Portrait de Rim Assouel

Rim Assouel

Doctorat - UdeM
Superviseur⋅e principal⋅e
Yoshua Bengio
Sujets de recherche
Apprentissage de représentations
Raisonnement
Vision par ordinateur

Publications

SPECTRA: Sparse Entity-centric Transitions
Rim Assouel
Yoshua Bengio
Learning an agent that interacts with objects is ubiquituous in many RL tasks. In most of them the agent’s actions have sparse effects : o… (voir plus)nly a small subset of objects in the visual scene will be affected by the action taken. We introduce SPECTRA, a model for learning slot-structured transitions from raw visual observations that embodies this sparsity assumption. Our model is composed of a perception module that decomposes the visual scene into a set of latent objects representations (i.e. slot-structured) and a transition module that predicts the next latent set slot-wise and in a sparse way. We show that learning a perception module jointly with a sparse slot-structured transition model not only biases the model towards more entity-centric perceptual groupings but also enables intrinsic exploration strategy that aims at maximizing the number of objects changed in the agents trajectory.
2019-09-24
(publié)
openreview.net
openreview.net
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
Rim Assouel
Mohamed Ahmed
Marwin Segler
Amir Saffari
Yoshua Bengio
Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. Recently, deep generative mo… (voir plus)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 (prépublication)
openreview.net
openreview.net