Publications

SORBET: A Siamese Network for Ontology Embeddings Using a Distance-Based Regression Loss and BERT

Francis Gosselin

Amal Zouaq

2023-10-27

The Semantic Web – ISWC 2023 (publié)

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions

Alvaro Carbonero

Alexandre AGM Duval

Victor Schmidt

Santiago Miret

Alex Hernandez-Garcia

Yoshua Bengio

David Rolnick

The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorpor… (voir plus)ate the geometric configuration of all atoms. However, in practice not all this information may be readily available, e.g.~when evaluating the potentially unknown binding of adsorbates to catalyst. In this paper, we investigate whether it is possible to predict a system's relaxed energy in the OC20 dataset while ignoring the relative position of the adsorbate with respect to the electro-catalyst. We consider SchNet, DimeNet++ and FAENet as base architectures and measure the impact of four modifications on model performance: removing edges in the input graph, pooling independent representations, not sharing the backbone weights and using an attention mechanism to propagate non-geometric relative information. We find that while removing binding site information impairs accuracy as expected, modified models are able to predict relaxed energies with remarkably decent MAE. Our work suggests future research directions in accelerated materials discovery where information on reactant configurations can be reduced or altogether omitted.

2023-10-27

NeurIPS.cc/2023/Workshop/AI4Mat (poster)

Towards equilibrium molecular conformation generation with GFlowNets

Alexandra Volokhova

Michał Koziarski

Alex Hernandez-Garcia

Cheng-Hao Liu

Santiago Miret

Pablo Lemos

Luca Thiede

Zichao Yan

Alan Aspuru-Guzik

Yoshua Bengio

Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this pa… (voir plus)per we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimation methods of different fidelity and discovers a diverse set of low-energy conformations for highly flexible drug-like molecules. We demonstrate that GFlowNet can reproduce molecular potential energy surfaces by sampling proportionally to the Boltzmann distribution.

2023-10-27

NeurIPS.cc/2023/Workshop/AI4Mat (poster)

Interacting Diffusion Processes for Event Sequence Forecasting

Mai Zeng

Florence Regol

Mark Coates

Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time… (voir plus) intervals, but their sequential nature can hamper performance for long-horizon forecasts. To address this, we introduce a novel approach that incorporates a diffusion generative model. The model facilitates sequence-to-sequence prediction, allowing multi-step predictions based on historical event sequences. In contrast to previous approaches, our model directly learns the joint probability distribution of types and inter-arrival times for multiple events. This allows us to fully leverage the high dimensional modeling capability of modern generative models. Our model is composed of two diffusion processes, one for the time intervals and one for the event types. These processes interact through their respective denoising functions, which can take as input intermediate representations from both processes, allowing the model to learn complex interactions. We demonstrate that our proposal outperforms state-of-the-art baselines for long-horizon forecasting of TPP.

2023-10-26

ArXiv (prépublication)

Managing AI Risks in an Era of Rapid Progress

Yoshua Bengio

Geoffrey Hinton

Andrew Yao

Dawn Song

Pieter Abbeel

Yuval Noah Harari

Trevor Darrell

Ya-Qin Zhang

Lan Xue

Shai Shalev-Shwartz

Gillian K. Hadfield

Jeff Clune

Tegan Maharaj

Frank Hutter

Atilim Güneş Baydin

Sheila McIlraith

Qiqi Gao

Ashwin Acharya

David Scott Krueger

Anca Dragan … (voir 5 de plus)

Philip Torr

Stuart Russell

Daniel Kahneman

Jan Brauner

Sören Mindermann

2023-10-26

Science (publié)

Surrogate Minimization: An Optimization Algorithm for Training Large Neural Networks with Model Parallelism

Reza Asad

Reza Babanezhad Harikandeh

Issam Hadj Laradji

Nicolas Le Roux

Sharan Vaswani

2023-10-26

NeurIPS.cc/2023/Workshop/OPT (poster)

The value of standards for health datasets in artificial intelligence-based applications

Anmol Arora

Joseph E. Alderman

Joanne Palmer

Shaswath Ganapathi

Elinor Laws

Melissa D. McCradden

Lauren Oakden-Rayner

Stephen R. Pfohl

Marzyeh Ghassemi

Francis McKay

Darren Treanor

Negar Rostamzadeh

Bilal Mateen

Jacqui Gath

Adewole O. Adebajo

Stephanie Kuku

Rubeta Matin

Katherine Heller

Elizabeth Sapey

Neil J. Sebire … (voir 4 de plus)

Heather Cole-Lewis

Melanie Calvert

Alastair Denniston

Xiaoxuan Liu

2023-10-26

Nature Medicine (publié)

CL-MASR: A Continual Learning Benchmark for Multilingual ASR

Luca Della Libera

Pooneh Mousavi

Salah Zaiem

Cem Subakan

Mirco Ravanelli

2023-10-25

ArXiv (prépublication)

PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design

Chuanrui Wang

Bozitao Zhong

Zuobai Zhang

Narendra Chaudhary

Sanchit Misra

Jian Tang

Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years. However, a univers… (voir plus)ally accepted method for evaluation has not been established, since the wet-lab validation can be overly time-consuming for the development of new algorithms, and the

2023-10-25

NeurIPS.cc/2023/Workshop/AI4D3 (poster)

PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design

Chuanrui Wang

Bozitao Zhong

Zuobai Zhang

Narendra Chaudhary

Sanchit Misra

Jian Tang

Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years. However, a univers… (voir plus)ally accepted method for evaluation has not been established, since the wet-lab validation can be overly time-consuming for the development of new algorithms, and the

2023-10-25

NeurIPS.cc/2023/Workshop/AI4D3 (poster)

Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms

Michael Perlmutter

Alexander Tong

Feng Gao

Guy Wolf

Matthew Hirn

The scattering transform is a multilayered wavelet-based deep learning architecture that acts as a model of convolutional neural networks. R… (voir plus)ecently, several works have introduced generalizations of the scattering transform for non-Euclidean settings such as graphs. Our work builds upon these constructions by introducing windowed and non-windowed geometric scattering transforms for graphs based upon a very general class of asymmetric wavelets. We show that these asymmetric graph scattering transforms have many of the same theoretical guarantees as their symmetric counterparts. As a result, the proposed construction unifies and extends known theoretical results for many of the existing graph scattering architectures. In doing so, this work helps bridge the gap between geometric scattering and other graph neural networks by introducing a large family of networks with provable stability and invariance guarantees. These results lay the groundwork for future deep learning architectures for graph-structured data that have learned filters and also provably have desirable theoretical properties.

2023-10-25

SIAM Journal on Mathematics of Data Science (publié)