Jeremie Desgagne-Bouchard
Jeremie is the Head of Science at Evovest. He joined the firm following his work as an applied research scientist at Element AI, where he expanded his deep learning acumen. He spent over 8 years at Intact in various R&D roles as an actuary (FCAS), where he pushed the development of analytical solutions in various areas, including assessment price optimization and telematics. He also has consulting experience at Willis Towers Watson, providing pragmatic solutions to clients.
Session
NeuroTreeModels.jl introduces NeuroTree, a differentiable tree operator for tabular data. It's implemented as a general neural network building block, similar to Dense or Conv, making it composable with the components of the Flux ecosystem.
The library includes a ready to use regressor that follows the MLJ interface. Comprehensive benchmarks on common datasets show its competitiveness with state-of-the-art boosted tree models.