Mark D. McDonnell
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View article: Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning
Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning Open
Continual learning requires a model to adapt to ongoing changes in the data distribution, and often to the set of tasks to be performed. It is rare, however, that the data and task changes are completely unpredictable. Given a description …
View article: RanPAC: Random Projections and Pre-trained Models for Continual Learning
RanPAC: Random Projections and Pre-trained Models for Continual Learning Open
Continual learning (CL) aims to incrementally learn different tasks (such as classification) in a non-stationary data stream without forgetting old ones. Most CL works focus on tackling catastrophic forgetting under a learning-from-scratch…
View article: Can Personal Budget Management Services Improve Debt Repayments? A Study Using Budget Data*
Can Personal Budget Management Services Improve Debt Repayments? A Study Using Budget Data* Open
We investigate the effect of personal budget management services provided by a financial institution on undesirable debts of 4,256 individuals and families over a period of one year using objective budget data spanning three years. Results…
View article: Planet Four: A Neural Network's Search For Polar Spring-time Fans On Mars
Planet Four: A Neural Network's Search For Polar Spring-time Fans On Mars Open
Dark deposits visible from orbit appear in the Martian south polar region during the springtime. These are thought to form from explosive jets of carbon dioxide gas breaking through the thawing seasonal ice cap, carrying dust and dirt whic…
View article: Modern Value Based Reinforcement Learning: A Chronological Review
Modern Value Based Reinforcement Learning: A Chronological Review Open
Investigation of value based Reinforcement Learning algorithms exhibited a resurgence into mainstream research in 2015 following demonstration of super-human performance when applied to Atari 2600 games. Since then, significant media atten…
View article: Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging
Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging Open
Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explor…
View article: Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants
Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants Open
Despite the development and success of cochlear implants over several decades, wide inter-subject variability in speech perception is reported. This suggests that cochlear implant user-dependent factors limit speech perception at the indiv…
View article: Identifying risk factors for COVID-19 severity and mortality in the UK Biobank
Identifying risk factors for COVID-19 severity and mortality in the UK Biobank Open
Severe acute respiratory syndrome coronavirus has infected over 114 million people worldwide as of March 2021, with worldwide mortality rates ranging between 1-10%. We use information on up to 421,111 UK Biobank participants to identify po…
View article: Can we use machine learning to discover risk factors? Testing the proof of principle using data on >11,000 predictors and mortality in the UK Biobank
Can we use machine learning to discover risk factors? Testing the proof of principle using data on >11,000 predictors and mortality in the UK Biobank Open
Background We present a simple and fast hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. Methods Mortali…
View article: Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications
Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications Open
Research into machine learning (ML) for clinical vascular analysis, such as those useful for stroke and coronary artery disease, varies greatly between imaging modalities and vascular regions. Limited accessibility to large diverse patient…
View article: The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey
The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey Open
In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) c…
View article: The Impact of Pan-Sharpening and Spectral Resolution on Vineyard Segmentation through Machine Learning
The Impact of Pan-Sharpening and Spectral Resolution on Vineyard Segmentation through Machine Learning Open
Precision viticulture benefits from the accurate detection of vineyard vegetation from remote sensing, without a priori knowledge of vine locations. Vineyard detection enables efficient, and potentially automated, derivation of spatial mea…
View article: Single-Bit-per-Weight Deep Convolutional Neural Networks without Batch-Normalization Layers for Embedded Systems
Single-Bit-per-Weight Deep Convolutional Neural Networks without Batch-Normalization Layers for Embedded Systems Open
Batch-normalization (BN) layers are thought to be an integrally important layer type in today's state-of-the-art deep convolutional neural networks for computer vision tasks such as classification and detection. However, BN layers introduc…
View article: Characterization of young and old adult brains: An EEG functional connectivity analysis
Characterization of young and old adult brains: An EEG functional connectivity analysis Open
Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old…
View article: Anomaly Detection in Satellite Communications Systems using LSTM Networks
Anomaly Detection in Satellite Communications Systems using LSTM Networks Open
Most satellite communications monitoring tools use simple thresholding of univariate measurements to alert the operator to unusual events. This approach suffers from frequent false alarms, and is moreover unable to detect sequence or multi…
View article: Diagnosing Convolutional Neural Networks using their Spectral Response
Diagnosing Convolutional Neural Networks using their Spectral Response Open
Convolutional Neural Networks (CNNs) are a class of artificial neural networks whose computational blocks use convolution, together with other linear and non-linear operations, to perform classification or regression. This paper explores t…
View article: Simulation of electromyographic recordings following transcranial magnetic stimulation
Simulation of electromyographic recordings following transcranial magnetic stimulation Open
Transcranial magnetic stimulation (TMS) is a technique that enables noninvasive manipulation of neural activity and holds promise in both clinical and basic research settings. The effect of TMS on the motor cortex is often measured by elec…
View article: Training wide residual networks for deployment using a single bit for each weight
Training wide residual networks for deployment using a single bit for each weight Open
For fast and energy-efficient deployment of trained deep neural networks on resource-constrained embedded hardware, each learned weight parameter should ideally be represented and stored using a single bit. Error-rates usually increase whe…
View article: Training wide residual networks for deployment using a single bit for\n each weight
Training wide residual networks for deployment using a single bit for\n each weight Open
For fast and energy-efficient deployment of trained deep neural networks on\nresource-constrained embedded hardware, each learned weight parameter should\nideally be represented and stored using a single bit. Error-rates usually\nincrease …
View article: Phase changes in neuronal postsynaptic spiking due to short term plasticity
Phase changes in neuronal postsynaptic spiking due to short term plasticity Open
In the brain, the postsynaptic response of a neuron to time-varying inputs is determined by the interaction of presynaptic spike times with the short-term dynamics of each synapse. For a neuron driven by stochastic synapses, synaptic depre…
View article: Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance
Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance Open
Suprathreshold stochastic resonance (SSR) is a distinct form of stochastic resonance, which occurs in multilevel parallel threshold arrays with no requirements on signal strength. In the generic SSR model, an optimal weighted decoding sche…
View article: Understanding Data Augmentation for Classification: When to Warp?
Understanding Data Augmentation for Classification: When to Warp? Open
In this paper we investigate the benefit of augmenting data with synthetically created samples when training a machine learning classifier. Two approaches for creating additional training samples are data warping, which generates additiona…
View article: Editorial: Neuronal Stochastic Variability: Influences on Spiking Dynamics and Network Activity
Editorial: Neuronal Stochastic Variability: Influences on Spiking Dynamics and Network Activity Open
EDITORIAL article Front. Comput. Neurosci., 21 April 2016 Volume 10 - 2016 | https://doi.org/10.3389/fncom.2016.00038
View article: Too good to be true: when overwhelming evidence fails to convince
Too good to be true: when overwhelming evidence fails to convince Open
Is it possible for a large sequence of measurements or observations, which support a hypothesis, to counterintuitively decrease our confidence? Can unanimous support be too good to be true? The assumption of independence is often made in g…