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Pattarawat Chormai

I am a PhD candidate at the Max Planck School of Cognition. At the intersection of computer and cognitive sciences, I attempt to investigate how we can verify and explain deep learning models. Therefore, my research interests include:

  • Deep Learning and its interpretability
  • Knowledge acquisition and language representation in the brain
  • Natural language processing
  • Data visualization and journalism
  • Civic technology

Previously, I completed a dual master's degree from Eindhoven University of Technology and Technical University of Berlin, under EIT Digital Master School. My master's thesis Designing RNNs for Explainability was supervised by Prof. Dr. Klaus-Robert Müller and Dr. Grégoire Montavon.

Selected Projects

Parliament Listening

Parliament Listening2019

A pilot project that aims to collect what being discussed in each parliament meeting. Having this kind of data will allow us to build various tools and applications that individuals can easily engage or interact. Hence, they would be well informed about the country's situtaion and potentially make a better decision in next elections.
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Designing RNNs for Explainability (M.Sc. Thesis)

Designing RNNs for Explainability (M.Sc. Thesis)2018

We study how the architecture of RNNs influences the level of explainability. We use existing explaination methods, such as LRP, Deep Taylor Decomposition, and Guided Backprop, to explain prediction from RNNs. Our experiments show that RNN architectures can have significantly different levels of explainability although they perform equally well in terms of an objective function.
Monte Carlo Particle Filter for Localization

Monte Carlo Particle Filter for Localization2018

Particle filter algorithm is a nonparametric approach using a set of particles to approximate the posterior distribution of some random processes. In this project, we develop a robot simulator and implement the algorithm to localize the location of a robot in mazes.
Black Box Optimization using RNNs

Black Box Optimization using RNNs2017

We extend on the work of Chen et al. (2016), who introduce a new learning-based approach to global Black-Box optimization. They train a LSTM model on functions sampled from Gaussian processes, learning to find an point that those training functions. We verified the claims made by the authors and conducted further experiments using various loss functions. We also applied these learned LSTM models to airfoil optimization and hyperparameter tuning of a SVM classifier. Our experiments show that the learning approach can produces optimizers that perform comparably to state-of-the-art Black-Box optimization algorithms on these benchmarks