Publications

Learning Typed Entailment Graphs with Global Soft Constraints
Mohammad Javad Hosseini
Nathanael Chambers
Siva Reddy
Xavier R. Holt
Shay B. Cohen
Mark Johnson
Mark Steedman
This paper presents a new method for learning typed entailment graphs from text. We extract predicate-argument structures from multiple-sour… (see more)ce news corpora, and compute local distributional similarity scores to learn entailments between predicates with typed arguments (e.g., person contracted disease). Previous work has used transitivity constraints to improve local decisions, but these constraints are intractable on large graphs. We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph. Learning takes only a few hours to run over 100K predicates and our results show large improvements over local similarity scores on two entailment data sets. We further show improvements over paraphrases and entailments from the Paraphrase Database, and prior state-of-the-art entailment graphs. We show that the entailment graphs improve performance in a downstream task.
2018-11-30
Transactions of the Association for Computational Linguistics (published)
doi.org
Contextual Bandits for Adapting Treatment in a Mouse Model of de Novo Carcinogenesis
Audrey Durand
Charis Achilleos
Demetris Iacovides
Katerina Strati
Georgios D. Mitsis
Joelle Pineau
2018-11-28
Proceedings of the 3rd Machine Learning for Healthcare Conference (published)
proceedings.mlr.press
proceedings.mlr.press
Understanding the impact of entropy in policy learning
Zafarali Ahmed
Nicolas Roux
Mohammad Norouzi
Dale Schuurmans
Entropy regularization is commonly used to improve policy optimization in reinforcement learning. It is believed to help with \emph{explorat… (see more)ion} by encouraging the selection of more stochastic policies. In this work, we analyze this claim using new visualizations of the optimization landscape based on randomly perturbing the loss function. We first show that even with access to the exact gradient, policy optimization is difficult due to the geometry of the objective function. Then, we qualitatively show that in some environments, a policy with higher entropy can make the optimization landscape smoother, thereby connecting local optima and enabling the use of larger learning rates. This paper presents new tools for understanding the optimization landscape, shows that policy entropy serves as a regularizer, and highlights the challenge of designing general-purpose policy optimization algorithms.
2018-11-26
(published)
www.semanticscholar.org
Environments for Lifelong Reinforcement Learning
Khimya Khetarpal
Shagun Sodhani
Sarath Chandar
Doina Precup
To achieve general artificial intelligence, reinforcement learning (RL) agents should learn not only to optimize returns for one specific ta… (see more)sk but also to constantly build more complex skills and scaffold their knowledge about the world, without forgetting what has already been learned. In this paper, we discuss the desired characteristics of environments that can support the training and evaluation of lifelong reinforcement learning agents, review existing environments from this perspective, and propose recommendations for devising suitable environments in the future.
2018-11-25
ArXiv (preprint)
doi.org
arxiv.org
Multi-task Learning over Graph Structures
Pengfei Liu
Jie Fu
Yue Dong
Xipeng Qiu
Jackie CK Cheung
We present two architectures for multi-task learning with neural sequence models. Our approach allows the relationships between different ta… (see more)sks to be learned dynamically, rather than using an ad-hoc pre-defined structure as in previous work. We adopt the idea from message-passing graph neural networks and propose a general \textbf{graph multi-task learning} framework in which different tasks can communicate with each other in an effective and interpretable way. We conduct extensive experiments in text classification and sequence labeling to evaluate our approach on multi-task learning and transfer learning. The empirical results show that our models not only outperform competitive baselines but also learn interpretable and transferable patterns across tasks.
2018-11-25
ArXiv (preprint)
arxiv.org
arxiv.org
Planning in Dynamic Environments with Conditional Autoregressive Models
Johanna Hansen
Kyle Kastner
Aaron Courville
Gregory Dudek
We demonstrate the use of conditional autoregressive generative models (van den Oord et al., 2016a) over a discrete latent space (van den Oo… (see more)rd et al., 2017b) for forward planning with MCTS. In order to test this method, we introduce a new environment featuring varying difficulty levels, along with moving goals and obstacles. The combination of high-quality frame generation and classical planning approaches nearly matches true environment performance for our task, demonstrating the usefulness of this method for model-based planning in dynamic environments.
2018-11-24
ArXiv (preprint)
arxiv.org
arxiv.org
A Survey of Mobile Computing for the Visually Impaired
Martin Weiss
Margaux Luck
Roger Girgis
Christopher Pal
Joseph Paul Cohen
The number of visually impaired or blind (VIB) people in the world is estimated at several hundred million. Based on a series of interviews … (see more)with the VIB and developers of assistive technology, this paper provides a survey of machine-learning based mobile applications and identifies the most relevant applications. We discuss the functionality of these apps, how they align with the needs and requirements of the VIB users, and how they can be improved with techniques such as federated learning and model compression. As a result of this study we identify promising future directions of research in mobile perception, micro-navigation, and content-summarization.
2018-11-24
ArXiv (preprint)
arxiv.org
arxiv.org
Keep Drawing It: Iterative language-based image generation and editing
Alaaeldin El-Nouby
Shikhar Sharma
Hannes Schulz
R Devon Hjelm
Layla El Asri
S Ebrahimi Kahou
Yoshua Bengio
Graham W. Taylor
Conditional text-to-image generation approaches commonly focus on generating a single image in a single step. One practical extension beyond… (see more) one-step generation is an interactive system that generates an image iteratively, conditioned on ongoing linguistic input / feedback. This is significantly more challenging as such a system must understand and keep track of the ongoing context and history. In this work, we present a recurrent image generation model which takes into account both the generated output up to the current step as well as all past instructions for generation. We show that our model is able to generate the background, add new objects, apply simple transformations to existing objects, and correct previous mistakes. We believe our approach is an important step toward interactive generation.
2018-11-23
arXiv.org (preprint)
dblp.uni-trier.de
Interpretable Convolutional Filters with SincNet
Mirco Ravanaelli
Yoshua Bengio
Deep learning is currently playing a crucial role toward higher levels of artificial intelligence. This paradigm allows neural networks to l… (see more)earn complex and abstract representations, that are progressively obtained by combining simpler ones. Nevertheless, the internal "black-box" representations automatically discovered by current neural architectures often suffer from a lack of interpretability, making of primary interest the study of explainable machine learning techniques. This paper summarizes our recent efforts to develop a more interpretable neural model for directly processing speech from the raw waveform. In particular, we propose SincNet, a novel Convolutional Neural Network (CNN) that encourages the first layer to discover more meaningful filters by exploiting parametrized sinc functions. In contrast to standard CNNs, which learn all the elements of each filter, only low and high cutoff frequencies of band-pass filters are directly learned from data. This inductive bias offers a very compact way to derive a customized filter-bank front-end, that only depends on some parameters with a clear physical meaning. Our experiments, conducted on both speaker and speech recognition, show that the proposed architecture converges faster, performs better, and is more interpretable than standard CNNs.
2018-11-22
ArXiv (preprint)
arxiv.org
arxiv.org
Harmonic Recomposition using Conditional Autoregressive Modeling
Kyle Kastner
Rithesh Kumar
Tim Cooijmans
Aaron Courville
We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(20… (see more)17). Recomposition (Casal & Casey, 2010) focuses on reworking existing musical pieces, adhering to structure at a high level while also re-imagining other aspects of the work. This can involve reuse of pre-existing themes or parts of the original piece, while also requiring the flexibility to generate new content at different levels of granularity. Applying the aforementioned modeling pipeline to recomposition, we show diverse and structured generation conditioned on chord sequence annotations.
2018-11-17
ArXiv (preprint)
arxiv.org
arxiv.org
The Barbados 2018 List of Open Issues in Continual Learning
Tom Schaul
Hado van Hasselt
Joseph. Modayil
Martha White
Adam White
Pierre-Luc Bacon
Jean Harb
Shibl Mourad
Bellemare Marc-Emmanuel
Doina Precup
We want to make progress toward artificial general intelligence, namely general-purpose agents that autonomously learn how to competently ac… (see more)t in complex environments. The purpose of this report is to sketch a research outline, share some of the most important open issues we are facing, and stimulate further discussion in the community. The content is based on some of our discussions during a week-long workshop held in Barbados in February 2018.
2018-11-15
ArXiv (preprint)
arxiv.org
arxiv.org
On Training Recurrent Neural Networks for Lifelong Learning
Shagun Sodhani
A. Chandar
Yoshua Bengio
Catastrophic forgetting and capacity saturation are the central challenges of any parametric lifelong learning system. In this work, we stud… (see more)y these challenges in the context of sequential supervised learning with emphasis on recurrent neural networks. To evaluate the models in the lifelong learning setting, we propose a curriculum-based, simple, and intuitive benchmark where the models are trained on tasks with increasing levels of difficulty. To measure the impact of catastrophic forgetting, the model is tested on all the previous tasks as it completes any task. As a step towards developing true lifelong learning systems, we unify Gradient Episodic Memory (a catastrophic forgetting alleviation approach) and Net2Net(a capacity expansion approach). Both these models are proposed in the context of feedforward networks and we evaluate the feasibility of using them for recurrent networks. Evaluation on the proposed benchmark shows that the unified model is more suitable than the constituent models for lifelong learning setting.
2018-11-15
ArXiv (preprint)
arxiv.org
arxiv.org