We create new connections through advanced research and technology in vision, text, machine learning, UX and ethnography.
Our multidisciplinary approach to AI allows us to tackle challenges from different perspectives and gives greater meaning to our work.
LeBenchmark: a reproducible framework for assessing self-supervised representation learning from speech
A new approach to learning few-shot imitation agents whereby you simply feed demonstrations of a new test task to the learned policy called DCRL. This new approach has several advantages.
A new sparse bi-encoder BERT-based model for effective and efficient first-stage ranking. The first to rival dense models.
Using domain randomization and meta-learning, computer vision models forget less when exposed to training samples from new domains. Remembering is a crucial element in the deployment of self-driving cars and robots which interact in dynamic environments.