Publications

DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation

Mohamed Ahmed

Marwin Segler

Amir Saffari

Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. Recently, deep generative mo… (see more)dels have shown a promising way of performing de-novo molecular design. Although graph generative models are currently available they either have a graph size dependency in their number of parameters, limiting their use to only very small graphs or are formulated as a sequence of discrete actions needed to construct a graph, making the output graph non-differentiable w.r.t the model parameters, therefore preventing them to be used in scenarios such as conditional graph generation. In this work we propose a model for conditional graph generation that is computationally efficient and enables direct optimisation of the graph. We demonstrate favourable performance of our model on prototype-based molecular graph conditional generation tasks.

2018-09-26

ArXiv (preprint)

openreview.net

On Difficulties of Probability Distillation

Alexandre Lacoste

2018-09-26

(published)

openreview.net

Dopamine: A Research Framework for Deep Reinforcement Learning

Pablo Samuel Castro

Subhodeep Moitra

Carles Gelada

Saurabh Kumar

Bellemare Marc-Emmanuel

Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provid… (see more)e stable, comprehensive implementations for benchmarking. At the same time, recent deep RL research has become more diverse in its goals. In this paper we introduce Dopamine, a new research framework for deep RL that aims to support some of that diversity. Dopamine is open-source, TensorFlow-based, and provides compact and reliable implementations of some state-of-the-art deep RL agents. We complement this offering with a taxonomy of the different research objectives in deep RL research. While by no means exhaustive, our analysis highlights the heterogeneity of research in the field, and the value of frameworks such as ours.

2018-09-26

ArXiv (preprint)

openreview.net