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2020 – today
- 2024
[i15]Mark D. McDonnell, Dong Gong, Ehsan Abbasnejad, Anton van den Hengel:
Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning. CoRR abs/2403.07356 (2024)- 2023
- [c38]Mark C. McKenzie, Mark D. McDonnell:
Hyperparameter Selection in Reinforcement Learning Using the "Design of Experiments" Method. INNS DLIA@IJCNN 2023: 11-24 - [c37]Mark D. McDonnell, Dong Gong, Amin Parvaneh, Ehsan Abbasnejad, Anton van den Hengel:
RanPAC: Random Projections and Pre-trained Models for Continual Learning. NeurIPS 2023 - [i14]Mark D. McDonnell, Dong Gong, Amin Parvaneh, Ehsan Abbasnejad, Anton van den Hengel:
RanPAC: Random Projections and Pre-trained Models for Continual Learning. CoRR abs/2307.02251 (2023) - 2022
- [j22]Mark McKenzie
, Mark D. McDonnell:
Modern Value Based Reinforcement Learning: A Chronological Review. IEEE Access 10: 134704-134725 (2022) - 2021
- [j21]Namrata Nath, Sang-Heon Lee, Mark D. McDonnell, Ivan Lee:
The quest for better clinical word vectors: Ontology based and lexical vector augmentation versus clinical contextual embeddings. Comput. Biol. Medicine 134: 104433 (2021) - 2020
- [j20]Eriita G. Jones, Sebastien C. Wong, Anthony Milton, Joseph Sclauzero, Holly Whittenbury, Mark D. McDonnell:
The Impact of Pan-Sharpening and Spectral Resolution on Vineyard Segmentation through Machine Learning. Remote. Sens. 12(6): 934 (2020) - [c36]Mark D. McDonnell, Wei Gao:
Acoustic Scene Classification Using Deep Residual Networks with Late Fusion of Separated High and Low Frequency Paths. ICASSP 2020: 141-145 - [c35]Wei Gao, Ahmad Hashemi-Sakhtsari, Mark D. McDonnell:
End-to-End Phoneme Recognition using Models from Semantic Image Segmentation. IJCNN 2020: 1-7
2010 – 2019
- 2019
- [c34]Mark D. McDonnell, Hesham Mostafa, Runchun Wang, André van Schaik:
Single-Bit-per-Weight Deep Convolutional Neural Networks without Batch-Normalization Layers for Embedded Systems. ACIRS 2019: 197-204 - [c33]Iqbal Madakkatel, Belinda A. Chiera, Mark D. McDonnell:
Predicting Financial Well-Being Using Observable Features and Gradient Boosting. Australasian Conference on Artificial Intelligence 2019: 228-239 - [c32]Mark D. McDonnell, Bahar Moezzi, Russell S. A. Brinkworth:
Using Style-Transfer to Understand Material Classification for Robotic Sorting of Recycled Beverage Containers. DICTA 2019: 1-8 - [c31]Rapeeporn Chamchong, Wei Gao, Mark D. McDonnell:
Thai Handwritten Recognition on Text Block-Based from Thai Archive Manuscripts. ICDAR 2019: 1346-1351 - [c30]Mark McKenzie, Mark D. McDonnell:
Degradation of Performance in Reinforcement Learning with State Measurement Uncertainty. MilCIS 2019: 1-5 - [i13]Mark D. McDonnell, Hesham Mostafa, Runchun Wang, André van Schaik:
Single-bit-per-weight deep convolutional neural networks without batch-normalization layers for embedded systems. CoRR abs/1907.06916 (2019) - 2018
- [c29]Victor Stamatescu, Mark D. McDonnell:
Diagnosing Convolutional Neural Networks using Their Spectral Response. DICTA 2018: 1-8 - [c28]Mark D. McDonnell:
Training wide residual networks for deployment using a single bit for each weight. ICLR (Poster) 2018 - [c27]Samya Bagchi, Mark D. McDonnell:
A model of neurobiologically plausible least-squares learning in visual cortex. IJCNN 2018: 1-8 - [c26]Lachlan J. Gunn, Peter Smet, Edward Arbon, Mark D. McDonnell:
Anomaly Detection in Satellite Communications Systems using LSTM Networks. MilCIS 2018: 1-6 - [i12]Mark D. McDonnell:
Training wide residual networks for deployment using a single bit for each weight. CoRR abs/1802.08530 (2018) - [i11]Victor Stamatescu, Mark D. McDonnell:
Diagnosing Convolutional Neural Networks using their Spectral Response. CoRR abs/1810.03241 (2018) - 2017
- [j19]Mark D. McDonnell, Bruce P. Graham:
Phase changes in neuronal postsynaptic spiking due to short term plasticity. PLoS Comput. Biol. 13(9) (2017) - [j18]Xiao Gao, David B. Grayden, Mark D. McDonnell:
Modeling Electrode Place Discrimination in Cochlear Implant Stimulation. IEEE Trans. Biomed. Eng. 64(9): 2219-2229 (2017) - [j17]Sebastien C. Wong, Victor Stamatescu, Adam Gatt, David A. Kearney, Ivan Lee, Mark D. McDonnell:
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition. IEEE Trans. Image Process. 26(10): 4669-4683 (2017) - [c25]Wei Gao, Mark D. McDonnell:
Analysis of Gradient Degradation and Feature Map Quality in Deep All-Convolutional Neural Networks Compared to Deep Residual Networks. ICONIP (2) 2017: 612-621 - [c24]Mahmood Yousefi-Azar, Len Hamey, Vijay Varadharajan, Mark D. McDonnell:
Fast, Automatic and Scalable Learning to Detect Android Malware. ICONIP (5) 2017: 848-857 - [c23]Philip de Chazal, Mark D. McDonnell:
Regularized training of the extreme learning machine using the conjugate gradient method. IJCNN 2017: 1802-1808 - [c22]Mahmood Yousefi-Azar, Mark D. McDonnell:
Semi-supervised convolutional extreme learning machine. IJCNN 2017: 1968-1974 - [i10]Sebastien C. Wong, Victor Stamatescu, Adam Gatt, David A. Kearney, Ivan Lee, Mark D. McDonnell:
Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition. CoRR abs/1704.06415 (2017) - 2016
- [j16]Mark D. McDonnell, Joshua H. Goldwyn, Benjamin Lindner:
Editorial: Neuronal Stochastic Variability: Influences on Spiking Dynamics and Network Activity. Frontiers Comput. Neurosci. 10: 38 (2016) - [j15]Migel D. Tissera, Mark D. McDonnell:
Deep extreme learning machines: supervised autoencoding architecture for classification. Neurocomputing 174: 42-49 (2016) - [j14]Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell:
Ion channel noise can explain firing correlation in auditory nerves. J. Comput. Neurosci. 41(2): 193-206 (2016) - [c21]Sebastien C. Wong, Adam Gatt, Victor Stamatescu, Mark D. McDonnell:
Understanding Data Augmentation for Classification: When to Warp? DICTA 2016: 1-6 - [c20]Philip de Chazal, Mark D. McDonnell:
Efficient computation of the Levenberg-Marquardt algorithm for feedforward networks with linear outputs. IJCNN 2016: 68-75 - [c19]Migel D. Tissera, Mark D. McDonnell:
Enhancing deep extreme learning machines by error backpropagation. IJCNN 2016: 735-739 - [c18]Daniel E. Padilla, Mark D. McDonnell:
Integrating convolutional neural networks into a sparse distributed representation model based on mammalian cortical learning. IJCNN 2016: 1187-1194 - [c17]Mark D. McDonnell, Robby G. McKilliam, Philip de Chazal:
On the importance of pair-wise feature correlations for image classification. IJCNN 2016: 2290-2297 - [c16]Migel D. Tissera, Mark D. McDonnell:
Modular expansion of the hidden layer in Single Layer Feedforward neural Networks. IJCNN 2016: 2939-2945 - [i9]Sebastien C. Wong, Adam Gatt, Victor Stamatescu, Mark D. McDonnell:
Understanding data augmentation for classification: when to warp? CoRR abs/1609.08764 (2016) - 2015
- [j13]Priscilla E. Greenwood, Mark D. McDonnell, Lawrence M. Ward:
Dynamics of Gamma Bursts in Local Field Potentials. Neural Comput. 27(1): 74-103 (2015) - [c15]Mark D. McDonnell, Tony Vladusich:
Enhanced image classification with a fast-learning shallow convolutional neural network. IJCNN 2015: 1-7 - [c14]Xiao Gao, David B. Grayden, Mark D. McDonnell:
Modeling electrode place discrimination in cochlear implants: Analysis of the influence of electrode array insertion depth. NER 2015: 691-694 - [i8]Mark D. McDonnell, Tony Vladusich:
Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network. CoRR abs/1503.04596 (2015) - 2014
- [j12]Bahar Moezzi, Nicolangelo Iannella, Mark D. McDonnell:
Modelling the influence of short term depression in vesicle release and stochastic calcium channel gating on auditory nerve spontaneous firing statistics. Frontiers Comput. Neurosci. 8: 163 (2014) - [j11]Mark D. McDonnell, Kwabena Boahen, Auke Jan Ijspeert, Terrence J. Sejnowski:
Engineering intelligent electronic systems based on computational neuroscience [scanning the issue]. Proc. IEEE 102(5): 646-651 (2014) - [c13]Siyi Wang, Weisi Guo, Mark D. McDonnell:
Downlink interference estimation without feedback for heterogeneous network interference avoidance. ICT 2014: 82-87 - [c12]Siyi Wang, Weisi Guo, Song Qiu, Mark D. McDonnell:
Performance of macro-scale molecular communications with sensor cleanse time. ICT 2014: 363-368 - [c11]Siyi Wang, Weisi Guo, Mark D. McDonnell:
Distance distributions for real cellular networks. INFOCOM Workshops 2014: 181-182 - [c10]Siyi Wang, Weisi Guo, Mark D. McDonnell:
Transmit pulse shaping for molecular communications. INFOCOM Workshops 2014: 209-210 - [c9]Xiao Gao, David B. Grayden, Mark D. McDonnell:
Using convex optimization to compute channel capacity in a channel model of cochlear implant stimulation. ISIT 2014: 2919-2923 - [c8]Xiao Gao, David B. Grayden, Mark D. McDonnell:
Inferring the dynamic range of electrode current by using an information theoretic model of cochlear implant stimulation. ITW 2014: 346-350 - [c7]Migel D. Tissera, Mark D. McDonnell:
Enabling 'Question Answering' in the MBAT Vector Symbolic Architecture by Exploiting Orthogonal Random Matrices. ICSC 2014: 171-174 - [c6]Daniel E. Padilla, Mark D. McDonnell:
A Neurobiologically Plausible Vector Symbolic Architecture. ICSC 2014: 242-245 - [i7]Siyi Wang, Weisi Guo, Mark D. McDonnell:
Downlink Interference Estimation without Feedback for Heterogeneous Network Interference Avoidance. CoRR abs/1404.0123 (2014) - [i6]Siyi Wang, Weisi Guo, Song Qiu, Mark D. McDonnell:
Performance of Macro-Scale Molecular Communications with Sensor Cleanse Time. CoRR abs/1404.0127 (2014) - [i5]Siyi Wang, Weisi Guo, Mark D. McDonnell:
Distance Distributions for Real Cellular Networks. CoRR abs/1404.3099 (2014) - [i4]Siyi Wang, Weisi Guo, Mark D. McDonnell:
Transmit Pulse Shaping for Molecular Communications. CoRR abs/1404.3104 (2014) - [i3]Mark D. McDonnell, Migel D. Tissera, André van Schaik, Jonathan Tapson:
Fast, simple and accurate handwritten digit classification using extreme learning machines with shaped input-weights. CoRR abs/1412.8307 (2014) - 2013
- [j10]Lubomir Kostal, Petr Lánský, Mark D. McDonnell:
Metabolic cost of neuronal information in an empirical stimulus-response model. Biol. Cybern. 107(3): 355-365 (2013) - [j9]Ashutosh Mohan, Mark D. McDonnell, Christian Stricker:
Interaction of short-term depression and firing dynamics in shaping single neuron encoding. Frontiers Comput. Neurosci. 7: 41 (2013) - [j8]Mark D. McDonnell, Ashutosh Mohan, Christian Stricker:
Mathematical analysis and algorithms for efficiently and accurately implementing stochastic simulations of short-term synaptic depression and facilitation. Frontiers Comput. Neurosci. 7: 58 (2013) - [c5]Mark D. McDonnell, Lawrence M. Ward:
Identifying positive roles for endogenous stochastic noise during computation in neural systems. EMBC 2013: 5232-5235 - [c4]Xiao Gao, David B. Grayden, Mark D. McDonnell:
Information theoretic optimization of cochlear implant electrode usage probabilities. EMBC 2013: 5974-5977 - 2012
- [c3]Alexey S. Moroz, Mark D. McDonnell, Anthony N. Burkitt, David B. Grayden, Hamish Meffin:
Information theoretic inference of the optimal number of electrodes for future cochlear implants using a spiral cochlea model. EMBC 2012: 2965-2968 - 2011
- [j7]Mark D. McDonnell, Shiro Ikeda, Jonathan H. Manton:
An introductory review of information theory in the context of computational neuroscience. Biol. Cybern. 105(1): 55-70 (2011) - [j6]Brenton J. Prettejohn, Matthew J. Berryman, Mark D. McDonnell:
Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists. Frontiers Comput. Neurosci. 5: 11 (2011) - [j5]Mark D. McDonnell:
Is electrical noise useful? [Point of View]. Proc. IEEE 99(2): 242-246 (2011) - [i2]Mark D. McDonnell, Shiro Ikeda, Jonathan H. Manton:
An Introductory Review of Information Theory in the Context of Computational Neuroscience. CoRR abs/1107.2984 (2011) - 2010
- [j4]Mark D. McDonnell, Nigel G. Stocks, Pierre-Olivier Amblard:
Communication of uncoded sensor measurements through nanoscale binary-node stochastic pooling networks. Nano Commun. Networks 1(3): 209-223 (2010) - [j3]Mark D. McDonnell, Anthony N. Burkitt, David B. Grayden, Hamish Meffin, Alex J. Grant:
A channel model for inferring the optimal number of electrodes for future cochlear implants. IEEE Trans. Inf. Theory 56(2): 928-940 (2010)
2000 – 2009
- 2009
- [j2]Mark D. McDonnell, Derek Abbott:
What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology. PLoS Comput. Biol. 5(5) (2009) - [j1]Mark D. McDonnell, Nigel G. Stocks:
Suprathreshold stochastic resonance. Scholarpedia 4(6): 6508 (2009) - [i1]Mark D. McDonnell, Adrian P. Flitney:
Signal acquisition via polarization modulation in single photon sources. CoRR abs/0911.3668 (2009) - 2004
- [c2]Mark D. McDonnell, Derek Abbott:
Signal reconstruction via noise through a system of parallel threshold nonlinearities. ICASSP (2) 2004: 809-812 - [c1]Mark D. McDonnell, Derek Abbott:
Optimal quantization in neural coding. ISIT 2004: 494
Coauthor Index
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last updated on 2024-08-06 21:01 CEST by the dblp team
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