Publications

Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions
Chanakya Ajit Ekbote
Moksh J. Jain
Payel Das
Yoshua Bengio
Generative Flow Networks (GFlowNets) have demonstrated significant performance improvements for generating diverse discrete objects …
2022-10-31
ArXiv (prépublication)
doi.org
arxiv.org
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
Yoshua Bengio
Remi Tachet des Combes
2022-10-31
ArXiv (prépublication)
doi.org
arxiv.org
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 … (voir 9 de plus)
Mercedes Yanes-Lane
Christian Cao
David A. Clifton
Matthew P. Cheng
Jesse Papenburg
David L Buckeridge
Niklas Bobrovitz
Rahul K. Arora
Maria D Van Kerkhove
2022-10-31
PLoS Medicine (publié)
doi.org
Successive-Cancellation Decoding of Reed-Muller Codes With Fast Hadamard Transform
Nghia Doan
Seyyed Ali Hashemi
Warren J. Gross
A novel permuted fast successive-cancellation list decoding algorithm with fast Hadamard transform (FHT-FSCL) is presented. The proposed dec… (voir plus)oder initializes
2022-10-31
IEEE Transactions on Vehicular Technology (publié)
doi.org
arxiv.org
Undiscounted Recursive Path Choice Models: Convergence Properties and Algorithms
Tien Mai
Emma Frejinger
2022-10-31
Transportation Science (publié)
doi.org
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints
Jose Gallego-Posada
Juan Ramirez
Akram Erraqabi
Yoshua Bengio
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,… (voir plus) 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.
2022-10-30
NeurIPS.cc/2022/Conference (accepté)
doi.org
openreview.net
MAgNet: Mesh Agnostic Neural PDE Solver
Oussama Boussif
Dan Assouline
Loubna Benabbou
Yoshua Bengio
The computational complexity of classical numerical methods for solving Partial Differential Equations (PDE) scales significantly as the res… (voir plus)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.
2022-10-30
NeurIPS.cc/2022/Conference (accepté)
doi.org
openreview.net
Rethinking Generalization: The Impact of Annotation Style on Medical Image Segmentation
Brennan Nichyporuk
Jillian Cardinell
Justin Szeto
Raghav Mehta
Jean-Pierre R. Falet
Douglas L. 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… (voir plus)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.
2022-10-30
ArXiv (prépublication)
doi.org
arxiv.org
When Do We Need Graph Neural Networks for Node Classification?
Sitao Luan
Chenqing Hua
Qincheng Lu
Jiaqi Zhu
Xiao-Wen Chang
Doina Precup
2022-10-29
arXiv.org (prépublication)
doi.org
arxiv.org
Notational Programming for Notebook Environments: A Case Study with Quantum Circuits
Ian Arawjo
Anthony DeArmas
Michael Roberts
Shrutarshi Basu
Tapan Parikh
We articulate a vision for computer programming that includes pen-based computing, a paradigm we term notational programming. Notational pro… (voir plus)gramming blurs contexts: certain typewritten variables can be referenced in handwritten notation and vice-versa. To illustrate this paradigm, we developed an extension, Notate, to computational notebooks which allows users to open drawing canvases within lines of code. As a case study, we explore quantum programming and designed a notation, Qaw, that extends quantum circuit notation with abstraction features, such as variable-sized wire bundles and recursion. Results from a usability study with novices suggest that users find our core interaction of implicit cross-context references intuitive, but suggests further improvements to debugging infrastructure, interface design, and recognition rates. Throughout, we discuss questions raised by the notational paradigm, including a shift from ‘recognition’ of notations to ‘reconfiguration’ of practices and values around programming, and from ‘sketching’ to writing and drawing, or what we call ‘notating.’
2022-10-27
ACM Symposium on User Interface Software and Technology (publié)
doi.org
Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?
Enamundram Naga Karthik
Anne Kerbrat
Pierre Labauge
Tobias Granberg
Jason F. Talbott
Daniel S Reich
Massimo Filippi
Rohit Bakshi
Virginie Callot
A. Chandar
Julien Cohen-Adad
Segmentation of Multiple Sclerosis (MS) lesions is a challenging problem. Several deep-learning-based methods have been proposed in recent y… (voir plus)ears. However, most methods tend to be static, that is, a single model trained on a large, specialized dataset, which does not generalize well. Instead, the model should learn across datasets arriving sequentially from different hospitals by building upon the characteristics of lesions in a continual manner. In this regard, we explore experience replay, a well-known continual learning method, in the context of MS lesion segmentation across multi-contrast data from 8 different hospitals. Our experiments show that replay is able to achieve positive backward transfer and reduce catastrophic forgetting compared to sequential fine-tuning. Furthermore, replay outperforms the multi-domain training, thereby emerging as a promising solution for the segmentation of MS lesions. The code is available at this link: https://github.com/naga-karthik/continual-learning-ms
2022-10-26
ArXiv (prépublication)
doi.org
arxiv.org
Arc travel time and path choice model estimation subsumed
Sobhan Mohammadpour
Emma Frejinger
We propose a method for maximum likelihood estimation of path choice model parameters and arc travel time using data of different levels of… (voir plus) granularity. Hitherto these two tasks have been tackled separately under strong assumptions. Using a small example, we illustrate that this can lead to biased results. Results on both real (New York yellow cab) and simulated data show strong performance of our method compared to existing baselines. models and loss functions. It is designed to estimate arc travel time and path choice model parameters simultaneously. We showed that by marginalizing the unobserved variables and using stochastic gradient estimates, we obtain a maximum likelihood estimation even for observations at different level of granularity. We showed that we can mix different data type when computing the MLE without needing to use a linear combination of losses as
2022-10-24
ArXiv (prépublication)
doi.org
arxiv.org