Publications

Impact of individual rater style on deep learning uncertainty in medical imaging segmentation
Olivier Vincent
Charley Gros
Julien Cohen-Adad
While multiple studies have explored the relation between inter-rater variability and deep learning model uncertainty in medical segmentatio… (voir plus)n tasks, little is known about the impact of individual rater style. This study quantifies rater style in the form of bias and consistency and explores their impacts when used to train deep learning models. Two multi-rater public datasets were used, consisting of brain multiple sclerosis lesion and spinal cord grey matter segmentation. On both datasets, results show a correlation (
2021-05-04
ArXiv (prépublication)
arxiv.org
arxiv.org
PNS-GAN: Conditional Generation of Peripheral Nerve Signals in the Wavelet Domain via Adversarial Networks
Olivier Tessier-Larivière
Luke Y. Prince
Pascal Fortier-Poisson
Lorenz Wernisch
Oliver Armitage
Emil Hewage
Guillaume Lajoie
Blake Aaron Richards
Simulated datasets of neural recordings are a crucial tool in neural engineering for testing the ability of decoding algorithms to recover k… (voir plus)nown ground-truth. In this work, we introduce PNS-GAN, a generative adversarial network capable of producing realistic nerve recordings conditioned on physiological biomarkers. PNS-GAN operates in the wavelet domain to preserve both the timing and frequency of neural events with high resolution. PNS-GAN generates sequences of scaleograms from noise using a recurrent neural network and 2D transposed convolution layers. PNS-GAN discriminates over stacks of scaleograms with a network of 3D convolution layers. We find that our generated signal reproduces a number of characteristics of the real signal, including similarity in a canonical time-series feature-space, and contains physiologically related neural events including respiration modulation and similar distributions of afferent and efferent signalling.
2021-05-03
IEEE/EMBS Conference on Neural Engineering (publié)
doi.org
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning
Ossama Ahmed
Frederik Träuble
Anirudh Goyal
Alexander Neitz
Manuel Wuthrich
Yoshua Bengio
Bernhard Schölkopf
Stefan Bauer
Despite recent successes of reinforcement learning (RL), it remains a challenge for agents to transfer learned skills to related environment… (voir plus)s. To facilitate research addressing this problem, we propose CausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation environment. The environment is a simulation of an open-source robotic platform, hence offering the possibility of sim-to-real transfer. Tasks consist of constructing 3D shapes from a given set of blocks - inspired by how children learn to build complex structures. The key strength of CausalWorld is that it provides a combinatorial family of such tasks with common causal structure and underlying factors (including, e.g., robot and object masses, colors, sizes). The user (or the agent) may intervene on all causal variables, which allows for fine-grained control over how similar different tasks (or task distributions) are. One can thus easily define training and evaluation distributions of a desired difficulty level, targeting a specific form of generalization (e.g., only changes in appearance or object mass). Further, this common parametrization facilitates defining curricula by interpolating between an initial and a target task. While users may define their own task distributions, we present eight meaningful distributions as concrete benchmarks, ranging from simple to very challenging, all of which require long-horizon planning as well as precise low-level motor control. Finally, we provide baseline results for a subset of these tasks on distinct training curricula and corresponding evaluation protocols, verifying the feasibility of the tasks in this benchmark.
2021-05-02
International Conference on Learning Representations (Poster)
doi.org
openreview.net
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
Chin-wei Huang
Ricky T. Q. Chen
Christos Tsirigotis
Aaron Courville
Flow-based models are powerful tools for designing probabilistic models with tractable density. This paper introduces Convex Potential Flows… (voir plus) (CP-Flow), a natural and efficient parameterization of invertible models inspired by the optimal transport (OT) theory. CP-Flows are the gradient map of a strongly convex neural potential function. The convexity implies invertibility and allows us to resort to convex optimization to solve the convex conjugate for efficient inversion. To enable maximum likelihood training, we derive a new gradient estimator of the log-determinant of the Jacobian, which involves solving an inverse-Hessian vector product using the conjugate gradient method. The gradient estimator has constant-memory cost, and can be made effectively unbiased by reducing the error tolerance level of the convex optimization routine. Theoretically, we prove that CP-Flows are universal density approximators and are optimal in the OT sense. Our empirical results show that CP-Flow performs competitively on standard benchmarks of density estimation and variational inference.
2021-05-02
International Conference on Learning Representations (Poster)
doi.org
openreview.net
Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments
Anirudh Goyal
Alex Lamb
Phanideep Gampa
Philippe Beaudoin
Charles Blundell
Sergey Levine
Yoshua Bengio
Michael Mozer
2021-05-02
International Conference on Learning Representations (Poster)
openreview.net
openreview.net
Integrating Categorical Semantics into Unsupervised Domain Translation
Samuel Lavoie
Faruk Ahmed
Aaron Courville
While unsupervised domain translation (UDT) has seen a lot of success recently, we argue that mediating its translation via categorical sema… (voir plus)ntic features could broaden its applicability. In particular, we demonstrate that categorical semantics improves the translation between perceptually different domains sharing multiple object categories. We propose a method to learn, in an unsupervised manner, categorical semantic features (such as object labels) that are invariant of the source and target domains. We show that conditioning the style encoder of unsupervised domain translation methods on the learned categorical semantics leads to a translation preserving the digits on MNIST
2021-05-02
International Conference on Learning Representations (Poster)
doi.org
openreview.net
Iterated Learning for Emergent Systematicity in VQA
Ankit Vani
Max Schwarzer
Yuchen Lu
Eeshan Dhekane
Aaron Courville
Although neural module networks have an architectural bias towards compositionality, they require gold standard layouts to generalize system… (voir plus)atically in practice. When instead learning layouts and modules jointly, compositionality does not arise automatically and an explicit pressure is necessary for the emergence of layouts exhibiting the right structure. We propose to address this problem using iterated learning, a cognitive science theory of the emergence of compositional languages in nature that has primarily been applied to simple referential games in machine learning. Considering the layouts of module networks as samples from an emergent language, we use iterated learning to encourage the development of structure within this language. We show that the resulting layouts support systematic generalization in neural agents solving the more complex task of visual question-answering. Our regularized iterated learning method can outperform baselines without iterated learning on SHAPES-SyGeT (SHAPES Systematic Generalization Test), a new split of the SHAPES dataset we introduce to evaluate systematic generalization, and on CLOSURE, an extension of CLEVR also designed to test systematic generalization. We demonstrate superior performance in recovering ground-truth compositional program structure with limited supervision on both SHAPES-SyGeT and CLEVR.
2021-05-02
International Conference on Learning Representations (Oral)
doi.org
openreview.net
Learning to Live with Dale's Principle: ANNs with Separate Excitatory and Inhibitory Units
Jonathan Cornford
Damjan Kalajdzievski
Marco Leite
Amélie Lamarquette
Dimitri M. Kullmann
Blake Richards
A bstract The units in artificial neural networks (ANNs) can be thought of as abstraction… (voir plus)s of biological neurons, and ANNs are increasingly used in neuroscience research. However, there are many important differences between ANN units and real neurons. One of the most notable is the absence of Dale’s principle, which ensures that biological neurons are either exclusively excitatory or inhibitory. Dale’s principle is typically left out of ANNs because its inclusion impairs learning. This is problematic, because one of the great advantages of ANNs for neuroscience research is their ability to learn complicated, realistic tasks. Here, by taking inspiration from feedforward inhibitory interneurons in the brain we show that we can develop ANNs with separate populations of excitatory and inhibitory units that learn just as well as standard ANNs. We call these networks Dale’s ANNs (DANNs). We present two insights that enable DANNs to learn well: (1) DANNs are related to normalization schemes, and can be initialized such that the inhibition centres and standardizes the excitatory activity, (2) updates to inhibitory neuron parameters should be scaled using corrections based on the Fisher Information matrix. These results demonstrate how ANNs that respect Dale’s principle can be built without sacrificing learning performance, which is important for future work using ANNs as models of the brain. The results may also have interesting implications for how inhibitory plasticity in the real brain operates.
2021-05-02
ICLR (Poster)
doi.org
openreview.net
Learning Neural Generative Dynamics for Molecular Conformation Generation
Minkai Xu
Shitong Luo
Yoshua Bengio
Jian Peng
Jian Tang
We study how to generate molecule conformations (i.e., 3D structures) from a molecular graph. Traditional methods, such as molecular dynamic… (voir plus)s, sample conformations via computationally expensive simulations. Recently, machine learning methods have shown great potential by training on a large collection of conformation data. Challenges arise from the limited model capacity for capturing complex distributions of conformations and the difficulty in modeling long-range dependencies between atoms. Inspired by the recent progress in deep generative models, in this paper, we propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph. We propose a method combining the advantages of both flow-based and energy-based models, enjoying: (1) a high model capacity to estimate the multimodal conformation distribution; (2) explicitly capturing the complex long-range dependencies between atoms in the observation space. Extensive experiments demonstrate the superior performance of the proposed method on several benchmarks, including conformation generation and distance modeling tasks, with a significant improvement over existing generative models for molecular conformation sampling.
2021-05-02
International Conference on Learning Representations (Poster)
doi.org
openreview.net
Learning Robust State Abstractions for Hidden-Parameter Block MDPS
Amy Zhang
Shagun Sodhani
Khimya Khetarpal
Joelle Pineau
2021-05-02
International Conference on Learning Representations (Poster)
openreview.net
openreview.net
Learning Task Decomposition with Ordered Memory Policy Network
Yuchen Lu
Yikang Shen
Siyuan Zhou
Aaron Courville
Joshua B. Tenenbaum
Chuang Gan
Many complex real-world tasks are composed of several levels of sub-tasks. Humans leverage these hierarchical structures to accelerate the l… (voir plus)earning process and achieve better generalization. In this work, we study the inductive bias and propose Ordered Memory Policy Network (OMPN) to discover subtask hierarchy by learning from demonstration. The discovered subtask hierarchy could be used to perform task decomposition, recovering the subtask boundaries in an unstruc-tured demonstration. Experiments on Craft and Dial demonstrate that our modelcan achieve higher task decomposition performance under both unsupervised and weakly supervised settings, comparing with strong baselines. OMPN can also bedirectly applied to partially observable environments and still achieve higher task decomposition performance. Our visualization further confirms that the subtask hierarchy can emerge in our model.
2021-05-02
International Conference on Learning Representations (Poster)
doi.org
openreview.net
Neural Approximate Sufficient Statistics for Implicit Models
Yanzhi Chen
Dinghuai Zhang
Michael U. Gutmann
Aaron Courville
Zhanxing Zhu
We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation… (voir plus) of the likelihood function is intractable, but sampling data from the model is possible. The idea is to frame the task of constructing sufficient statistics as learning mutual information maximizing representations of the data with the help of deep neural networks. The infomax learning procedure does not need to estimate any density or density ratio. We apply our approach to both traditional approximate Bayesian computation and recent neural likelihood methods, boosting their performance on a range of tasks.
2021-05-02
International Conference on Learning Representations (Spotlight)
doi.org
openreview.net