AI Synergies


Antoine Wehenkel

PhD Student

Unconstrained Monotonic Neural Networks

In this talk, I will first introduce the principles of density estimation with neural architectures. Then, I will describe Unconstrained Monotonic Neural Network, a new neural architecture for monotonic function modeling. I will conclude my presentation by showing how such transformations can be used for density estimation and present experimental results.


Antoine Wehenkel is a PhD student (FNRS Research Fellowship) in machine learning at ULi├Ęge (Belgium) under the supervision of Professor Gilles Louppe. The subject of his thesis lays at the intersection between likelihood free inference and deep learning.


Halle vitree
Research presentations ML
Day 3 - Nov 8th

Brewery of Ideas

AI Synergies is organized by VUB/ULB, BNVKI and Brewery of Ideas.

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