Publications

Adult neurogenesis acts as a neural regularizer

Lina M. Tran

Adam Santoro

Lulu Liu

Sheena A. Josselyn

Blake Richards

Paul W. Frankland

2022-11-02

Proceedings of the National Academy of Sciences of the United States of America (published)

Automatic measure and normalization of spinal cord cross-sectional area using the pontomedullary junction

Sandrine Bédard

Julien Cohen-Adad

2022-11-02

Frontiers in Neuroimaging (published)

A General-Purpose Neural Architecture for Geospatial Systems

Nasim Rahaman

Martin Weiss

Frederik Träuble

Francesco Locatello

Alexandre Lacoste

Chris Pal

Li Erran Li

Bernhard Schölkopf

2022-11-02

OpenReview.net/Anonymous_Preprint (unknown)

openreview.net

Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions

Chanakya Ajit Ekbote

Moksh J. Jain

Payel Das

Generative Flow Networks (GFlowNets) have demonstrated significant performance improvements for generating diverse discrete objects …

2022-11-01

ArXiv (preprint)

Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning

Riashat Islam

Hongyu Zang

Anirudh Goyal

Alex Lamb

Kenji Kawaguchi

Xin Li

Romain Laroche

Remi Tachet des Combes

2022-11-01

ArXiv (preprint)

Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: A systematic review and meta-analysis of standardized population-based studies

Isabel Bergeri

Mairead Whelan

Harriet Ware

Lorenzo Subissi

Anthony Nardone

Hannah C. Lewis

Zihan Li

Xiaomeng Ma

Marta Valenciano

Brianna Cheng

Lubna Al Ariqi

Arash Rashidian

Joseph Okeibunor

Tasnim Azim

Pushpa Wijesinghe

Linh-Vi Le

Aisling Vaughan

Richard Pebody

Andrea Vicari

Tingting Yan … (see 9 more)

Mercedes Yanes-Lane

Christian Cao

David A. Clifton

Matthew P. Cheng

Jesse Papenburg

David Buckeridge

Niklas Bobrovitz

Rahul K. Arora

Maria D Van Kerkhove

2022-11-01

PLoS Medicine (published)

Successive-Cancellation Decoding of Reed-Muller Codes With Fast Hadamard Transform

Nghia Doan

Seyyed Ali Hashemi

Warren Gross

A novel permuted fast successive-cancellation list decoding algorithm with fast Hadamard transform (FHT-FSCL) is presented. The proposed dec… (see more)oder initializes

2022-11-01

IEEE Transactions on Vehicular Technology (published)

Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints

Jose Gallego-Posada

Juan Ramirez

Akram Erraqabi

Simon Lacoste-Julien

The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models,… (see more) this observation has motivated extensive research on learning sparse models. In this work, we focus on the task of controlling the level of sparsity when performing sparse learning. Existing methods based on sparsity-inducing penalties involve expensive trial-and-error tuning of the penalty factor, thus lacking direct control of the resulting model sparsity. In response, we adopt a constrained formulation: using the gate mechanism proposed by Louizos et al. (2018), we formulate a constrained optimization problem where sparsification is guided by the training objective and the desired sparsity target in an end-to-end fashion. Experiments on CIFAR-{10, 100}, TinyImageNet, and ImageNet using WideResNet and ResNet{18, 50} models validate the effectiveness of our proposal and demonstrate that we can reliably achieve pre-determined sparsity targets without compromising on predictive performance.

openreview.net

MAgNet: Mesh Agnostic Neural PDE Solver

Oussama Boussif

Dan Assouline

Loubna Benabbou

The computational complexity of classical numerical methods for solving Partial Differential Equations (PDE) scales significantly as the res… (see more)olution increases. As an important example, climate predictions require fine spatio-temporal resolutions to resolve all turbulent scales in the fluid simulations. This makes the task of accurately resolving these scales computationally out of reach even with modern supercomputers. As a result, current numerical modelers solve PDEs on grids that are too coarse (3km to 200km on each side), which hinders the accuracy and usefulness of the predictions. In this paper, we leverage the recent advances in Implicit Neural Representations (INR) to design a novel architecture that predicts the spatially continuous solution of a PDE given a spatial position query. By augmenting coordinate-based architectures with Graph Neural Networks (GNN), we enable zero-shot generalization to new non-uniform meshes and long-term predictions up to 250 frames ahead that are physically consistent. Our Mesh Agnostic Neural PDE Solver (MAgNet) is able to make accurate predictions across a variety of PDE simulation datasets and compares favorably with existing baselines. Moreover, MAgNet generalizes well to different meshes and resolutions up to four times those trained on.

openreview.net

Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation

Brennan Nichyporuk

Jillian L. Cardinell

Justin Szeto

Raghav Mehta

Jean-Pierre R. Falet

Douglas Arnold

Sotirios A. Tsaftaris

Tal Arbel

Generalization is an important attribute of machine learning models, particularly for those that are to be deployed in a medical context, wh… (see more)ere unreliable predictions can have real world consequences. While the failure of models to generalize across datasets is typically attributed to a mismatch in the data distributions, performance gaps are often a consequence of biases in the "ground-truth" label annotations. This is particularly important in the context of medical image segmentation of pathological structures (e.g. lesions), where the annotation process is much more subjective, and affected by a number underlying factors, including the annotation protocol, rater education/experience, and clinical aims, among others. In this paper, we show that modeling annotation biases, rather than ignoring them, poses a promising way of accounting for differences in annotation style across datasets. To this end, we propose a generalized conditioning framework to (1) learn and account for different annotation styles across multiple datasets using a single model, (2) identify similar annotation styles across different datasets in order to permit their effective aggregation, and (3) fine-tune a fully trained model to a new annotation style with just a few samples. Next, we present an image-conditioning approach to model annotation styles that correlate with specific image features, potentially enabling detection biases to be more easily identified.

2022-10-31

ArXiv (preprint)

When Do We Need GNN for Node Classification?

Sitao Luan

Chenqing Hua

Qincheng Lu

Jiaqi Zhu

Xiao-Wen Chang

Doina Precup

2022-10-30

arXiv.org (preprint)