Publications

Repurposing Density Functional Theory to Suit Deep Learning

Alexander Mathiasen

Hatem Helal

Paul Balanca

Kerstin Klaeser

Josef Dean

Carlo Luschi

Dominique Beaini

Andrew William Fitzgibbon

Dominic Masters

Density Functional Theory (DFT) accurately predicts the properties of molecules given their atom types and positions, and often serves as gr… (see more)ound truth for molecular property prediction tasks. Neural Networks (NN) are popular tools for such tasks and are trained on DFT datasets, with the aim to approximate DFT at a fraction of the computational cost. Research in other areas of machine learning has shown that generalisation performance of NNs tends to improve with increased dataset size, however, the computational cost of DFT limits the size of DFT datasets. We present PySCFIPU, a DFT library that allows us to iterate on both dataset generation and NN training. We create QM10X, a dataset with 100M conformers, in 13 hours, on which we subsequently train SchNet in 12 hours. We show that the predictions of SchNet improve solely by increasing training data without incorporating further inductive biases.

2023-07-28

ICML.cc/2023/Workshop/SynS_and_ML (published)

What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?

Oussama Boussif

Ghait Boukachab

Dan Assouline

Stefano Massaroli

Tianle Yuan

Loubna Benabbou

Yoshua Bengio

The global integration of solar power into the electrical grid could have a crucial impact on climate change mitigation, yet poses a challen… (see more)ge due to solar irradiance variability. We present a deep learning architecture which uses spatio-temporal context from satellite data for highly accurate day-ahead time-series forecasting, in particular Global Horizontal Irradiance (GHI). We provide a multi-quantile variant which outputs a prediction interval for each time-step, serving as a measure of forecasting uncertainty. In addition, we suggest a testing scheme that separates easy and difficult scenarios, which appears useful to evaluate model performance in varying cloud conditions. Our approach exhibits robust performance in solar irradiance forecasting, including zero-shot generalization tests at unobserved solar stations, and holds great promise in promoting the effective use of solar power and the resulting reduction of CO

2023-07-28

ICML.cc/2023/Workshop/SynS_and_ML (published)

What if We Enrich day-ahead Solar Irradiance Time Series Forecasting with Spatio-Temporal Context?

Oussama Boussif

Ghait Boukachab

Dan Assouline

Stefano Massaroli

Tianle Yuan

Loubna Benabbou

Yoshua Bengio

2023-07-28

ICML.cc/2023/Workshop/SynS_and_ML (published)

Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition

Catherine Bouchard

Theresa Wiesner

Andréanne Deschênes

Anthony Bilodeau

Benoit Turcotte

Christian Gagné

Flavie Lavoie-Cardinal

2023-07-27

Nature Machine Intelligence (published)

The Canadian Open Neuroscience Platform—An open science framework for the neuroscience community

Rachel J. Harding

Patrick Bermudez

Alexander Bernier

Michael Beauvais

Pierre (Louis) Bellec

Sean Hill

Bartha M. Knoppers

Agâh Karakuzu

Paul Pavlidis

Jean-Baptiste Poline

Jane Roskams

Nikola Stikov

Jessica Stone

Stephen Strother

Conp Consortium

Alan C. Evans

The Canadian Open Neuroscience Platform (CONP) takes a multifaceted approach to enabling open neuroscience, aiming to make research, data, a… (see more)nd tools accessible to everyone, with the ultimate objective of accelerating discovery. Its core infrastructure is the CONP Portal, a repository with a decentralized design, where datasets and analysis tools across disparate platforms can be browsed, searched, accessed, and shared in accordance with FAIR principles. Another key piece of CONP infrastructure is NeuroLibre, a preprint server capable of creating and hosting executable and fully reproducible scientific publications that embed text, figures, and code. As part of its holistic approach, the CONP has also constructed frameworks and guidance for ethics and data governance, provided support and developed resources to help train the next generation of neuroscientists, and has fostered and grown an engaged community through outreach and communications. In this manuscript, we provide a high-level overview of this multipronged platform and its vision of lowering the barriers to the practice of open neuroscience and yielding the associated benefits for both individual researchers and the wider community.

2023-07-27

PLOS Computational Biology (published)

A machine learning framework for neighbor generation in metaheuristic search

De-You Liu

Defeng Liu

Vincent Perreault

Alain Hertz

Andrea Lodi

This paper presents a methodology for integrating machine learning techniques into metaheuristics for solving combinatorial optimization pro… (see more)blems. Namely, we propose a general machine learning framework for neighbor generation in metaheuristic search. We first define an efficient neighborhood structure constructed by applying a transformation to a selected subset of variables from the current solution. Then, the key of the proposed methodology is to generate promising neighbors by selecting a proper subset of variables that contains a descent of the objective in the solution space. To learn a good variable selection strategy, we formulate the problem as a classification task that exploits structural information from the characteristics of the problem and from high-quality solutions. We validate our methodology on two metaheuristic applications: a Tabu Search scheme for solving a Wireless Network Optimization problem and a Large Neighborhood Search heuristic for solving Mixed-Integer Programs. The experimental results show that our approach is able to achieve a satisfactory trade-offs between the exploration of a larger solution space and the exploitation of high-quality solution regions on both applications.

2023-07-26

Frontiers in Applied Mathematics and Statistics (published)

Offline Reinforcement Learning with On-Policy Q-Function Regularization

Laixi Shi

Robert Dadashi

Yuejie Chi

Pablo Samuel Castro

Matthieu. Geist

The core challenge of offline reinforcement learning (RL) is dealing with the (potentially catastrophic) extrapolation error induced by the … (see more)distribution shift between the history dataset and the desired policy. A large portion of prior work tackles this challenge by implicitly/explicitly regularizing the learning policy towards the behavior policy, which is hard to estimate reliably in practice. In this work, we propose to regularize towards the Q-function of the behavior policy instead of the behavior policy itself, under the premise that the Q-function can be estimated more reliably and easily by a SARSA-style estimate and handles the extrapolation error more straightforwardly. We propose two algorithms taking advantage of the estimated Q-function through regularizations, and demonstrate they exhibit strong performance on the D4RL benchmarks.

2023-07-25

ArXiv (preprint)

Parameter-space ReSTIR for Differentiable and Inverse Rendering

Wesley Chang

Venkataram Sivaram

Derek Nowrouzezahrai

Toshiya Hachisuka

Ravi Ramamoorthi

Tzu-Mao Li

Differentiable rendering is frequently used in gradient descent-based inverse rendering pipelines to solve for scene parameters – such as … (see more)reflectance or lighting properties – from target image inputs. Efficient computation of accurate, low variance gradients is critical for rapid convergence. While many methods employ variance reduction strategies, they operate independently on each gradient descent iteration, requiring large sample counts and computation. Gradients may however vary slowly between iterations, leading to unexplored potential benefits when reusing sample information to exploit this coherence. We develop an algorithm to reuse Monte Carlo gradient samples between gradient iterations, motivated by reservoir-based temporal importance resampling in forward rendering. Direct application of this method is not feasible, as we are computing many derivative estimates (i.e., one per optimization parameter) instead of a single pixel intensity estimate; moreover, each of these gradient estimates can affect multiple pixels, and gradients can take on negative values. We address these challenges by reformulating differential rendering integrals in parameter space, developing a new resampling estimator that treats negative functions, and combining these ideas into a reuse algorithm for inverse texture optimization. We significantly reduce gradient error compared to baselines, and demonstrate faster inverse rendering convergence in settings involving complex direct lighting and material textures.

2023-07-23

Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Proceedings (published)

Discovering the Electron Beam Induced Transition Rates for Silicon Dopants in Graphene with Deep Neural Networks in the STEM

Kevin M Roccapriore

Max Schwarzer

Joshua Greaves

Jesse Farebrother

Rishabh Agarwal

Colton Bishop

Maxim Ziatdinov

Igor Mordatch

Ekin Dogus Cubuk

Aaron Courville

Pablo Samuel Castro

Marc Gendron-Bellemare

Sergei V Kalinin

2023-07-22

Microscopy and Microanalysis (published)

CARTIER: Cartographic lAnguage Reasoning Targeted at Instruction Execution for Robots

Nikhil Kakodkar

Dmitriy Rivkin

Bobak H. Baghi

Francois Hogan

Gregory Dudek

2023-07-21

ArXiv (preprint)

Acceleration in Policy Optimization

Veronica Chelu

Tom Zahavy

Arthur Guez

Doina Precup

Sebastian Flennerhag

We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) through predictive and adapt… (see more)ive directions of (functional) policy ascent. Leveraging the connection between policy iteration and policy gradient methods, we view policy optimization algorithms as iteratively solving a sequence of surrogate objectives, local lower bounds on the original objective. We define optimism as predictive modelling of the future behavior of a policy, and hindsight adaptation as taking immediate and anticipatory corrective actions to mitigate accumulating errors from overshooting predictions or delayed responses to change. We use this shared lens to jointly express other well-known algorithms, including model-based policy improvement based on forward search, and optimistic meta-learning algorithms. We show connections with Anderson acceleration, Nesterov's accelerated gradient, extra-gradient methods, and linear extrapolation in the update rule. We analyze properties of the formulation, design an optimistic policy gradient algorithm, adaptive via meta-gradient learning, and empirically highlight several design choices pertaining to acceleration, in an illustrative task.

2023-07-20

EWRL/2023/Workshop (accepted)