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Michiel Straat

Postdoc in Machine Learning

Bielefeld University

Biography

I am a postdoctoral researcher in the Machine Learning group at Bielefeld University, where I lead a junior research group on robust life-long machine learning in the research network SAIL: SustAInable Life-cycle of Intelligent Socio-Technical Systems. In order to address important demands for AI systems such as transparency, human agency, safety and resource-efficiency, the interdisciplinary research network SAIL focuses on the full life-cycle of AI systems. Before this, I performed my PhD research in the Intelligent Systems group at the University of Groningen.

Interests

  • Machine Learning
  • Computational Intelligence
  • Statistics and Statistical Physics
  • Data Science

Education

  • Postdoc in Machine Learning

    Machine Learning group, Bielefeld University

  • PhD in Machine Learning

    Intelligent Systems group, University of Groningen, PhD thesis

  • MSc. in Computing Science

    University of Groningen

Projects

SAIL

A multidisciplinary project focusing on transparent AI, human agency, safety and resource-efficiency.

Statistical Physics of Learning

Typical case analysis of machine learning scenarios

Reinforcement learning based control of dynamical systems

Employing AI techniques to model and control dynamical systems.

Machine Learning for health

Using Machine Learning methods to improve health care

Region of Smart Factories (RoSF), Industry 4.0

Focuses on the development of intelligent, connected and customized production processes

Recent Publications

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Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection

We build a linear dynamical model for complex fluid flows using a Machine Learning architecture based on Koopman theory.

Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis

In this work we show theoretically that using GELU activation in neural networks induces continuous phase transitions and we analyse …

Off-line Learning Analysis for Soft Committee Machines with GELU Activation

In this work we show that the GELU activation function in two-layer neural networks causes a continuous phase transition, independent …

An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data

We present a typical Industry 4.0 case study: the real-time quality estimation of steel in a high-throughput production line using …

Supervised Learning in the Presence of Concept Drift: A modelling framework

A statistical physics based modelling framework is developed in which we study standard training algorithms (SGD, LVQ1) under concept …

Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation

Our systematic comparison of networks with ReLU and sigmoidal units in model situations reveals surprising differences in their …

Complex-valued embeddings of generic proximity data

We propose and evaluate an information-preserving complex-valued embedding for general non-psd proximity data.

On-line learning dynamics of ReLU neural networks using statistical physics techniques

We introduce exact macroscopic on-line learning dynamics of two-layer neural networks with ReLU units in the form of a system of …

Learning Vector Quantization and relevances in complex coefficient space

In this contribution, we consider the classification of time series and similar functional data which can be represented in complex …

Statistical Mechanics of On-Line Learning Under Concept Drift

We introduce a modeling framework for the investigation of on-line machine learning processes in non-stationary environments. We …

Recent & Upcoming Talks

Koopman-based Modeling of Rayleigh-Bénard Convection

In this talk I will present our recent work on Koopman-based surrogate modeling of Rayleigh-Bénard Convection.

Statistical Physics of Learning

In this talk I discuss the main principles behind the statistical physics of learning.

Modelling adversarial training

In this talk I discuss my recent work on analyzing adversarial training procedures that make machine learning models more robust.

An Industry 4.0 example: real-time quality control for steel-based mass production using Machine Learning on non-invasive sensor data

In this talk a typical Industry 4.0 setting is presented: the use of sensor measurements for real-time quality control and fault …

Feedback Alignment methods for training neural networks

In order to compute weight updates, backpropagation uses complete knowledge of the downstream weights in the network. In [1] it is …

Dynamics of on-line learning in two-layer neural networks in the presence of concept drift

In this talk I will discuss our recent work on learning in non-stationary situations. A modelling scenario of neural networks learning …

Dynamics of on-line learning in two-layer neural networks in the presence of concept drift

Towards a statistical physics analysis of multilayer ReLU neural networks

On-line learning dynamics of ReLU neural networks using statistical physics techniques

Online Learning Dynamics of ReLU Neural Networks

The rectifier activation function (Rectified Linear Unit: ReLU) has become popular in deep learning applications, mostly because the …

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