Publications

Leveraging unlabeled examples is a crucial issue for boosting performances in semi-supervised learning. In this work, we introduce the …

Online High Definition Map (HDMap) estimation from sensors offers a low-cost alternative to manually acquired HDMaps. As such, it …

As Deep Learning tackles complex tasks like trajectory forecasting in autonomous vehicles, a number of new challenges emerge. In …

Multi-input multi-output training improves network performance by optimizing multiple subnetworks simultaneously. In this paper, we …

Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data. In fact, the amount of …

Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork …

Multi-input multi-output models have proven capable of providing ensembling for free in convolutional neural networks by training …

Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of …

Recent strategies achieved ensembling “for free” by fitting concurrently diverse subnetworks inside a single base network. …

Leveraging unlabeled examples is a crucial issue for boosting performances in semi-supervised learning. In this work, we introduce the …

Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. …

Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. …

Computer vision systems for automatic image categorization have become accurate and reliable enough that they can run continuously for …

Computer vision systems for automatic image categorization have become accurate and reliable enough that they can run continuously for …

Deep generative models such as Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs) are important tools to …