Publications

What comes next? Extractive summarization by next-sentence prediction
Jingyun Liu
Jackie Cheung
Annie Priyadarshini Louis
Existing approaches to automatic summarization assume that a length limit for the summary is given, and view content selection as an optimiz… (voir plus)ation problem to maximize informativeness and minimize redundancy within this budget. This framework ignores the fact that human-written summaries have rich internal structure which can be exploited to train a summarization system. We present NEXTSUM, a novel approach to summarization based on a model that predicts the next sentence to include in the summary using not only the source article, but also the summary produced so far. We show that such a model successfully captures summary-specific discourse moves, and leads to better content selection performance, in addition to automatically predicting how long the target summary should be. We perform experiments on the New York Times Annotated Corpus of summaries, where NEXTSUM outperforms lead and content-model summarization baselines by significant margins. We also show that the lengths of summaries produced by our system correlates with the lengths of the human-written gold standards.
2019-01-12
ArXiv (prépublication)
arxiv.org
arxiv.org
A Geometric Perspective on Optimal Representations for Reinforcement Learning
Marc Gendron-Bellemare
Will Dabney
Robert Dadashi
Adrien Ali Taiga
Pablo Samuel Castro
Nicolas Le Roux
Dale Schuurmans
Tor Lattimore
Clare Lyle
We propose a new perspective on representation learning in reinforcement learning based on geometric properties of the space of value functi… (voir plus)ons. We leverage this perspective to provide formal evidence regarding the usefulness of value functions as auxiliary tasks. Our formulation considers adapting the representation to minimize the (linear) approximation of the value function of all stationary policies for a given environment. We show that this optimization reduces to making accurate predictions regarding a special class of value functions which we call adversarial value functions (AVFs). We demonstrate that using value functions as auxiliary tasks corresponds to an expected-error relaxation of our formulation, with AVFs a natural candidate, and identify a close relationship with proto-value functions (Mahadevan, 2005). We highlight characteristics of AVFs and their usefulness as auxiliary tasks in a series of experiments on the four-room domain.
arxiv.org
Improving advance medical directives: lessons from Quebec
Louise G. Bernier
Catherine Régis
Policy-makers’ efforts to increase the uptake of advance medical directives (AMDs), and the legal constraints they impose on health profes… (voir plus)sionals, are bringing greater scrutiny to provincial AMD regimes. In 2015, Quebec introduced a new, legally binding form to be filled out for AMDs, which limits individuals’ expression of their wishes to narrow, checklist responses to questions on specific medical interventions. This form-focused regime has other shortcomings: it relies on individuals to self-inform and it does not provide them the opportunity to meaningfully convey their preferences for end-of-life care. A more values-based and collaborative approach provides a better path forward for Quebec and for other provinces.
2019-01-01
(publié)
www.semanticscholar.org
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy
Alex Lamb
Vikas Verma
Juho Kannala
Yoshua Bengio
Adversarial robustness has become a central goal in deep learning, both in theory and practice. However, successful methods to improve adver… (voir plus)sarial robustness (such as adversarial training) greatly hurt generalization performance on the clean data. This could have a major impact on how adversarial robustness affects real world systems (i.e. many may opt to forego robustness if it can improve performance on the clean data). We propose Interpolated Adversarial Training, which employs recently proposed interpolation based training methods in the framework of adversarial training. On CIFAR-10, adversarial training increases clean test error from 5.8% to 16.7%, whereas with our Interpolated adversarial training we retain adversarial robustness while achieving a clean test error of only 6.5%. With our technique, the relative error increase for the robust model is reduced from 187.9% to just 12.1%.
2019-01-01
arXiv.org (preprint)
dblp.uni-trier.de
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy
Alex Lamb
Vikas Verma
Juho Kannala
Yoshua Bengio
Adversarial robustness has become a central goal in deep learning, both in theory and practice. However, successful methods to improve adver… (voir plus)sarial robustness (such as adversarial training) greatly hurt generalization performance on the clean data. This could have a major impact on how adversarial robustness affects real world systems (i.e. many may opt to forego robustness if it can improve performance on the clean data). We propose Interpolated Adversarial Training, which employs recently proposed interpolation based training methods in the framework of adversarial training. On CIFAR-10, adversarial training increases clean test error from 5.8% to 16.7%, whereas with our Interpolated adversarial training we retain adversarial robustness while achieving a clean test error of only 6.5%. With our technique, the relative error increase for the robust model is reduced from 187.9% to just 12.1%.
2019-01-01
arXiv.org (prépublication)
dblp.uni-trier.de
Learning Brain Dynamics from Calcium Imaging with Coupled van der Pol and LSTM
Germán Abrevaya
Irina Rish
Aleksandr Y. Aravkin
Guillermo Cecchi
James Kozloski
Pablo Polosecki
Peng Zheng
Silvina Ponce Dawson
Juliana Y. Rhee
David Daniel Cox
Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems. In this paper, we focus on brain calc… (voir plus)ium imaging (CaI) of different organisms (zebrafish and rat), aiming to build a model of joint activation dynamics in large neuronal populations, including the whole brain of zebrafish. We propose a new approach for capturing dynamics of temporal SVD components that uses the coupled (multivariate) van der Pol (VDP) oscillator, a nonlinear ordinary differential equation (ODE) model describing neural activity, with a new parameter estimation technique that combines variable projection optimization and stochastic search. We show that the approach successfully handles nonlinearities and hidden state variables in the coupled VDP. The approach is accurate, achieving 0.82 to 0.94 correlation between the actual and model-generated components, and interpretable, as VDP’s coupling matrix reveals anatomically meaningful positive (excitatory) and negative (inhibitory) interactions across different brain subsystems corresponding to spatial SVD components. Moreover, VDP is comparable to (or sometimes better than) recurrent neural networks (LSTM) for (short-term) prediction of future brain activity; VDP needs less parameters to train, which was a plus on our small training data. Finally, the overall best predictive method, greatly outperforming both VDP and LSTM in shortand long-term predicitve settings on both datasets, was the new hybrid VDP-LSTM approach that used VDP to simulate large domain-specific dataset for LSTM pretraining; note that simple LSTM data-augmentation via noisy versions of training data was much less effective.
2019-01-01
(publié)
www.semanticscholar.org
Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference
Matthew D Riemer
Ignacio Cases
Robert Ajemian
Miao Liu
Irina Rish
Yuhai Tu
Gerald Tesauro
Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neura… (voir plus)l network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples. We then propose a new algorithm, Meta-Experience Replay (MER), that directly exploits this view by combining experience replay with optimization based meta-learning. This method learns parameters that make interference based on future gradients less likely and transfer based on future gradients more likely. We conduct experiments across continual lifelong supervised learning benchmarks and non-stationary reinforcement learning environments demonstrating that our approach consistently outperforms recently proposed baselines for continual learning. Our experiments show that the gap between the performance of MER and baseline algorithms grows both as the environment gets more non-stationary and as the fraction of the total experiences stored gets smaller.
2019-01-01
ICLR.cc/2019/Conference (poster)
openreview.net
openreview.net
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
Yuanshuo Zhou
Bradley Gram-Hansen
Tobias Kohn
Tom Rainforth
Hongseok Yang
Frank Wood
We develop a new Low-level, First-order Probabilistic Programming Language~(LF-PPL) suited for models containing a mix of continuous, discre… (voir plus)te, and/or piecewise-continuous variables. The key success of this language and its compilation scheme is in its ability to automatically distinguish parameters the density function is discontinuous with respect to, while further providing runtime checks for boundary crossings. This enables the introduction of new inference engines that are able to exploit gradient information, while remaining efficient for models which are not everywhere differentiable. We demonstrate this ability by incorporating a discontinuous Hamiltonian Monte Carlo (DHMC) inference engine that is able to deliver automated and efficient inference for non-differentiable models. Our system is backed up by a mathematical formalism that ensures that any model expressed in this language has a density with measure zero discontinuities to maintain the validity of the inference engine.
2019-01-01
AISTATS (publié)
proceedings.mlr.press
arxiv.org
Reducing the variance in online optimization by transporting past gradients
Sébastien M. R. Arnold
Pierre-Antoine Manzagol
Reza Babanezhad Harikandeh
Ioannis Mitliagkas
Nicolas Le Roux
Most stochastic optimization methods use gradients once before discarding them. While variance reduction methods have shown that reusing pas… (voir plus)t gradients can be beneficial when there is a finite number of datapoints, they do not easily extend to the online setting. One issue is the staleness due to using past gradients. We propose to correct this staleness using the idea of implicit gradient transport (IGT) which transforms gradients computed at previous iterates into gradients evaluated at the current iterate without using the Hessian explicitly. In addition to reducing the variance and bias of our updates over time, IGT can be used as a drop-in replacement for the gradient estimate in a number of well-understood methods such as heavy ball or Adam. We show experimentally that it achieves state-of-the-art results on a wide range of architectures and benchmarks. Additionally, the IGT gradient estimator yields the optimal asymptotic convergence rate for online stochastic optimization in the restricted setting where the Hessians of all component functions are equal.
arxiv.org
La science, un droit humain. Mettre en œuvre le principe d’une science participative, équitable et accessible à tous
Catherine Régis
2019-01-01
(publié)
www.semanticscholar.org
The Termination Critic
Anna Harutyunyan
Will Dabney
Diana Borsa
Nicolas Heess
Remi Munos
Doina Precup
In this work, we consider the problem of autonomously discovering behavioral abstractions, or options, for reinforcement learning agents. We… (voir plus) propose an algorithm that focuses on the termination function, as opposed to - as is common - the policy. The termination function is usually trained to optimize a control objective: an option ought to terminate if another has better value. We offer a different, information-theoretic perspective, and propose that terminations should focus instead on the compressibility of the option’s encoding - arguably a key reason for using abstractions.To achieve this algorithmically, we leverage the classical options framework, and learn the option transition model as a “critic” for the termination function. Using this model, we derive gradients that optimize the desired criteria. We show that the resulting options are non-trivial, intuitively meaningful, and useful for learning.
2019-01-01
AISTATS (publié)
arxiv.org
arxiv.org
Toward Requirements Specification for Machine-Learned Components
Mona Rahimi
Jin Guo
Sahar Kokaly
Marsha Chechik
In current practice, the behavior of Machine-Learned Components (MLCs) is not sufficiently specified by the predefined requirements. Instead… (voir plus), they "learn" existing patterns from the available training data, and make predictions for unseen data when deployed. On the surface, their ability to extract patterns and to behave accordingly is specifically useful for hard-to-specify concepts in certain safety critical domains (e.g., the definition of a pedestrian in a pedestrian detection component in a vehicle). However, the lack of requirements specifications on their behaviors makes further software engineering tasks challenging for such components. This is especially concerning for tasks such as safety assessment and assurance. In this position paper, we call for more attention from the requirements engineering community on supporting the specification of requirements for MLCs in safety critical domains. Towards that end, we propose an approach to improve the process of requirements specification in which an MLC is developed and operates by explicitly specifying domain-related concepts. Our approach extracts a universally accepted benchmark for hard-to-specify concepts (e.g., "pedestrian") and can be used to identify gaps in the associated dataset and the constructed machine-learned model.
2019-01-01
2019 IEEE 27th International Requirements Engineering Conference Workshops (REW) (publié)
doi.org