Recently, by relying only on the self-attention blocks, the transformer mechanism has taken many AI fields by storm. For example in NLP, several transformer-based architectures were proposed like BERT, GPT 2, GPT3 outperforming classical NLP approaches such RNN and LSTM… Also, in Biology, AlphaFold 2 was proposed as a transformer-based model that better predicts the structures of proteins from their genetic sequences. And more recently many researchers have tried to apply the same transformer recipe to tackle computer visions tasks such as classification, semantic segmentation, object detection…

The aim of this webinar is to give an overview of the Vision Transformer ViT and how it has changed the computer vision landscape by replacing the most famous convolution operator with only self-attention. The webinar will discuss how the proposed architecture succeeded to outperform CNN-based architectures like ResNet by only stacking transformer layers and by considering input images as patches tokens. Considerations such as scalability, complexity, interpretability and other ViT variants will be discussed as well.

Rich-resource languages have plenty of frameworks to consider when developing for language technology purposes. For low-resourced languages, either no frameworks exist such as the Amazigh language or very few components are integrated in known and large frameworks. We present a comparative study of frameworks in order to clarify which ones can handle Arabic suitably and report on best practices to be applied for low-resource languages.

In this project, a Reinforcement Learning (RL) agent is trained to obtain a safe policy thus rendering a risk averse agent that prioritize avoiding worst case scenarios.

The model leverages distributional RL (e.g. Deep Quantile Regression) and optimizes the Conditional Value at Risk (CVaR) thus providing the user with an adjustable level of risk aversion.

Many applications can benefit from this approach, especially in fields where worst case scenarios are inadmissible such as security or medicine.

Plastic debris are one of the most widespread debris contributing to marine pollution, it threatens food safety and quality, human health, coastal tourism and contributes to climate changes. Remote sensing has shown great effectiveness in locating this type of debris. By leveraging AI/ML and hydrodynamic ocean models, we will demonstrate how to detect, quantify and track plastic marine debris in the marine environment.

The problem of human motion (face and body) prediction and generation is at the core of many applications in computer vision and robotics, such as human-robot interaction, autonomous driving and computer graphics. In this talk I will present some of our recent achievements addressing these specific aspects: 1) generating videos of the facial expressions given a neutral face image, 2) dynamic 3D expression generation from an expression label, 3) Human motion prediction and generation of 3D skeleton. We model the temporal evolution of the 3D human motion and face expression as trajectory, what allows us to map human motions to single points on a sphere manifold. We propose a manifold-aware Wasserstein generative adversarial model that captures the temporal and spatial dependencies of facial expression and human motion through different losses. Our solutions score best on diverse benchmarks.

How do we compare between hypotheses that are entirely consistent with observations? The marginal likelihood (aka Bayesian evidence), which represents the probability of generating our observations from a prior, provides a distinctive approach to this foundational question, automatically encoding Occam’s razor. Although it has been observed that the marginal likelihood can overfit and is sensitive to prior assumptions, its limitations for hyperparameter learning and discrete model comparison have not been thoroughly investigated. We revisit the appealing properties of the marginal likelihood for learning constraints and hypothesis testing. Then, we highlight the conceptual and practical issues in using the marginal likelihood as a proxy for generalization. Namely, we show how marginal likelihood can be negatively correlated with generalization, with implications for neural architecture search, and can lead to both underfitting and overfitting in hyperparameter learning. We provide a partial remedy through a conditional marginal likelihood, which we show is more aligned with generalization, and practically valuable for large-scale hyperparameter learning, such as in deep kernel learning.