Publications

Equivariance with Learned Canonicalization Functions
Sékou-Oumar Kaba
Arnab Kumar Mondal
Yan Zhang
Yoshua Bengio
Siamak Ravanbakhsh
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
Alexandre AGM Duval
Victor Schmidt
Alex Hernandez-Garcia
Santiago Miret
Fragkiskos D. Malliaros
Yoshua Bengio
David Rolnick
Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to speci… (voir plus)fic symmetries. While graph neural networks (GNNs) have proven successful in such tasks, they enforce symmetries via the model architecture, which often reduces their expressivity, scalability and comprehensibility. In this paper, we introduce (1) a flexible framework relying on stochastic frame-averaging (SFA) to make any model E(3)-equivariant or invariant through data transformations. (2) FAENet: a simple, fast and expressive GNN, optimized for SFA, that processes geometric information without any symmetrypreserving design constraints. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling on the OC20 dataset (S2EF, IS2RE) as well as common molecular modeling tasks (QM9, QM7-X). A package implementation is available at https://faenet.readthedocs.io.
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
Flexible Phase Dynamics for Bio-Plausible Contrastive Learning
Ezekiel Williams
Colin Bredenberg
Guillaume Lajoie
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Songtao Liu
Zhengkai Tu
Minkai Xu
Zuobai Zhang
Lu Lin
Rex Ying
Zhitao Ying
Jian Tang
Peilin Zhao
Dinghao Wu
Retrosynthetic planning aims to devise a complete multi-step synthetic route from starting materials to a target molecule. Current strategie… (voir plus)s use a decoupled approach of single-step retrosynthesis models and search algorithms, taking only the product as the input to predict the reactants for each planning step and ignoring valuable context information along the synthetic route. In this work, we propose a novel framework that utilizes context information for improved retrosynthetic planning. We view synthetic routes as reaction graphs and propose to incorporate context through three principled steps: encode molecules into embeddings, aggregate information over routes, and readout to predict reactants. Our approach is the first attempt to utilize in-context learning for retrosynthesis prediction in retrosynthetic planning. The entire framework can be efficiently optimized in an end-to-end fashion and produce more practical and accurate predictions. Comprehensive experiments demonstrate that by fusing in the context information over routes, our model significantly improves the performance of retrosynthetic planning over baselines that are not context-aware, especially for long synthetic routes. Code is available at https://github.com/SongtaoLiu0823/FusionRetro.
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
proceedings.mlr.press
openreview.net
GFlowNet-EM for Learning Compositional Latent Variable Models
Edward J Hu
Nikolay Malkin
Moksh J. Jain
Katie E Everett
Alexandros Graikos
Yoshua Bengio
Latent variable models (LVMs) with discrete compositional latents are an important but challenging setting due to a combinatorially large nu… (voir plus)mber of possible configurations of the latents. A key tradeoff in modeling the posteriors over latents is between expressivity and tractable optimization. For algorithms based on expectation-maximization (EM), the E-step is often intractable without restrictive approximations to the posterior. We propose the use of GFlowNets, algorithms for sampling from an unnormalized density by learning a stochastic policy for sequential construction of samples, for this intractable E-step. By training GFlowNets to sample from the posterior over latents, we take advantage of their strengths as amortized variational inference algorithms for complex distributions over discrete structures. Our approach, GFlowNet-EM, enables the training of expressive LVMs with discrete compositional latents, as shown by experiments on non-context-free grammar induction and on images using discrete variational autoencoders (VAEs) without conditional independence enforced in the encoder.
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
Shengchao Liu
weitao Du
Zhi-Ming Ma
Hongyu Guo
Jian Tang
Molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Naturally, molecules can be re… (voir plus)presented as 2D topological graphs or 3D geometric point clouds. Although most existing pertaining methods focus on merely the single modality, recent research has shown that maximizing the mutual information (MI) between such two modalities enhances the molecule representation ability. Meanwhile, existing molecule multi-modal pretraining approaches approximate MI based on the representation space encoded from the topology and geometry, thus resulting in the loss of critical structural information of molecules. To address this issue, we propose MoleculeSDE. MoleculeSDE leverages group symmetric (e.g., SE(3)-equivariant and reflection-antisymmetric) stochastic differential equation models to generate the 3D geometries from 2D topologies, and vice versa, directly in the input space. It not only obtains tighter MI bound but also enables prosperous downstream tasks than the previous work. By comparing with 17 pretraining baselines, we empirically verify that MoleculeSDE can learn an expressive representation with state-of-the-art performance on 26 out of 32 downstream tasks.
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
Guided-topic modelling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes
Lakshmipuram Seshadri Swapna
Michael Huang
Yue Li
Cell-type composition is an important indicator of health. We present Guided Topic Model for deconvolution (GTM-decon) to automatically infe… (voir plus)r cell-type-specific gene topic distributions from single-cell RNA-seq data for deconvolving bulk transcriptomes. GTM-decon performs competitively on deconvolving simulated and real bulk data compared with the state-of-the-art methods. Moreover, as demonstrated in deconvolving disease transcriptomes, GTM-decon can infer multiple cell-type-specific gene topic distributions per cell type, which captures sub-cell-type variations. GTM-decon can also use phenotype labels from single-cell or bulk data as a guide to infer phenotype-specific gene distributions. In a nested-guided design, GTM-decon identified cell-type-specific differentially expressed genes from bulk breast cancer transcriptomes.
2023-07-03
bioRxiv (prépublication)
doi.org
Hidden Symmetries of ReLU Networks
J. Grigsby
Elisenda Grigsby
Kathryn Lindsey
David Rolnick
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance
Abdurakhmon Sadiev
Marina Danilova
Eduard Gorbunov
Samuel Horváth
Gauthier Gidel
Pavel Dvurechensky
Alexander Gasnikov
Peter Richtárik
During the recent years the interest of optimization and machine learning communities in high-probability convergence of stochastic optimiza… (voir plus)tion methods has been growing. One of the main reasons for this is that high-probability complexity bounds are more accurate and less studied than in-expectation ones. However, SOTA high-probability non-asymptotic convergence results are derived under strong assumptions such as boundedness of the gradient noise variance or of the objective’s gradient itself. In this paper, we propose several algorithms with high-probability convergence results under less restrictive assumptions. In particular, we derive new high-probability convergence results under the assumption that the gradient/operator noise has bounded central
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
Maximal Initial Learning Rates in Deep ReLU Networks
Gaurav Iyer
Boris Hanin
David Rolnick
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
Mechanistic Mode Connectivity
Ekdeep Singh Lubana
Eric J Bigelow
Robert P. Dick
David Scott Krueger
Hidenori Tanaka
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net
Neural FIM for learning Fisher information metrics from point cloud data
Oluwadamilola Fasina
Guillaume Huguet
Alexander Tong
Yanlei Zhang
Guy Wolf
Maximilian Nickel
Ian Adelstein
Smita Krishnaswamy
Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the under… (voir plus)lying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM’s utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells).
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net