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2020 – today
- 2024
[j23]Harvey Wang, Selena Singh, Thomas Trappenberg
, Abraham Nunes:
An Information-Geometric Formulation of Pattern Separation and Evaluation of Existing Indices. Entropy 26(9): 737 (2024)- [c42]Isaac Xu, Benjamin Misiuk, Scott C. Lowe, Martin Gillis, Thomas Trappenberg, Craig J. Brown:
Hierarchical Multi-Label Classification with Missing Information for Benthic Habitat Imagery. IJCNN 2024: 1-10 - [i10]Scott C. Lowe, Benjamin Misiuk, Isaac Xu, Shakhboz Abdulazizov, Amit R. Baroi, Alex C. Bastos, Merlin Best, Vicki Ferrini, Ariell Friedman, Deborah Hart, Ove Hoegh-Guldberg, Daniel Ierodiaconou, Julia Mackin-McLaughlin, Kathryn Markey, Pedro S. Menandro, Jacquomo Monk, Shreya Nemani, John O'Brien, Elizabeth S. Oh, Luba Y. Reshitnyk, Katleen Robert, Chris M. Roelfsema, Jessica A. Sameoto, Alexandre C. G. Schimel, Jordan A. Thomson, Brittany R. Wilson, Melisa C. Wong, Craig J. Brown, Thomas Trappenberg:
BenthicNet: A global compilation of seafloor images for deep learning applications. CoRR abs/2405.05241 (2024) - [i9]Kazi Hasan, Thomas Trappenberg, Israat Haque:
A Generalized Transformer-based Radio Link Failure Prediction Framework in 5G RANs. CoRR abs/2407.05197 (2024) - [i8]Isaac Xu, Scott C. Lowe, Thomas Trappenberg:
Label-free Monitoring of Self-Supervised Learning Progress. CoRR abs/2409.06612 (2024) - [i7]Isaac Xu, Benjamin Misiuk, Scott C. Lowe, Martin Gillis, Craig J. Brown, Thomas Trappenberg:
Hierarchical Multi-Label Classification with Missing Information for Benthic Habitat Imagery. CoRR abs/2409.06618 (2024) - 2022
- [c41]Isaac Xu, Scott C. Lowe, Thomas Trappenberg:
Label-free Monitoring of Self-Supervised Learning Progress. CCECE 2022: 78-84 - [c40]Scott C. Lowe, Robert Earle, Jason d'Eon, Thomas Trappenberg, Sageev Oore:
Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators. NeurIPS 2022 - 2021
- [i6]Scott C. Lowe, Robert Earle, Jason d'Eon, Thomas Trappenberg, Sageev Oore:
Logical Activation Functions: Logit-space equivalents of Boolean Operators. CoRR abs/2110.11940 (2021) - [i5]Scott C. Lowe, Thomas Trappenberg, Sageev Oore:
LogAvgExp Provides a Principled and Performant Global Pooling Operator. CoRR abs/2111.01742 (2021) - 2020
- [j22]Abraham Nunes, Martin Alda, Timothy Bardouille, Thomas Trappenberg:
Representational Rényi Heterogeneity. Entropy 22(4): 417 (2020) - [j21]Abraham Nunes, Martin Alda, Thomas Trappenberg:
Multiplicative Decomposition of Heterogeneity in Mixtures of Continuous Distributions. Entropy 22(8): 858 (2020) - [c39]André G. C. Pacheco, Chandramouli Shama Sastry, Thomas Trappenberg, Sageev Oore, Renato A. Krohling:
On Out-of-Distribution Detection Algorithms with Deep Neural Skin Cancer Classifiers. CVPR Workshops 2020: 3152-3161 - [c38]André G. C. Pacheco, Thomas Trappenberg, Renato A. Krohling:
Learning dynamic weights for an ensemble of deep models applied to medical imaging classification. IJCNN 2020: 1-8
2010 – 2019
- 2019
- [j20]Peter Q. Lee, Alessandro Guida, Steve Patterson, Thomas Trappenberg, Chris V. Bowen, Steven D. Beyea, Jennifer Merrimen, Cheng Wang, Sharon E. Clarke:
Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study. Comput. Medical Imaging Graph. 75: 14-23 (2019) - [j19]Farzaneh Sheikhnezhad Fard, Thomas Trappenberg:
A Novel Model for Arbitration Between Planning and Habitual Control Systems. Frontiers Neurorobotics 13: 52 (2019) - [j18]Pitoyo Hartono, Thomas Trappenberg:
Topographic representation adds robustness to supervised learning. J. Intell. Fuzzy Syst. 36(4): 3249-3262 (2019) - [c37]Abder-Rahman Ali, Jingpeng Li, Thomas Trappenberg:
Supervised Versus Unsupervised Deep Learning Based Methods for Skin Lesion Segmentation in Dermoscopy Images. Canadian AI 2019: 373-379 - [c36]Yoshimasa Kubo, Thomas Trappenberg:
Mitigating Overfitting Using Regularization to Defend Networks Against Adversarial Examples. Canadian AI 2019: 400-405 - [c35]Junliang Luo, Sageev Oore, Paul Hollensen, Alan Fine, Thomas Trappenberg:
Self-training for Cell Segmentation and Counting. Canadian AI 2019: 406-412 - [c34]Francesco Usai, Thomas Trappenberg:
Using a Deep CNN for Automatic Classification of Sleep Spindles: A Preliminary Study. Canadian AI 2019: 570-575 - [c33]Abder-Rahman Ali, Jingpeng Li, Sally Jane O'Shea, Guang Yang, Thomas Trappenberg, Xujiong Ye:
A Deep Learning Based Approach to Skin Lesion Border Extraction With a Novel Edge Detector in Dermoscopy Images. IJCNN 2019: 1-7 - [c32]Yoshimasa Kubo, Michael Traynor, Thomas Trappenberg, Sageev Oore:
Learning Adaptive Weight Masking for Adversarial Examples. IJCNN 2019: 1-8 - [i4]André G. C. Pacheco, Abder-Rahman Ali, Thomas Trappenberg:
Skin cancer detection based on deep learning and entropy to detect outlier samples. CoRR abs/1909.04525 (2019) - [i3]Abraham Nunes, Martin Alda, Timothy Bardouille, Thomas Trappenberg:
Representational Rényi heterogeneity. CoRR abs/1912.05031 (2019) - 2018
- [c31]Farzaneh Sheikhnezhad Fard, Thomas Trappenberg:
Mixing Habits and Planning for Multi-Step Target Reaching Using Arbitrated Predictive Actor-Critic. IJCNN 2018: 1-8 - [c30]Michael Traynor, Thomas Trappenberg:
Words Are Not Temporal Sequences of Characters. IJCNN 2018: 1-6 - 2017
- [c29]Farzaneh Sheikhnezhad Fard, Paul Hollensen, Stuart McIlroy, Thomas Trappenberg:
Impact of biased mislabeling on learning with deep networks. IJCNN 2017: 2652-2657 - [c28]Stuart McIlroy, Yoshimasa Kubo, Thomas Trappenberg, James Toguri, Christian Lehmann:
In vivo classification of inflammation in blood vessels with convolutional neural networks. IJCNN 2017: 3022-3027 - [i2]Farzaneh Sheikhnezhad Fard, Thomas Trappenberg:
A Novel Model for Arbitration between Planning and Habitual Control Systems. CoRR abs/1712.02441 (2017) - 2016
- [c27]Chun Kwang Tan, Paul G. Plöger, Thomas Trappenberg:
A Neural Field Approach to Obstacle Avoidance. KI 2016: 69-87 - 2015
- [j17]Hossein Parvar, Lauren D. Sculthorpe-Petley, Jason Satel, Rober Boshra, Ryan C. N. D'Arcy, Thomas Trappenberg:
Detection of event-related potentials in individual subjects using support vector machines. Brain Informatics 2(1): 1-12 (2015) - [j16]Patrick C. Connor, Paul Hollensen, Olav E. Krigolson, Thomas Trappenberg:
A biological mechanism for Bayesian feature selection: Weight decay and raising the LASSO. Neural Networks 67: 121-130 (2015) - [j15]Farzaneh Sheikhnezhad Fard, Paul Hollensen, Dietmar Heinke, Thomas Trappenberg:
Modeling human target reaching with an adaptive observer implemented with dynamic neural fields. Neural Networks 72: 13-30 (2015) - [j14]Pitoyo Hartono, Paul Hollensen, Thomas Trappenberg:
Learning-Regulated Context Relevant Topographical Map. IEEE Trans. Neural Networks Learn. Syst. 26(10): 2323-2335 (2015) - [c26]Pitoyo Hartono, Paul Hollensen, Thomas Trappenberg:
Classifier with Hierarchical Topographical Maps as Internal Representation. ICLR (Workshop) 2015 - 2014
- [j13]Jason Satel, Farzaneh Sheikhnezhad Fard, Zhiguo Wang, Thomas Trappenberg:
Simulating oculomotor inhibition of return with a two-dimensional dynamic neural field model of the superior colliculus. Aust. J. Intell. Inf. Process. Syst. 14(1) (2014) - [c25]Pitoyo Hartono, Paul Hollensen, Thomas Trappenberg:
Visualizing Hierarchical Representation in a Multilayered Restricted RBF Network. ICANN 2014: 339-346 - [c24]Patrick C. Connor, Thomas Trappenberg:
An Architecture for Distinguishing between Predictors and Inhibitors in Reinforcement Learning. ICLR (Workshop Poster) 2014 - [p1]Thomas Trappenberg:
A Brief Introduction to Probabilistic Machine Learning and Its Relation to Neuroscience. Growing Adaptive Machines 2014: 61-108 - 2013
- [j12]Warren A. Connors, Thomas Trappenberg:
Improved Path Integration Using a Modified Weight Combination Method. Cogn. Comput. 5(3): 295-306 (2013) - [c23]Patrick C. Connor, Thomas Trappenberg:
Biologically plausible feature selection through relative correlation. IJCNN 2013: 1-8 - [c22]Pitoyo Hartono, Thomas Trappenberg:
Classificability-regulated self-organizing map using restricted RBF. IJCNN 2013: 1-5 - [c21]Thomas Trappenberg:
Bubbles in the robot. IJCNN 2013: 1-2 - 2012
- [j11]Robert A. Marino, Thomas Trappenberg, Michael Dorris, Douglas P. Munoz:
Spatial Interactions in the Superior Colliculus Predict Saccade Behavior in a Neural Field Model. J. Cogn. Neurosci. 24(2): 315-336 (2012) - [c20]Pitoyo Hartono, Thomas Trappenberg:
Internal representation of sensory information for training autonomous robot. SCIS&ISIS 2012: 341-345 - 2011
- [c19]Patrick C. Connor, Thomas Trappenberg:
Characterizing a Brain-Based Value-Function Approximator. Canadian AI 2011: 92-103 - [c18]Paul Hollensen, Warren A. Connors, Thomas Trappenberg:
Comparison of Learned versus Engineered Features for Classification of Mine Like Objects from Raw Sonar Images. Canadian AI 2011: 174-185 - [c17]Pitoyo Hartono, Thomas Trappenberg:
Internal topographical structure in training autonomous robot. SMC 2011: 239-243 - [i1]Misha Denil, Thomas Trappenberg:
A Characterization of the Combined Effects of Overlap and Imbalance on the SVM Classifier. CoRR abs/1109.3532 (2011) - 2010
- [c16]Misha Denil, Thomas Trappenberg:
Overlap versus Imbalance. Canadian AI 2010: 220-231 - [c15]Warren A. Connors, Patrick C. Connor, Thomas Trappenberg:
Detection of Mine-Like Objects Using Restricted Boltzmann Machines. Canadian AI 2010: 362-365 - [c14]Thomas Trappenberg, Aya Saito, Pitoyo Hartono:
Selective attention improves self-organization of cortical maps with multiple inputs. IJCNN 2010: 1-4
2000 – 2009
- 2009
- [b1]Thomas Trappenberg:
Fundamentals of Computational Neuroscience (2. ed.). Oxford University Press 2009, ISBN 978-0-19-956841-3, pp. I-XXV, 1-390 - [c13]Pitoyo Hartono, Thomas Trappenberg:
Learning Intialized by Topologically Correct Representation. SMC 2009: 2723-2727 - [c12]Thomas Trappenberg, Pitoyo Hartono, Douglas Rasmusson:
Top-Down Control of Learning in Biological Self-Organizing Maps. WSOM 2009: 316-324 - 2008
- [j10]Joshua P. Salmon, Thomas Trappenberg:
Modeling the integration of expectations in visual search with centre-surround neural fields. Neural Networks 21(10): 1476-1492 (2008) - 2007
- [j9]Dominic I. Standage, Sajiya Jalil, Thomas Trappenberg:
Computational consequences of experimentally derived spike-time and weight dependent plasticity rules. Biol. Cybern. 96(6): 615-623 (2007) - [c11]Dominic I. Standage, Thomas Trappenberg:
The Trouble with Weight-Dependent STDP. IJCNN 2007: 1348-1353 - 2006
- [j8]Michael Lawrence, Thomas Trappenberg, Alan Fine:
Rapid learning and robust recall of long sequences in modular associator networks. Neurocomputing 69(7-9): 634-641 (2006) - [j7]Thomas Trappenberg, Jie Ouyang, Andrew D. Back:
Input Variable Selection: Mutual Information and Linear Mixing Measures. IEEE Trans. Knowl. Data Eng. 18(1): 37-46 (2006) - [c10]Matthew Boardman, Thomas Trappenberg:
A Heuristic for Free Parameter Optimization with Support Vector Machines. IJCNN 2006: 610-617 - 2005
- [j6]Thomas Trappenberg, Dominic I. Standage:
Multi-packet regions in stabilized continuous attractor networks. Neurocomputing 65-66: 617-622 (2005) - [j5]Dominic I. Standage, Thomas Trappenberg, Raymond M. Klein:
Modelling divided visual attention with a winner-take-all network. Neural Networks 18(5-6): 620-627 (2005) - [c9]Thomas Trappenberg:
Coverage-performance estimation for classification with ambiguous data. ESANN 2005: 411-416 - [c8]Michael Lawrence, Thomas Trappenberg, Alan Fine:
A multi-modular associator network for simple temporal sequence learning and generation. ESANN 2005: 423-428 - 2004
- [j4]Simon M. Stringer, Edmund T. Rolls, Thomas Trappenberg:
Self-organising continuous attractor networks with multiple activity packets, and the representation of space. Neural Networks 17(1): 5-27 (2004) - 2003
- [j3]Simon M. Stringer, Edmund T. Rolls, Thomas Trappenberg, Ivan E. Tavares de Araújo:
Self-organizing continuous attractor networks and motor function. Neural Networks 16(2): 161-182 (2003) - 2001
- [j2]Andrew D. Back, Thomas Trappenberg:
Selecting inputs for modeling using normalized higher order statistics and independent component analysis. IEEE Trans. Neural Networks 12(3): 612-617 (2001) - [c7]Thomas Trappenberg, Edmund T. Rolls, Simon M. Stringer:
Effective Size of Receptive Fields of Inferior Temporal Visual Cortex Neurons in Natural Scenes. NIPS 2001: 293-300 - 2000
- [c6]Thomas Trappenberg, Andrew D. Back:
A Classification Scheme for Applications with Ambiguous Data. IJCNN (6) 2000: 296-301
1990 – 1999
- 1999
- [c5]Andrew D. Back, Thomas Trappenberg:
Input variable selection using independent component analysis. IJCNN 1999: 989-992 - [c4]Lars Kindermann, Thomas Trappenberg:
Modeling time-varying processes by unfolding the time domain. IJCNN 1999: 2600-2603 - 1998
- [c3]Thomas Trappenberg:
Dynamic Cooperation and Competition in a Network of Spiking Neurons. ICONIP 1998: 1299-1302 - 1997
- [c2]Thomas Trappenberg, S. Simpson, Raymond M. Klein, P. McMullen, Douglas P. Munoz, Michael Dorris:
Neural field model of oculomotor preparation and disengagement. ICNN 1997: 591-596 - [c1]Thomas Trappenberg:
Non-monotone network dynamics; preliminary results. ICNN 1997: 821-824 - 1994
- [j1]K. J. M. Moriarty, Sergiu Sanielevici, Thomas Trappenberg:
High Speed Monte Carlo Simulations on Vector Parallel Computers. Parallel Algorithms Appl. 3(3-4): 165-175 (1994)
Coauthor Index
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