| 2021 |
Shirazi, A. Z., McDonnell, M. D., Fornaciari, E., Bagherian, N. S., Scheer, K. G., Samuel, M. S., . . . Gomez, G. A. (2021). A deep convolutional neural network for segmentation of whole-slide pathology images in glioblastoma. In CLINICAL CANCER RESEARCH Vol. 27 (pp. 2 pages). AMER ASSOC CANCER RESEARCH.
DOI |
| 2020 |
McDonnell, M. D., & Gao, W. (2020). Acoustic Scene Classification Using Deep Residual Networks with Late Fusion of Separated High and Low Frequency Paths. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings Vol. 2020-May (pp. 141-145). New York, NY, USA: IEEE.
DOI Scopus43 WoS35 |
| 2020 |
Gao, W., Hashemi-Sakhtsari, A., & McDonnell, M. D. (2020). End-to-End Phoneme Recognition using Models from Semantic Image Segmentation. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 7 pages). New York, NY, USA: IEEE.
DOI Scopus1 |
| 2019 |
Madakkatel, I., Chiera, B., & McDonnell, M. D. (2019). Predicting Financial Well-Being Using Observable Features and Gradient Boosting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11919 LNAI (pp. 228-239). Springer International Publishing.
DOI |
| 2019 |
McDonnell, M. D., Mostafa, H., Wang, R., & Schaik, A. (2019). Single-Bit-per-Weight Deep Convolutional Neural Networks without Batch-Normalization Layers for Embedded Systems. In Proceedings of the 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS 2019) (pp. 197-204). Nagoya, Japan: IEEE.
DOI Scopus2 WoS2 |
| 2019 |
McKenzie, M., & Mcdonnell, M. D. (2019). Degradation of Performance in Reinforcement Learning with State Measurement Uncertainty. In 2019 Military Communications and Information Systems Conference, MilCIS 2019 - Proceedings. IEEE.
DOI Scopus2 |
| 2019 |
McDonnell, M. D., Moezzi, B., & Brinkworth, R. S. A. (2019). Using Style-Transfer to Understand Material Classification for Robotic Sorting of Recycled Beverage Containers. In Proceedings of the Digital Image Computing: Techniques and Applications (DICTA 2019) (pp. 1-8). Perth, Australia: IEEE.
DOI |
| 2019 |
Chamchong, R., Gao, W., & McDonnell, M. D. (2019). Thai handwritten recognition on text block-based from thai archive manuscripts. In Proceedings of the International Conference on Document Analysis and Recognition, ICDAR (pp. 1346-1351). IEEE.
DOI Scopus8 |
| 2019 |
Stamatescu, V., & McDonnell, M. D. (2019). Diagnosing Convolutional Neural Networks using Their Spectral Response. In M. Murshed, M. Paul, M. Asikuzzaman, M. Pickering, A. Natu, A. RoblesKelly, . . . A. Rahman (Eds.), 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018 (pp. 603-610). Canberra, AUSTRALIA: IEEE.
DOI Scopus1 WoS1 |
| 2018 |
McDonnell, M. D. (2018). Training wide residual networks for deployment using a single bit for each weight. In 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings.
Scopus15 |
| 2018 |
Bagchi, S., & McDonnell, M. D. (2018). A model of neurobiologically plausible least-squares learning in visual cortex. In 2018 International Joint Conference on Neural Networks Vol. 2018-July (pp. 1-8). New York, NY, USA: IEEE.
DOI |
| 2018 |
Gunn, L., Smet, P., Arbon, E., & McDonnell, M. D. (2018). Anomaly Detection in Satellite Communications Systems using LSTM Networks. In 2018 Military Communications and Information Systems Conference, MilCIS 2018 - Proceedings (pp. 6 pages). Canberra, AUSTRALIA: IEEE.
DOI Scopus7 WoS4 |
| 2017 |
Yousefi-Azar, M., & McDonnell, M. D. (2017). Semi-supervised convolutional extreme learning machine. In Proceedings of the International Joint Conference on Neural Networks Vol. 2017-May (pp. 1968-1974). Anchorage, AK: IEEE.
DOI Scopus9 WoS8 |
| 2017 |
De Chazal, P., & McDonnell, M. (2017). Regularized training of the extreme learning machine using the conjugate gradient method. In 2017 International Joint Conference on Neural Networks (IJCNN) Vol. 2017-May (pp. 1802-1808). Anchorage, AK: IEEE.
DOI Scopus4 WoS4 |
| 2017 |
Gao, W., & McDonnell, M. D. (2017). Analysis of Gradient Degradation and Feature Map Quality in Deep All-Convolutional Neural Networks Compared to Deep Residual Networks. In D. Liu, S. Xie, Y. Li, D. Zhao, & E. S. M. ElAlfy (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10635 LNCS (pp. 612-621). Guangzhou, PEOPLES R CHINA: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus1 WoS1 |
| 2017 |
Yousefi-Azar, M., Hamey, L., Varadharajan, V., & McDonnell, M. D. (2017). Fast, automatic and scalable learning to detect android malware. In D. Liu, S. Xie, Y. Li, D. Zhao, & E. S. M. ElAlfy (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10638 LNCS (pp. 848-857). Guangzhou, PEOPLES R CHINA: SPRINGER INTERNATIONAL PUBLISHING AG.
DOI Scopus5 WoS5 |
| 2016 |
Stamatescu, V., Wong, S., McDonnell, M. D., & Kearney, D. (2016). Learned filters for object detection in multi-object visual tracking. In F. A. Sadjadi, & A. Mahalanobis (Eds.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 9844 (pp. 14 pages). Baltimore, MD: SPIE-INT SOC OPTICAL ENGINEERING.
DOI Scopus3 WoS2 |
| 2016 |
Tissera, M., & McDonnell, M. (2016). Modular expansion of the hidden layer in Single Layer Feedforward neural Networks. In 2016 Intemational Joint Conference on Neural Networks (IJCNN) Vol. 2016-October (pp. 2939-2945). Vancouver, Canada: IEEE.
DOI Scopus5 WoS5 |
| 2016 |
McDonnell, M., McKilliam, R., & De Chazal, P. (2016). On the importance of pair-wise feature correlations for image classification. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2016) Vol. 2016-October (pp. 2290-2297). http://www.ijcnn.org: IEEE.
DOI Scopus3 WoS1 |
| 2016 |
Padilla, D., & McDonnell, M. (2016). Integrating convolutional neural networks into a sparse distributed representation model based on mammalian cortical learning. In 2016 Intemational Joint Conference on Neural Networks (IJCNN) Vol. 2016-October (pp. 1187-1194). Vancouver, Canada: IEEE.
DOI |
| 2016 |
De Chazal, P., & McDonnell, M. (2016). Efficient computation of the Levenberg-Marquardt algorithm for feedforward networks with linear outputs. In 2016 Intemational Joint Conference on Neural Networks (IJCNN) Vol. 2016-October (pp. 68-75). Vancouver, Canada: IEEE.
DOI Scopus2 WoS1 |
| 2016 |
Tissera, M. D., & McDonnell, M. D. (2016). Enhancing deep extreme learning machines by error backpropagation. In Proceedings of the International Joint Conference on Neural Networks Vol. 2016-October (pp. 735-739). Vancouver, CANADA: IEEE.
DOI Scopus3 WoS4 |
| 2016 |
Wong, S. C., Gatt, A., Stamatescu, V., & McDonnell, M. D. (2016). Understanding Data Augmentation for Classification: When to Warp?. In 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016. IEEE.
DOI Scopus571 |
| 2015 |
Gao, X., Grayden, D., & McDonnell, M. (2015). Modeling electrode place discrimination in cochlear implants: Analysis of the influence of electrode array insertion depth. In International IEEE/EMBS Conference on Neural Engineering, NER Vol. 2015-July (pp. 691-694). Montpellier, FRANCE: IEEE.
DOI Scopus2 WoS2 |
| 2015 |
McDonnell, M., & Vladusich, T. (2015). Enhanced image classification with a fast-learning shallow convolutional neural network. In Proceedings of the International Joint Conference on Neural Networks Vol. 2015-September (pp. 1-7). online: IEEE.
DOI Scopus72 |
| 2014 |
Gao, X., Graydeny, D., & McDonnell, M. (2014). Inferring the dynamic range of electrode current by using an information theoretic model of cochlear implant stimulation. In Proceedings of the IEEE Information Theory Workshop (ITW 2014) (pp. 346-350). Piscataway, NJ: IEEE.
DOI Scopus3 WoS2 |
| 2014 |
Gao, X., Grayden, D., & McDonnell, M. (2014). Using convex optimization to compute channel capacity in a channel model of cochlear implant stimulation. In IEEE International Symposium on Information Theory - Proceedings (pp. 2919-2923). Honolulu, HI: IEEE.
DOI Scopus2 WoS2 |
| 2014 |
Wang, S., Guo, W., & McDonnell, M. (2014). Distance distributions for real cellular networks. In Proceedings - IEEE INFOCOM (pp. 181-182). Toronto, CANADA: IEEE.
DOI Scopus5 WoS3 |
| 2014 |
Wang, S., Guo, W., & McDonnell, M. (2014). Transmit pulse shaping for molecular communications. In Proceedings - IEEE INFOCOM (pp. 209-210). Toronto, CANADA: IEEE.
DOI Scopus20 WoS15 |
| 2014 |
Wang, S., Guo, W., Qiu, S., & McDonnell, M. (2014). Performance of macro-scale molecular communications with sensor cleanse time. In 2014 21st International Conference on Telecommunications, ICT 2014 (pp. 363-368). Lisbon, PORTUGAL: IEEE.
DOI Scopus12 WoS11 |
| 2014 |
Wang, S., Guo, W., & McDonnell, M. (2014). Downlink interference estimation without feedback for heterogeneous network interference avoidance. In 2014 21st International Conference on Telecommunications, ICT 2014 (pp. 82-87). Lisbon, PORTUGAL: IEEE.
DOI Scopus8 WoS5 |
| 2014 |
Padilla, D., & McDonnell, M. (2014). A neurobiologically plausible vector symbolic architecture. In Proceedings of the 8th IEEE International Conference on Semantic Computing (ICSC 2014) (pp. 242-245). online: IEEE.
DOI Scopus3 WoS2 |
| 2014 |
Tissera, M., & McDonnell, M. (2014). Enabling 'question answering' in the MBAT vector symbolic architecture by exploiting orthogonal random matrices. In Proceedings - 2014 IEEE International Conference on Semantic Computing, ICSC 2014 (pp. 171-174). Online: IEEE.
DOI Scopus2 WoS2 |
| 2013 |
Padilla, D., Brinkworth, R., & McDonnell, M. (2013). Performance of a hierarchical temporal memory network in noisy sequence learning. In W. Wahab, & A. Muis (Eds.), Proceeding - IEEE CYBERNETICSCOM 2013: IEEE International Conference on Computational Intelligence and Cybernetics (pp. 45-51). Yogyakarta, INDONESIA: IEEE.
DOI Scopus15 WoS12 |
| 2013 |
McDonnell, M., & Ward, L. (2013). Identifying positive roles for endogenous stochastic noise during computation in neural systems. In Proceedings, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013) Vol. 2013 (pp. 5232-5235). Osaka, Japan: IEEE.
DOI Scopus1 WoS1 Europe PMC1 |
| 2013 |
Gao, X., Grayden, D., & McDonnell, M. (2013). Information theoretic optimization of cochlear implant electrode usage probabilities. In Proceedings, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013) Vol. 2013 (pp. 5974-5977). Osaka, Japan: IEEE.
DOI Scopus7 WoS7 Europe PMC2 |
| 2012 |
Moroz, A., McDonnell, M., Burkitt, A., Grayden, D., & Meffin, H. (2012). Information theoretic inference of the optimal number of electrodes for future cochlear implants using a spiral cochlea model. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS Vol. 2012 (pp. 2965-2968). San Diego, California: IEEE.
DOI Scopus6 WoS6 Europe PMC2 |
| 2011 |
Prettejohn, B., & McDonnell, M. (2011). Effect of network topology in opinion formation models. In C. Guttmann, F. Dignum, & M. Georgeff (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 6066 LNAI (pp. 114-124). York Univ, Toronto, CANADA: SPRINGER-VERLAG BERLIN.
DOI Scopus3 WoS3 |
| 2009 |
McDonnell, M. D. (2009). Applying stochastic signal quantization theory to the robust digitization of noisy analog signals. In V. In, P. Longhini, & A. Palacious (Eds.), Understanding Complex Systems Vol. 2009 (pp. 249-261). Poipu Beach, HI: SPRINGER-VERLAG BERLIN.
DOI Scopus1 |
| 2009 |
Li, F., McDonnell, M. D., Amblard, P. O., & Grant, A. J. (2009). Sensor selection for distributed detection via multiaccess channels. In Proceedings of the 2009 Australian Communications Theory Workshop, AusCTW 2009 (pp. 77-82). Univ New S Wales, Sydney, AUSTRALIA: IEEE.
DOI Scopus1 |
| 2009 |
Stocks, N. G., Nikitin, A. P., McDonnell, M. D., & Morse, R. P. (2009). The role of stochasticity in an information-optimal neural population code. In M. Inoue, S. Ishii, Y. Kabashima, & M. Okada (Eds.), Journal of Physics: Conference Series Vol. 197 (pp. 11 pages). Kyoto, JAPAN: IOP PUBLISHING LTD.
DOI |
| 2008 |
McDonnell, M., Amblard, P., & Stocks, N. (2008). Stochastic Pooling Networks: a biologically inspired model for robust signal detection and compression. In D. Kearney, V. Nguyen, G. Gioiosa, & T. Hendtlass (Eds.), 2008 Third International Conference on Bio-Inspired Computing: Theories and Applications (pp. 75-81). Adelaide, South Australia: IEEE.
DOI Scopus1 WoS1 |
| 2008 |
McDonnell, M. D. (2008). Reliable communication and sensing via parallel redundancy in noisy digital receivers. In Australian Communications Theory Workshop, 2008, AusCTW 2008 (pp. 23-28). Christchurch, New Zealand: IEEE.
DOI Scopus5 WoS4 |
| 2008 |
McDonnell, M. D. (2008). Signal compression in biological sensory systems: information theoretic performance limits. In D. V. Nicolau, D. Abbott, K. KalantarZadeh, T. DiMatteo, & S. M. Bezrukov (Eds.), BioMEMS and Nanotechnology III Vol. 6799 (pp. 679913-1-679913-10). Canberra, ACT: SPIE.
DOI Scopus1 |
| 2007 |
Amblard, P. O., Zozor, S., McDonnell, M. D., & Stocks, N. G. (2007). Pooling networks for a discrimination task: Noise-enhanced detection. In S. M. Bezrukov (Ed.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 6602 (pp. 12 pages). Florence, ITALY: SPIE-INT SOC OPTICAL ENGINEERING.
DOI Scopus11 WoS2 |
| 2007 |
Martorell, F., McDonnell, M., Abbott, D., & Rubio, A. (2007). SNDR enhancement in noisy sinusoidal signals by non-linear processing elements. In M. Macucci, L. K. J. Vandamme, C. Ciofi, & M. B. Weissman (Eds.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 6600 (pp. 1-100). USA: International Society for Optical Engineering.
DOI |
| 2007 |
McDonnell, M. (2007). Signal estimation via averaging of coarsely quantised signals. In P. Mareels (Ed.), Proceedings of 2007 Information, Decision and Control, IDC (pp. 100-105). Australia: IEEE.
DOI Scopus3 |
| 2007 |
McDonnell, M., Stocks, N., & Abbott, D. (2007). Optimal coding of a random stimulus by a population of parallel neuron models. In S. M. Bezrukov (Ed.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 6602 (pp. 1-100). USA: International Society for Optical Engineering.
DOI |
| 2006 |
McDonnell, M., & Abbott, D. (2006). A biologically inspired model for signal compression. In D. V. Nicolau (Ed.), Proceedings of Smart Materials, Nano-, and Micro-Smart Systems 2006 Vol. 6416 (pp. 1-12). USA: SPIE.
DOI Scopus1 WoS1 |
| 2006 |
McDonnell, M., Amblard, P. O., Stocks, N., Zozor, S., & Abbott, D. (2006). High-resolution optimal quantization for stochastic pooling networks. In Axel Bender (Ed.), Proceedings of Smart Materials, Nano-, and Micro-Smart Systems 2006 Vol. 6417 (pp. CDROM1-CDROM15). USA: SPIE.
DOI Scopus3 WoS1 |
| 2006 |
McDonnell, M., Stocks, N., Pearce, C., & Abbott, D. (2006). How to use noise to reduce complexity in quantization. In A. Bender (Ed.), Proceedings of Microelectronics, MEMS, and Nanotechnology 2005 Vol. 6039 (pp. 60390E-1-60390E-12). http://www.spie.org/conferences/programs/05/au/: SPIE.
DOI Scopus1 |
| 2005 |
McDonnell, M., Stocks, N., Pearce, C., & Abbott, D. (2005). Optimal quantization and suprathreshold stochastic resonance. In N. G. Stocks, D. Abbott, & R. P. Morse (Eds.), Fluctuations and noise in biological, biophysical, and biomedical systems III : 24-26 May, 2005, Austin, Texas, USA Vol. 5841 (pp. 164-173). Austin, Texas, USA: SPIE.
DOI Scopus1 WoS1 |
| 2005 |
McDonnell, M., Stocks, N., Pearce, C., & Abbott, D. (2005). Analog to digital conversion using suprathreshold stochastic resonance. In S. Al Sarawi (Ed.), Proceedings of the SPIE International Symposium on Smart Structures, Devices, and Systems II Vol. 5649 (pp. 75-84). Bellingham, Washington, USA: SPIE.
DOI Scopus13 WoS7 |
| 2005 |
Martorell, F., McDonnell, M., Rubio, A., & Abbott, D. (2005). Using noise to break the noise barrier in circuits. In S. Al Sarawi (Ed.), Proceedings of the SPIE International Symposium on Smart Structures, Devices, and Systems II Vol. 5649 (pp. 53-66). Bellingham, Washington, USA: SPIE.
DOI Scopus7 WoS5 |
| 2004 |
McDonnell, M., & Abbott, D. (2004). Optimal quantization in neural coding. In F. Kschischang, & D. Tse (Eds.), Proceedings of the 2004 IEEE International Symposium on Information Theory (pp. 1-6). New Jersey, USA: IEEE.
DOI Scopus1 WoS1 |
| 2004 |
McDonnell, M., & Abbott, D. (2004). Signal reconstruction via noise through a system of parallel threshold nonlinearities. In D. O'Shaughnessy (Ed.), Proceedings of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing Vol. 2 (pp. CD-ROM II - 809-CD-ROM II - 812). CD-ROM: IEEE.
DOI Scopus9 WoS5 |
| 2004 |
McDonnell, M., Sethuraman, S., Kish, L., & Abbott, D. (2004). Cross-spectral measurement of neural signal transfer. In L. B. Kish (Ed.), Proceedings of SPIE on CD-ROM: Fluctuations and Noise 2004 Vol. 5471 (pp. CD-ROM 550-CD-ROM 559). CD-ROM: SPIE.
DOI Scopus1 WoS1 |
| 2004 |
McDonnell, M., Stocks, N., Pearce, C., & Abbott, D. (2004). Optimal quantization for energy-efficient information transfer in a population of neuron-like devices. In L. Kish (Ed.), Proceedings of SPIE on CD-ROM: Fluctuations and Noise 2004 Vol. 5471 (pp. CD-ROM 222-CD-ROM 232). CD-ROM: SPIE.
DOI Scopus1 WoS1 |
| 2004 |
McDonnell, M., Abbott, D., & Pearce, C. (2004). Neural mechanisms for analog to digital conversion. In L. Faraone, & V. K. Varadan (Eds.), Proceedings of SPIE on CD-ROM: Microelectronics MEMS, and Nanotechnology 2003 Vol. 5275 (pp. CD-ROM 278-CD-ROM 286). CD-ROM: SPIE.
DOI Scopus6 WoS6 |
| 2003 |
McDonnell, M., Stocks, N., Pearce, C., & Abbott, D. (2003). The data processing inequality and stochastic resonance. In H. M. Jaenisch, & J. W. Handley (Eds.), Proceedings of SPIE Vol. 5114 Vol. 5114 (pp. 249-260). Washington, USA: SPIE.
DOI Scopus2 WoS2 |
| 2003 |
McDonnell, M., & Abbott, D. (2003). Open questions for suprathreshold stochastic resonance in sensory neural models for motion detection using artificial insect vision. In S. M. Bezrukov (Ed.), Proceedings of UPon 2003: Third International Conference on Unsolved Problems of Noise and Fluctuations in Physics, Biology, and High Technology Vol. 665 (pp. 51-58). Melville, New York, USA: American Institute of Physics.
DOI Scopus2 WoS1 |
| 2002 |
McDonnell, M., Stocks, N., Pearce, C., & Abbott, D. (2002). Maximising information transfer through nonlinear noisy devices. In D. V. Nicolau, & A. P. Lee (Eds.), Proceedings of SPIE Vol. 4937 Vol. 4937 (pp. 254-263). CDROM: SPIE.
DOI Scopus2 WoS2 |
| 2001 |
McDonnell, M., Pearce, C., & Abbott, D. (2001). Neural information transfer in a noisy environment. In N. W. Bergmann (Ed.), Proceedings of SPIE - The International Society for Optical Engineering Vol. 4591 (pp. 59-69). PO BOX 10 BELLINGHAM WASHINGTON USA: THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS.
DOI Scopus2 WoS1 |
