Wohlert

[ ] am a machine learning engineer at  Special Project group working on autonomous systems software.

In the past I've developed machine-learning based planning algorithms using reinforcement learning and trajectory optimisation for cyber-physical systems. These systems are currently deployed in real-time safety critical environments such as chemical processing plants and autonomous mining vehicles.

Some of my interests in machine learning include representation learning, i.e. what consistutes a good representation for a given problem and how to learn it. I am also interested in how learned systems can be validated and interpreted such that one can give reason about the system's function.

Research

[ 2021 ] Density estimation on smooth manifolds with normalizing flows

Dimitris Kalatzis, Johan Ziruo Ye, Alison Pouplin, Jesper Wohlert, Søren Hauberg
We present a method for simultaneous density estimation and manifold learning without the need for prescribing geometric priors.

Open source

Due to contractual commitments, I cannot provide support to open source projects at the moment.

[ 2018 ] Semi-supervised PyTorch

Implementations of semi-supervised deep generative models that were popular around 2018.

[ 2018 ] Generative query network PyTorch

Implementation of the generative query network paper by DeepMind (Eslami et al).