Joelle Pineau

openreview.net

Independently Controllable Factors

Valentin Thomas

Jules Pondard

Emmanuel Bengio

Marc Sarfati

Philippe Beaudoin

Marie-Jean Meurs

Doina Precup

Yoshua Bengio

It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it rema… (voir plus)ins an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that some of the causal factors could be discovered if the learner is allowed to interact with its environment. The agent can experiment with different actions and observe their effects. More specifically, we hypothesize that some of these factors correspond to aspects of the environment which are independently controllable, i.e., that there exists a policy and a learnable feature for each such aspect of the environment, such that this policy can yield changes in that feature with minimal changes to other features that explain the statistical variations in the observed data. We propose a specific objective function to find such factors and verify experimentally that it can indeed disentangle independently controllable aspects of the environment without any extrinsic reward signal.

2016-12-31

arXiv (prépublication)

Independently Controllable Features

Valentin Thomas

Philippe Beaudoin

Y. Bengio

Marie-Jean Meurs

2016-12-31

(publié)

Multitask Spectral Learning of Weighted Automata.

Guillaume Rabusseau

Borja Balle

2016-12-31

Neural Information Processing Systems (inconnu)

dblp.uni-trier.de

Piecewise Latent Variables for Neural Variational Text Processing

Iulian V. Serban

Alexander G. Ororbia II

Aaron Courville

Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variable… (voir plus)s, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.

2016-12-31

Conference on Empirical Methods in Natural Language Processing (publié)

Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

Iulian V. Serban

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Ge… (voir plus)nerative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.

2016-03-04

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Recent Advances in Reinforcement Learning

2007-12-31

Lecture Notes in Computer Science (publié)