Deep Graph Infomax
Petar Veličković
William Fedus
William L. Hamilton
Pietro Lio
Deep Graph Infomax
Petar Veličković
William Fedus
William L. Hamilton
Pietro Lio
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.
Deep Graph Infomax
Petar Veličković
William Fedus
William L. Hamilton
Pietro Lio
Deep Graph Infomax
Petar Veličković
William Fedus
William L. Hamilton
Pietro Lio
Deep Graph Infomax
Petar Veličković
William Fedus
William L. Hamilton
Pietro Lio
Deep Graph Infomax
Petar Veličković
William Fedus
William L. Hamilton
Pietro Lio
Modeling the Long Term Future in Model-Based Reinforcement Learning
Nan Rosemary Ke
Amanpreet Singh
Ahmed Touati
Anirudh Goyal
Devi Parikh
Dhruv Batra
Probabilistic Planning with Sequential Monte Carlo methods
Alexandre Piché
Valentin Thomas
Cyril Ibrahim
Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio
Stanisław Jastrzębski
Zac Kenton
Devansh Arpit
Nicolas Ballas
Asja Fischer
Amos Storkey
Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation
Tanya Nair
Douglas Arnold
How can deep learning advance computational modeling of sensory information processing?
Jessica A.F. Thompson
Elia Formisano
Marc Schönwiesner
Deep learning, computational neuroscience, and cognitive science have overlapping goals related to understanding intelligence such that perc… (voir plus)eption and behaviour can be simulated in computational systems. In neuroimaging, machine learning methods have been used to test computational models of sensory information processing. Recently, these model comparison techniques have been used to evaluate deep neural networks (DNNs) as models of sensory information processing. However, the interpretation of such model evaluations is muddied by imprecise statistical conclusions. Here, we make explicit the types of conclusions that can be drawn from these existing model comparison techniques and how these conclusions change when the model in question is a DNN. We discuss how DNNs are amenable to new model comparison techniques that allow for stronger conclusions to be made about the computational mechanisms underlying sensory information processing.