I am a machine learning PhD student at the University of Edinburgh. My supervisors are Steve Renals and Iain Murray. My research is focused on density estimation with recurrent neural networks and its applications to speech and language. I am also a member of the the University of Edinburgh's team to build a social bot for the Amazon Alexa prize. I have past neuroimaging research studying the role of neurotransmitters in brain blood flow and schizophrenia.
My most recent work was developing the multiplicative long short-term memory (mLSTM) recurrent neural network architecture for language modelling. mLSTM combines the ideas of a multiplicative RNN and an LSTM. mLSTM was used by Open AI for unsupervised sentiment analysis.
Krause, B., Lu, L., Murray, I., & Renals, S. (2016). Multiplicative LSTM for sequence modelling. arXiv preprint arXiv:1609.07959.
Krause, B. (2015). Optimizing and Contrasting Recurrent Neural Network Architectures. arXiv preprint arXiv:1510.04953.
Krause, B., Lu, L., Murray, I., & Renals, S. (2015). On the Efficiency of Recurrent Neural Network Optimization Algorithms. NIPS Workshop on Optimization for Machine Learning, Montreal, Canada, 2015.
Krause, B. W., Wijtenburg, S. A., Holcomb, H. H., Kochunov, P., Wang, D. J., Hong, L. E., & Rowland, L. M. (2014). Anterior cingulate GABA levels predict whole-brain cerebral blood flow. Neuroscience letters, 561, 188-191.
Rowland, L. M., Krause, B. W., Wijtenburg, S. A., McMahon, R. P., Chiappelli, J., Nugent, K. L., ... & Hong, L. E. (2016). Medial frontal GABA is lower in older schizophrenia: a MEGA-PRESS with macromolecule suppression study. Molecular psychiatry, 21(2), 198-204.
Korenic, S. A., Nisonger, S. J., Krause, B. W., Wijtenburg, S. A., Hong, L. E., & Rowland, L. M. (2016). Effectiveness of fast mapping to promote learning in schizophrenia. Schizophrenia Research: Cognition, 4, 24-31.