Publications

Myeloarchitecture gradients in the human insula: Histological underpinnings and association to intrinsic functional connectivity

Jessica Royer

Casey Paquola

Sara Larivière

Reinder Vos de Wael

Shahin Tavakol

Alexander J. Lowe

Oualid Benkarim

Alan C. Evans

Danilo Bzdok

Jonathan Smallwood

Birgit Frauscher

Boris C. Bernhardt

2019-12-31

NeuroImage (publié)

doi.org

Natural Language Processing and Text Mining with Graph-Structured Representations

Bang Liu

2019-12-31

(publié)

doi.org

N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting

Boris N. Oreshkin

Dmitri Carpov

Nicolas Chapados

Yoshua Bengio

We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based o… (voir plus)n backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train. We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year's winner of the M4 competition, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that, contrarily to received wisdom, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.

2019-12-31

ICLR (publié)

doi.org

openreview.net

An operator view of policy gradient methods

Dibya Ghosh

Marlos C. Machado

Nicolas Roux

We cast policy gradient methods as the repeated application of two operators: a policy improvement operator …

2019-12-31

NeurIPS (publié)

arxiv.org

PAST DSAA KEYNOTE SPEAKERS

Yoshua Bengio

An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs. By utilizing fas… (voir plus)t matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions, while being able to meaningfully model local information of the graph (e.g., degrees) as well as global information (e.g., clustering coefficient, assortativity, etc.) if desired. This allows one to efficiently generate random networks with similar properties as an observed network, and the models can be used for several downstream tasks such as link prediction. Our methods are scalable to sparse graphs consisting of millions of nodes. Empirical evaluation demonstrates competitiveness in terms of both speed and accuracy with state-of-the-art methods—which are typically based on embedding the graph into some lowdimensional space— for link prediction, showcasing the potential of a more direct and interpretable probablistic model for this task.

2019-12-31

(publié)

www.semanticscholar.org

Practical Dynamic SC-Flip Polar Decoders: Algorithm and Implementation

Furkan Ercan

Thibaud Tonnellier

Nghia Doan

Warren J. Gross

SC-Flip (SCF) is a low-complexity polar code decoding algorithm with improved performance, and is an alternative to high-complexity (CRC)-ai… (voir plus)ded SC-List (CA-SCL) decoding. However, the performance improvement of SCF is limited since it can correct up to only one channel error (

2019-12-31

IEEE Transactions on Signal Processing (publié)

doi.org

arxiv.org

Principal Neighbourhood Aggregation for Graph Nets

Gabriele Corso

Luca Cavalleri

Dominique Beaini

Pietro Lio

Petar Veličković

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)

arxiv.org

G RADIENT -B ASED N EURAL DAG L EARNING WITH I NTERVENTIONS

Alexandre Lacoste

Decision making based on statistical association alone can be a dangerous endeavor due to non-causal associations. Ideally, one would rely o… (voir plus)n causal relationships that enable reasoning about the effect of interventions. Several methods have been proposed to discover such relationships from observational and inter-ventional data. Among them, GraN-DAG, a method that relies on the constrained optimization of neural networks, was shown to produce state-of-the-art results among algorithms relying purely on observational data. However, it is limited to observational data and cannot make use of interventions. In this work, we extend GraN-DAG to support interventional data and show that this improves its ability to infer causal structures

2019-12-31

(publié)

www.semanticscholar.org

Reinforced active learning for image segmentation

Arantxa Casanova

Pedro O. Pinheiro

Negar Rostamzadeh

Christopher Pal

Learning-based approaches for semantic segmentation have two inherent challenges. First, acquiring pixel-wise labels is expensive and time-c… (voir plus)onsuming. Second, realistic segmentation datasets are highly unbalanced: some categories are much more abundant than others, biasing the performance to the most represented ones. In this paper, we are interested in focusing human labelling effort on a small subset of a larger pool of data, minimizing this effort while maximizing performance of a segmentation model on a hold-out set. We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL). An agent learns a policy to select a subset of small informative image regions -- opposed to entire images -- to be labeled, from a pool of unlabeled data. The region selection decision is made based on predictions and uncertainties of the segmentation model being trained. Our method proposes a new modification of the deep Q-network (DQN) formulation for active learning, adapting it to the large-scale nature of semantic segmentation problems. We test the proof of concept in CamVid and provide results in the large-scale dataset Cityscapes. On Cityscapes, our deep RL region-based DQN approach requires roughly 30% less additional labeled data than our most competitive baseline to reach the same performance. Moreover, we find that our method asks for more labels of under-represented categories compared to the baselines, improving their performance and helping to mitigate class imbalance.

2019-12-31

ICLR (publié)

openreview.net

A Reinforcement Learning Pipeline for Band Gap-directed Crystal Generation

Prashant Govindarajan

Mathieu Reymond

Santiago Miret

Antoine Clavaud

Mariano Phielipp

Sarath Chandar

Property-driven AI-automated material discovery presents unique challenges owing to the complex nature of the chemical structural space and … (voir plus)computationally expensive simulations. For crystalline solids, the band gap is an important property for designing semiconductors and batteries. However, optimizing crystals for a target band gap is difficult and not well-explored. Reinforcement learning (RL) shows promise towards optimizing crystals, as it can freely explore the chemical space. However, it relies on regular band gap evaluations, which can only be accurately computed through expensive Density Functional Theory (DFT) simulations. In this study, we propose an active learning-inspired pipeline that combines RL and DFT simulations for optimizing crystal compositions given a target band gap. The pipeline includes an RL policy for predicting atom types and a band gap network that is fine-tuned with DFT data. Preliminary results indicate the need for furthering the state-of-the-art to address the inherent challenges of the problem.

2019-12-31

International Conference on Machine Learning (publié)

openreview.net

Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives

Anirudh Goyal

Shagun Sodhani

Jonathan Binas

Xue Bin Peng

Sergey Levine

Yoshua Bengio

Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavi… (voir plus)or. Often, this is addressed in the context of hierarchical reinforcement learning, where the aim is to decompose a policy into lower-level primitives or options, and a higher-level meta-policy that triggers the appropriate behaviors for a given situation. However, the meta-policy must still produce appropriate decisions in all states. In this work, we propose a policy design that decomposes into primitives, similarly to hierarchical reinforcement learning, but without a high-level meta-policy. Instead, each primitive can decide for themselves whether they wish to act in the current state. We use an information-theoretic mechanism for enabling this decentralized decision: each primitive chooses how much information it needs about the current state to make a decision and the primitive that requests the most information about the current state acts in the world. The primitives are regularized to use as little information as possible, which leads to natural competition and specialization. We experimentally demonstrate that this policy architecture improves over both flat and hierarchical policies in terms of generalization.

2019-12-31

ICLR.cc/2020/Conference (poster)

doi.org

openreview.net

Revisiting Successor Features for Inverse Reinforcement Learning

Sanjiban Choudhury

2019-12-31