(Rex) Devon Hjelm

Marc-Alexandre Côté

State representation learning, or the ability to capture latent generative factors of an environment, is crucial for building intelligent ag… (voir plus)ents that can perform a wide variety of tasks. Learning such representations without supervision from rewards is a challenging open problem. We introduce a method that learns state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground truth state variables. We believe this new framework for evaluating representation learning models will be crucial for future representation learning research. Finally, we compare our technique with other state-of-the-art generative and contrastive representation learning methods. The code associated with this work is available at this https URL

Keep Drawing It: Iterative language-based image generation and editing

Alaaeldin El-Nouby

Shikhar Sharma

Hannes Schulz

Layla El Asri

Samira Ebrahimi Kahou

Graham W. Taylor

Conditional text-to-image generation approaches commonly focus on generating a single image in a single step. One practical extension beyond… (voir plus) 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.

2018-11-24

arXiv.org (prépublication)

dblp.uni-trier.de

Deep Graph Infomax

Petar Veličković

William Fedus

William L. Hamilton

Pietro Lio

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised ma… (voir plus)nner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

Petar Veličković

William Fedus

William L. Hamilton

Pietro Lio

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

Petar Veličković

William Fedus

William L. Hamilton

Pietro Lio

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised ma… (voir plus)nner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

Petar Veličković

William Fedus

William L. Hamilton

Pietro Lio

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

Petar Veličković

William Fedus

William L. Hamilton

Pietro Lio

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

Petar Veličković

William Fedus

William L. Hamilton

Pietro Lio

2018-09-27

ArXiv (prépublication)

Deep Graph Infomax

Petar Veličković

William Fedus

William L. Hamilton

Pietro Lio

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised ma… (voir plus)nner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures. The learnt patch representations summarize subgraphs centered around nodes of interest, and can thus be reused for downstream node-wise learning tasks. In contrast to most prior approaches to unsupervised learning with GCNs, DGI does not rely on random walk objectives, and is readily applicable to both transductive and inductive learning setups. We demonstrate competitive performance on a variety of node classification benchmarks, which at times even exceeds the performance of supervised learning.

2018-09-27

ArXiv (prépublication)