| Understanding data augmentation for classification: when to warp? SC Wong, A Gatt, V Stamatescu, MD McDonnell 2016 international conference on digital image computing: techniques and …, 2016 | 1305 | 2016 |
| What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology MD McDonnell, D Abbott PLoS computational biology 5 (5), e1000348, 2009 | 924 | 2009 |
| The benefits of noise in neural systems: bridging theory and experiment MD McDonnell, LM Ward Nature Reviews Neuroscience 12 (7), 415-425, 2011 | 831 | 2011 |
| Stochastic resonance MD McDonnell, NG Stocks, CEM Pearce, D Abbott Stochastic Resonance, 2008 | 379 | 2008 |
| Mathematical methods for spatially cohesive reserve design MD McDonnell, HP Possingham, IR Ball, EA Cousins Environmental Modeling & Assessment 7, 107-114, 2002 | 358 | 2002 |
| Deep extreme learning machines: supervised autoencoding architecture for classification MD Tissera, MD McDonnell Neurocomputing 174, 42-49, 2016 | 142 | 2016 |
| Methods for generating complex networks with selected structural properties for simulations: a review and tutorial for neuroscientists BJ Prettejohn, MJ Berryman, MD McDonnell Frontiers in computational neuroscience 5, 8783, 2011 | 127 | 2011 |
| Enhanced image classification with a fast-learning shallow convolutional neural network MD McDonnell, T Vladusich 2015 International Joint Conference on Neural Networks (IJCNN), 1-7, 2015 | 119 | 2015 |
| Acoustic scene classification using deep residual networks with late fusion of separated high and low frequency paths MD McDonnell, W Gao ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 110 | 2020 |
| Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the ‘extreme learning machine’algorithm MD McDonnell, MD Tissera, T Vladusich, A van Schaik, J Tapson PloS one 10 (8), e0134254, 2015 | 91 | 2015 |
| Optimal information transmission in nonlinear arrays through suprathreshold stochastic resonance MD McDonnell, NG Stocks, CEM Pearce, D Abbott Physics Letters A 352 (3), 183-189, 2006 | 91 | 2006 |
| Training wide residual networks for deployment using a single bit for each weight MD McDonnell arXiv preprint arXiv:1802.08530, 2018 | 89 | 2018 |
| An analysis of noise enhanced information transmission in an array of comparators MD McDonnell, D Abbott, CEM Pearce Microelectronics Journal 33 (12), 1079-1089, 2002 | 81 | 2002 |
| Ranpac: Random projections and pre-trained models for continual learning MD McDonnell, D Gong, A Parvaneh, E Abbasnejad, A van den Hengel Advances in Neural Information Processing Systems 36, 2024 | 80 | 2024 |
| Maximally Informative Stimuli and Tuning Curves<? format?> for Sigmoidal Rate-Coding Neurons and Populations MD McDonnell, NG Stocks Physical review letters 101 (5), 058103, 2008 | 73 | 2008 |
| A characterization of suprathreshold stochastic resonance in an array of comparators by correlation coefficient MD Mcdonnell, D Abbott, CEM Pearce Fluctuation and noise letters 2 (03), L205-L220, 2002 | 69 | 2002 |
| Neural population coding is optimized by discrete tuning curves AP Nikitin, NG Stocks, RP Morse, MD McDonnell Physical review letters 103 (13), 138101, 2009 | 58 | 2009 |
| Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance MD McDonnell, NG Stocks, D Abbott Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 75 (6 …, 2007 | 58 | 2007 |
| The application of deep convolutional neural networks to brain cancer images: a survey A Zadeh Shirazi, E Fornaciari, MD McDonnell, M Yaghoobi, Y Cevallos, ... Journal of personalized medicine 10 (4), 224, 2020 | 56 | 2020 |
| Characterization of young and old adult brains: An EEG functional connectivity analysis B Moezzi, LM Pratti, B Hordacre, L Graetz, C Berryman, LM Lavrencic, ... Neuroscience 422, 230-239, 2019 | 54 | 2019 |
Derek AbbottProfessor of Electrical & Electronic Engineering, University of Adelaide, Australiaadelaide.edu.au의 이메일 확인됨