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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
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Posts
Future Blog Post
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Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
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Blog Post number 2
Published:
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Blog Post number 1
Published:
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portfolio
publications
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing
Published in arXiv, 2019
This paper expands upon prior work on time-sensitive and variable-selection-free deep learning models for mortality prediction in the ICU. Currently under review.
Recommended citation: Deasy J, Liò P, Ercole A (2019). Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing. arXiv preprint arXiv:1909.07214. http://jacobdeasy.github.io/files/publications/2019-09-17-dynamic.pdf
Time-Sensitive Deep Learning for ICU Outcome Prediction Without Variable Selection or Cleaning
Published in Intensive Care Medicine Experimental, 2019
This paper is about preliminary findings of a time-sensitive and variable-selection-free deep learning model, for mortality prediction in the ICU, developed with Dr Ari Ercole and Prof. Pietro Liò at the University of Cambridge.
Recommended citation: Deasy J, Liò P, Ercole A (2019). Time-Sensitive Deep Learning for ICU Outcome Prediction Without Variable Selection or Cleaning. Intensive Care Medicine Experimental. 2019;1(1):368. http://jacobdeasy.github.io/files/publications/2019-09-27-esicm.pdf
Adaptive Prediction Timing for Electronic Health Records
Published in ICLR 2020, 2020
This paper presents an adaptive prediction timing method for sequences, based on equi-precision rather than equi-time intervals, applied to electronic health records. This work was carried out during my PhD with Dr Ari Ercole and Prof. Pietro Lio at the University of Cambridge and accepted at the Machine learning in real life workshop at ICLR 2020.
Recommended citation: Deasy J, Ercole A, and Lio P (2020). Adaptive Prediction Timing for Electronic Health Records. arXiv preprint arXiv:2003.02554. http://jacobdeasy.github.io/files/publications/2020-03-05-adaptive.pdf
Constraining Variational Inference with Geometric Jensen-Shannon Divergence
Published in NeurIPS, 2020
This paper presents geometric Jensen-Shannon VAEs, a generalisation of the beta-VAE family and a closed-form interpolation between forward and reverse Kullback-Liebler divergence. This was derived during my PhD with Prof. Pietro Lio at the University of Cambridge and accepted at NeurIPS 2020.
Recommended citation: Deasy J, Simidjievski N and Lio P (2020). Constraining Variational Inference with Geometric Jensen-Shannon Divergence. arXiv preprint arXiv:2006.10599. http://jacobdeasy.github.io/files/publications/2020-10-18-gjs.pdf
The r-Hunter-Saxton equation, smooth and singular solutions and their approximation
Published in Nonlinearity, 2020
This paper presents the r-Hunter-Saxton equation, a generalisation of the Hunter-Saxton equation derived and analysed during my MSci project with Prof. Colin Cotter at Imperial College London. Currently under review.
Recommended citation: Cotter C, Deasy J, Pryer T (2020). The r-Hunter-Saxton equation, smooth and singular solutions and their approximation. Nonlinearity, Volume 33, Number 12. http://jacobdeasy.github.io/files/publications/2020-10-23-rhs.pdf
Heavy-tailed denoising score matching
Published in arXiv, 2021
This paper presents heavy-tailed DSM for score-based models, a generalisation of DSM training which benefits from the use of the generalised normal distribution. This was derived during my PhD with Prof. Pietro Lio at the University of Cambridge and is currently at the pre-print stage.
Recommended citation: Deasy J, Simidjievski N and Lio P (2020). Constraining Variational Inference with Geometric Jensen-Shannon Divergence. arXiv preprint arXiv:2006.10599. http://jacobdeasy.github.io/files/publications/2021-12-17-heavy.pdf
talks
Visualisation of Deep Reinforcement Learning in Artificial Intelligence
Published:
Talk given at the concluding conference of the High Performance Computing in Life sciences, Engineering and Physics (HPC-LEAP) conference in the department of applied mathematics and theoretical physics at the University of Cambridge.
Predicting patient outcomes using electronic health records
Published:
Elevator pitch given while I was a first year PhD student as part of the Computer Laboratory’s One Minute Madness event.
Time-Sensitive Deep Learning for ICU Outcome Prediction Without Variable Selection or Cleaning
Published:
As one of the top submissions, Time-Sensitive Deep Learning for ICU Outcome Prediction Without Variable Selection or Cleaning was selected for oral presentation at the session From bytes to bedside: Improving intensive care with Data by the Congress Committee.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.