Publications
Our research helps push the boundaries of clinical AI, informing the technology we build while contributing to the advancement of healthcare and machine learning.
Training integrable parameterizations of deep neural networks in the infinite-width limit
Karl Hajjar
Uncertainty-aware automated assessment of the arm impedance with upper-limb exoskeletons
Ronan Sangouard
Score-based generative modeling through stochastic differential equations
Grégoire Retourné
Aggregating similarity metrics for natural language generation
Grégoire Retourné
Ranked reward: Enabling self-play reinforcement learning for combinatorial optimization
Karl Hajjar
TorchKbNufft: A high-level, hardware-agnostic non-uniform fast Fourier transform
Ruben Stern
Using deep learning to accelerate knee MRI at 3 T: results of an interchangeability study
Ruben Stern
The GraphNet zoo: an all-in-one graph based deep semi-supervised framework for medical image classification
Marianne de Vriendt
Inferring transmission histories of rare alleles in population-scale genealogies
Marianne de Vriendt
Real-time inference in multi-sentence tasks with deep pretrained transformers
Sam Humeau
Build it break it fix it for dialogue safety: Robustness from adversarial human attack
Sam Humeau
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Martin Raison
Poly-encoders: Transformer architectures and pre-training strategies for fast and accurate multi-sentence scoring
Sam Humeau
Weaver: Deep co-encoding of questions and documents for machine reading
Martin Raison
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