and angles. In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space. To avoid issues arising from simultaneous perturbation of all four torsional angles, we propose autoregressively generating the four torsional angles from

2023-12-11

Neural Information Processing Systems (Accept (poster))

GAUCHE: A Library for Gaussian Processes in Chemistry

Ryan-Rhys Griffiths

Leo Klarner

Henry Moss

Aditya Ravuri

Sang Truong

Bojana Rankovic

Samuel Stanton

Yuanqi Du

Arian Jamasb

Gary Tom

Julius Schwartz

Austin Tripp

Aryan Deshwal

Gregory Kell

Anthony Bourached

Alex J. Chan

Jacob Moss

Chengzhi Guo

Simon Frieder

Alpha A. Lee … (voir 8 de plus)

Philippe Schwaller

Johannes Durholt

Saudamini Chaurasia

Ji Won Park

Felix Strieth-Kalthoff

Bingqing Cheng

Alán Aspuru-Guzik

We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine… (voir plus) learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to chemical representations, however, is nontrivial, necessitating kernels defined over structured inputs such as graphs, strings and bit vectors. By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry. Motivated by scenarios frequently encountered in experimental chemistry, we showcase applications for GAUCHE in molecular discovery and chemical reaction optimisation. The codebase is made available at https://github.com/leojklarner/gauche

2023-12-11

Conference on Neural Information Processing Systems (Accept (poster))

Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction

Zuobai Zhang

Minghao Xu

Aurelie Lozano

Vijil Chenthamarakshan

Payel Das

Self-supervised pre-training methods on proteins have recently gained attention, with most approaches focusing on either protein sequences o… (voir plus)r structures, neglecting the exploration of their joint distribution, which is crucial for a comprehensive understanding of protein functions by integrating co-evolutionary information and structural characteristics. In this work, inspired by the success of denoising diffusion models in generative tasks, we propose the DiffPreT approach to pre-train a protein encoder by sequence-structure joint diffusion modeling. DiffPreT guides the encoder to recover the native protein sequences and structures from the perturbed ones along the joint diffusion trajectory, which acquires the joint distribution of sequences and structures. Considering the essential protein conformational variations, we enhance DiffPreT by a method called Siamese Diffusion Trajectory Prediction (SiamDiff) to capture the correlation between different conformers of a protein. SiamDiff attains this goal by maximizing the mutual information between representations of diffusion trajectories of structurally-correlated conformers. We study the effectiveness of DiffPreT and SiamDiff on both atom- and residue-level structure-based protein understanding tasks. Experimental results show that the performance of DiffPreT is consistently competitive on all tasks, and SiamDiff achieves new state-of-the-art performance, considering the mean ranks on all tasks. Our implementation is available at https://github.com/DeepGraphLearning/SiamDiff.

2023-12-11

Conference on Neural Information Processing Systems (Accept (spotlight))

Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials

Shengchao Liu

Weitao Du

Yanjing Li

Zhuoxinran Li

Zhiling Zheng

Chenru Duan

Zhiming Ma

Omar Yaghi

Anima Anandkumar

Christian Borgs

Jennifer Chayes

Hongyu Guo

Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific comm… (voir plus)unities, particularly in the domains of chemistry, biology, and material discovery. For these scientific problems, molecules serve as the fundamental building blocks, and machine learning has emerged as a highly effective and powerful tool for modeling their geometric structures. Nevertheless, due to the rapidly evolving process of the field and the knowledge gap between science (e.g., physics, chemistry, & biology) and machine learning communities, a benchmarking study on geometrical representation for such data has not been conducted. To address such an issue, in this paper, we first provide a unified view of the current symmetry-informed geometric methods, classifying them into three main categories: invariance, equivariance with spherical frame basis, and equivariance with vector frame basis. Then we propose a platform, coined Geom3D, which enables benchmarking the effectiveness of geometric strategies. Geom3D contains 16 advanced symmetry-informed geometric representation models and 14 geometric pretraining methods over 46 diverse datasets, including small molecules, proteins, and crystalline materials. We hope that Geom3D can, on the one hand, eliminate barriers for machine learning researchers interested in exploring scientific problems; and, on the other hand, provide valuable guidance for researchers in computational chemistry, structural biology, and materials science, aiding in the informed selection of representation techniques for specific applications.

2023-12-11

Conference on Neural Information Processing Systems (Accept (Poster))