Publications

Existence of Nash Equilibria on Integer Programming Games

Margarida Carvalho

Andrea Lodi

João Pedro Pedroso

2018-02-14

Springer Proceedings in Mathematics & Statistics (publié)

doi.org

Learning Generative Models with Locally Disentangled Latent Factors

Brady Neal

2018-02-14

(publié)

openreview.net

Online Hyper-Parameter Optimization

Damien Vincent

Sylvain Gelly

Nicolas Roux

Olivier Bousquet

2018-02-14

(publié)

openreview.net

Sequential Coordination of Deep Models for Learning Visual Arithmetic

Eric Crawford

Guillaume Rabusseau

Joelle Pineau

Achieving machine intelligence requires a smooth integration of perception and reasoning, yet models developed to date tend to specialize in… (voir plus) one or the other; sophisticated manipulation of symbols acquired from rich perceptual spaces has so far proved elusive. Consider a visual arithmetic task, where the goal is to carry out simple arithmetical algorithms on digits presented under natural conditions (e.g. hand-written, placed randomly). We propose a two-tiered architecture for tackling this problem. The lower tier consists of a heterogeneous collection of information processing modules, which can include pre-trained deep neural networks for locating and extracting characters from the image, as well as modules performing symbolic transformations on the representations extracted by perception. The higher tier consists of a controller, trained using reinforcement learning, which coordinates the modules in order to solve the high-level task. For instance, the controller may learn in what contexts to execute the perceptual networks and what symbolic transformations to apply to their outputs. The resulting model is able to solve a variety of tasks in the visual arithmetic domain, and has several advantages over standard, architecturally homogeneous feedforward networks including improved sample efficiency.

2018-02-14

ArXiv (prépublication)

openreview.net

Finding Flatter Minima with SGD

Stanisław Jastrzębski

Amos Storkey

2018-02-11

International Conference on Learning Representations (publié)

dblp.uni-trier.de

Graph Priors for Deep Neural Networks

In this work we explore how gene-gene interaction graphs can be used as a prior for the representation of a model to construct features base… (voir plus)d on known interactions between genes. Most existing machine learning work on graphs focuses on building models when data is confined to a graph structure. In this work we focus on using the information from a graph to build better representations in our models. We use the percolate task, determining if a path exists across a grid for a set of node values, as a proxy for gene pathways. We create variants of the percolate task to explore where existing methods fail. We test the limits of existing methods in order to determine what can be improved when applying these methods to a real task. This leads us to propose new methods based on Graph Convolutional Networks (GCN) that use pooling and dropout to deal with noise in the graph prior.

2018-02-11

(publié)

openreview.net

Inferring Identity Factors for Grouped Examples

Shawn Tan

Christopher Pal

Aaron Courville

We propose a method for modelling groups of face images from the same identity. The model is trained to infer a distribution over the latent… (voir plus) space for identity given a small set of “training data”. One can then sample images using that latent representation to produce images of the same identity. We demonstrate that the model extracts disentangled factors for identity factors and image-specific vectors. We also perform generative classification over identities to assess its feasibility for few-shot face recognition.

2018-02-11

(publié)

openreview.net

Online variance-reducing optimization

Nicolas Roux

Reza Babanezhad Harikandeh

Reza Babanezhad

Pierre-Antoine Manzagol

2018-02-11

International Conference on Learning Representations (publié)

openreview.net

SGD S MOOTHS THE S HARPEST D IRECTIONS

Stanisław Jastrzębski

Amos Storkey

Stochastic gradient descent (SGD) is able to find regions that generalize well, even in drastically over-parametrized models such as deep ne… (voir plus)ural networks. We observe that noise in SGD controls the spectral norm and conditioning of the Hessian throughout the training. We hypothesize the cause of this phenomenon is due to the dynamics of neurons saturating their non-linearity along the largest curvature directions, thus leading to improved conditioning.

2018-02-11

(publié)

openreview.net

Extending the Framework of Equilibrium Propagation to General Dynamics

2018-02-10

International Conference on Learning Representations (publié)

openreview.net

Learning Robust Options

Daniel J. Mankowitz

Timothy A. Mann

Pierre-Luc Bacon

Doina Precup

Shie Mannor

Robust reinforcement learning aims to produce policies that have strong guarantees even in the face of environments/transition models whose … (voir plus)parameters have strong uncertainty. Existing work uses value-based methods and the usual primitive action setting. In this paper, we propose robust methods for learning temporally abstract actions, in the framework of options. We present a Robust Options Policy Iteration (ROPI) algorithm with convergence guarantees, which learns options that are robust to model uncertainty. We utilize ROPI to learn robust options with the Robust Options Deep Q Network (RO-DQN) that solves multiple tasks and mitigates model misspecification due to model uncertainty. We present experimental results which suggest that policy iteration with linear features may have an inherent form of robustness when using coarse feature representations. In addition, we present experimental results which demonstrate that robustness helps policy iteration implemented on top of deep neural networks to generalize over a much broader range of dynamics than non-robust policy iteration.

2018-02-08

ArXiv (prépublication)

doi.org

arxiv.org

Hierarchical Adversarially Learned Inference

Ishmael Belghazi

Sai Rajeswar

We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model. Both the generative … (voir plus)and inference model are trained using the adversarial learning paradigm. We demonstrate that the hierarchical structure supports the learning of progressively more abstract representations as well as providing semantically meaningful reconstructions with different levels of fidelity. Furthermore, we show that minimizing the Jensen-Shanon divergence between the generative and inference network is enough to minimize the reconstruction error. The resulting semantically meaningful hierarchical latent structure discovery is exemplified on the CelebA dataset. There, we show that the features learned by our model in an unsupervised way outperform the best handcrafted features. Furthermore, the extracted features remain competitive when compared to several recent deep supervised approaches on an attribute prediction task on CelebA. Finally, we leverage the model's inference network to achieve state-of-the-art performance on a semi-supervised variant of the MNIST digit classification task.

2018-02-03

ArXiv (prépublication)

openreview.net

Mila sur Udemy

Publications du Fellowship en politiques de l'IA

La plateforme Mila Ventures

Publications

Mila sur Udemy

Publications du Fellowship en politiques de l'IA

La plateforme Mila Ventures

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Publications