Learning from relational data is crucial for modeling the processes found in many application domains ranging from computational biology to social networks. In this project, we propose to work on developing new modeling techniques that combine the advantages of the approaches found in two fields of study: Machine Learning (through graph neural networks and transformer networks) and Statistical Learning (through statistical relational learning methods).
Human perception has developed the ability to decompose scenes into fine grained elements. This lays the foundation for strong generalization to new situations where the base concepts can be recomposed to interpret objects never seen before. While it has been shown that, in the general case, proper decomposition is not possible, new paradigms provide provable decomposition in constrained environments. We hypothesize that the multiple sensory systems of human perception offer a strong signal for decomposing scenes in a proper way.
Humans recognise objects in the world leveraging multi-modal sensory inputs beyond visual aspects (images and videos). Touch based information (Haptics) possesses rich information about structure, shape and other objetness properties. In this work, we will study and learn cross-modal representations between vision and touch. To connect vision and touch, we plan to introduce a zero shot classification task of recognising unseen object categories from shapenet dataset using haptics signals.
We’d like to address the issue of 3D reconstruction from 2D images. This means developing a machine learning algorithm that can take a regular photo as an input and generate a full 3-dimensional reconstruction of the contents of the photo. Such technology can be used creatively or to help the coming generation of robots better understand their surroundings.
This fundamental research project investigates semantic visual navigation tasks, such as asking a household robot to “go find my keys”. We seek to enhance the efficacy of repeated search tasks within the same environment, by explicitly building, maintaining, and exploiting a map of locations that the robot had previously explored. We also seek to exploit prior location-tolocation, object-within-location, and object-to-object relationships from similar environments (e.g. within a common cultural region) to improve semantic visual navigation in unseen environments.
In this project we aim to automatically learn an augmenter network by using an approximate online bilevel optimization procedure. We plan to learn a augmenter network that generates a distribution of transformations that minimizes the loss on a validation set. By unfolding the gradients of the training loss, we will optimize the loss on validation with respect to the data augmentation parameters. In this way we can provide a general solution for an efficient and automatic data augmentation that is learned jointly with the training of the model.
In this project, we propose a continual learning approach to face the problem of catastrophic forgetting in online image classification problems. Concretely, we propose a model that learns how to mask a series of general modules in a deep learning architecture, so that generalization emerges through the composition of those modules. This is of vital importance for Element AI to provide reusable solutions that scale with new data, without the need of learning a new model for every problem and improving the overall performance.
Loptimisation combinatoire occupe une place prépondérante dans notre société actuelle. Que ce soit la logistique, le transport ou la gestion financière, tous ses domaines se retrouvent confrontés à des problèmes pour lesquels on recherche la meilleure solution. Cependant, un grand nombre de problèmes très complexes reste encore hors de portée des méthodes doptimisation actuelles. Cest pourquoi, lamélioration des techniques est un sujet crucial. Parmi les techniques récentes, les diagrammes de décisions semblent avoir un avenir prometteur.