Fast Neural Network Surrogates for Very High Dimensional
River-Estuary-Ocean Circulation Models. Rudolph van der Merwe,
Todd K. Leen, Zhengdong Lu, Sergey Frolov, and Antonio Baptista. Neural Networks, 20, 462-478, 2007.
Penalized Probabilistic Clustering. Zhengdong Lu and Todd K.
Leen. Neural Computation, 19, 1528-1567, 2007.
Semi-supervised clustering with pairwise constraints: A
discriminative approach. Zhengdong
Lu and Todd K. Leen. Eleventh
International Conference on Artificial Intelligence and Statistics,
Puerto Rico, 2007.
Detection of Early Cognitive Loss from Medication Adherence
Behavior. Todd Leen, Zhengdong Lu, Tamara Hayes, and Jeffrey Kaye. The 2nd International Conference on
Technology and Aging, Toronto, 2007.
Semi-supervised
Learning with Penalized Probabilistic Clustering. Zhengdong
Lu and Todd K. Leen. Advances
in Neural Information Processing
Systems 17, 2005.
Random walks for spike-timing-dependent plasticity. Alan Williams, Todd Leen, and Pat Roberts, Physical Review E, 70, 021916, 2004.
Parameterized Novelty Detection for Environmental Sensor Monitoring. Cynthia Archer, Todd Leen, Antonio Baptista. Advances in Neural Information Processing Systems 16 (2004).
Stability of Negative-Image Equilibria in Spike-Timing-Dependent Plasticity. Alan Williams, Pat Roberts, and Todd K. Leen. Physical Review E, 68, 2003.
A Generalized Lloyd-type Algorithm for Adaptive Transform Coder Design. Cythia Archer and Todd Leen. IEEE Transactions on Signal Processing, 52, 1, 255-264, 2004.
Fault Detection for Salinity Sensors in the Columbia River Estuary Cynthia Archer, Antonio Baptista, and Todd Leen. Water Resources Research, 39, 3, 19 March, 2003.
Bayesian Sensor Image Fusion Using Local Linear Generative Models, Ravi K. Sharma, Todd K. Leen, and Misha Pavel. Optical Engineering, 40, 1364-1376, July, 2001.
The Coding Optimal Transform, C. Archer and T. Leen. Data Compression Conference 2001, IEEE Computer Society Press, 2001.
From Mixtures of Mixtures to Adaptive Transform Coding, C. Archer and T. Leen. In T. Leen, T. Dietterich, V. Tresp (eds.), Advances in Neural Information Processing Systems 13, The MIT Press, 2001.
Adaptive Transform Coding as Constrained Vector Quantization, Cynthia Archer and Todd Leen, in Neural Networks for Signal Processing -- Proceedings of the IEEE Workshop, Sidney, 2000.
Probabilistic Sensor Fusion, Ravi K. Sharma, T.K. Leen and Misha Pavel, Advances in Neural Information Processing Systems 11, The MIT Press, 1999.
Optimal Asymptotic Learning Rate - Macroscopic vs. Microscopic Dynamics, T. K. Leen, Bernhard Schottky, and David Saad, Physical Review E, 59, 985-991, 1999.
A Fast Histogram-Based Postprocessor that Improves Posterior Probability Estimates, Wei Wei, Todd K. Leen, and Etienne Barnard, Neural Computation, 11, no. 5, 1999.
Exact and Perturbation Solutions for the Ensemble Dynamics, T.K. Leen, in D. Saad (ed.), Online Learning in Neural Networks, The Newton Institute Series, Cambridge University Press, Cambridge, 1999.
Optimal Dimension Reduction and Transform Coding with Mixture Principal Components, Cynthia Archer and Todd K. Leen, International Joint Conference on Neural Networks (IJCNN), IEEE, 1999.
Multi-stream Video Fusion Using Local Principal Components Analysis, Ravi Sharma, Misha Pavel, and Todd K. Leen, in Infrared Technology and Applications XXIV, Proceedings of SPIE, vol. 3436, SPIE, 1998.
Automatic Prediction of Trauma Registry Procedure Codes from Emergency Room dictations, William R. Hersh, T.K. Leen, P. Steve Rehfuss, and Susan Malveau, MEDINFO 98, Seoul, South Korea, 1998.
Two Approaches to Optimal Annealing, T.K. Leen, B. Schottky, and D. Saad, in M. Jordan, D. Kearns, and S. Solla (eds.), Advances in Neural Information Processing Systems 10, The MIT Press, 1998.
Dimension Reduction by Local Principal Component Analysis, N. Kambhatla and Todd K. Leen, Neural Computation, 9, 1493, 1997. (This paper was one of 21 articles selected for the collection Unsupervised Learning, Foundations of Neural Computation, Geoffrey Hinton and Terrence J. Sejnowski (eds), The MIT Press, 1999.)
Stochastic Manhattan Learning: An Exact Time-Evolution Operator for the Ensemble Dynamics, T.K. Leen and John E. Moody, Physical Review E, 56, 1262, 1997.
Using Curvature Information for Fast Stochastic Search, G.B. Orr and T.K. Leen, in M. Mozer, M. Jordan, and T. Petsche (eds.), Advances in Neural Information Processing Systems 9, The MIT Press, 1997.
Invariance and Regularization in Learning, T.K. Leen, in G. Tesauro, D. Touretzky, and T. Leen (eds.), Advances in Neural Information Processing Systems 7, The MIT Press, 1995,
Classification with Gaussian Mixtures and Clusters, N. Kambhatla and T.K. Leen, in G. Tesauro, D. Touretzky, and T. Leen (eds.), Advances in Neural Information Processing Systems 7, The MIT Press, 1995.
From Data Distributions to Regularization in Invariant Learning, Todd K. Leen, Neural Computation, 7, 974, 1995.
Invariant Learning, Todd K. Leen, in Tesauro, Touretzky, and Leen (eds.), Advances in Neural Information Processing Systems 7, MIT Press, 1995
Fast Pruning Using Principal Components, A.U. Levin, T.K. Leen, and J.E. Moody, In J.D. Cowan, G.Tesauro, and J. Alspoector (eds.), Advances in Neural Information Processing Systems 6, Morgan Kauffman Publishers, 1994.
Fast Non-Linear Dimension Reduction, T.K. Leen and Nandakishore Kambhatla, In J.D. Cowan, G.Tesauro, and J. Alspoector (eds.), Advances in Neural Information Processing Systems 6, Morgan Kauffman Publishers, 1994.
Optimal Stochastic Search with Adaptive Momentum, T.K. Leen and Genevieve B. Orr, In J.D. Cowan, G.Tesauro, and J. Alspoector (eds.), Advances in Neural Information Processing Systems 6, Morgan Kauffman Publishers, 1994.
Optimal Stochastic Search, G.B. Orr and T.K. Leen, in Proceedings of the 1993 Connectionist Models Summer School, M.C. Mozer, P. Smolensky, D.S. Touretzky, J.L. Elman, and A.S. Weigend (eds.), Erlbaum Associates, 1993.
Fast Nonlinear Dimension Reduction, N. Kambhatla and T.K. Leen, in IEEE International Conference on Neural Networks, vol. 3, 1213-1218, IEEE, San Francisco, 1993.
A Coordinate-Independent Center Manifold Reduction, Todd K. Leen, Physics Letters, A-174, 89, 1993.
Weight-Space Probability Densities in Stochastic Learning: I. Dynamics and Equilibria, Todd K. Leen and John E. Moody, In Giles, Hanson and Cowan (eds.), Advances in Neural Information Processing Systems 5, Morgan Kaufmann Publishers, 1993.
Weight-Space Probability Densities in Stochastic Learning: II. Transients and Basin-Hopping Times, G.B. Orr and T.K. Leen, In Giles, Hanson and Cowan (eds.), Advances in Neural Information Processing Systems 5, Morgan Kaufmann Publishers, 1993.
Weight-Space Probability Densities and Convergence Times for Stochastic Learning, T.K. Leen and G.B. Orr, in International Joint Conference on Neural Networks, Baltimore, 1992.
Feature Selection for Improved Classification, F. Shaudys and T.K. Leen, in International Joint Conference on Neural Networks, Baltimore, 1992.
Weight-Space Densities in Stochastic Learning, T.K. Leen and G.B. Orr, in Proc. Canadian Conf. on Electrical and Computer Engineering, Toronto, Ontario, Sept. 1992.
Learning in Linear Feature Networks, T.K. Leen, invited paper for Adaptive Signal Processing, SPIE Proceedings, 1565, 472-481, 1991.
Dynamics of Learning in Linear Feature-Discovery Networks, Todd K. Leen, Network: Computation in Neural Systems, 2, 85, 1991.
Dyanamics of Learning in Recurrent Feature-Discovery Networks , Todd K. Leen, Advances in Neural Information Processing Systems 3, Lippmann, Moody and Touretzky (eds.), Morgan Kaufmann, 1991 (short version of above article in Network).
Encoding and Classification in a Model of Olfactory Cortex, T.K. Leen, Max Webb, and S. Rehfuss, International Joint Conference on Neural Networks, Seattle, 1991.
Hebbian Learning: Algorithms and Applications, T.K. Leen, in Proceedings of the 34th Annual Conference of the International Society for the Systems Sciences, Portland, OR, July, 1990.
Weight Dynamics of Recurrent Hebbian Networks, T.K. Leen, in Proceedings of the 34th Annual Conference of the International Society for the Systems Sciences, Portland, OR, July, 1990.
Hebbian Learning Improves Classifier Efficiency, T.K. Leen, M. Rudnick, and D. Hammerstrom, in International Joint Conference on Neural Networks, San Diego, 1990.
Dynamics and Implementation of Self-Organizing Networks, D. Hammerstrom, T.K. Leen, and E. Means, in Advanced Neural Computers, R. Eckmiller (ed.), Elsevier Science Publishers B.V. (North-Holland), March, 1990.
Spekaer-Independent Vowel Recognition: Comparison of Backpropagation and Classification Trees, R.A. Cole, Y.K. Muthusamy, L. Atlas, T. Leen, and M. Rudnick, in Proceedings of the IEEE Hawaii International Conference on System Sciences, 1990.
Speech Recognition, VLSI, and Neural Networks, R. Cole, D. Hammerstrom, T. Leen, M. Gopalakrishnan, J. Inouye, E. Means, Y. Muthusamy, T. Rooker, and M. Rudnick, in Proceedings of NORTHOCON, 1989.
Form and exploration of mechanical stability limits in erect stance, G. McCollum and T.K. Leen, Journal of Motor Behavior, 21, 255, 1989.
Theory and practice of proximity correction by secondary exposure, Todd K. Leen, J. Appl. Physics, 65, 1776, 1989
Renormalization and Scaling Behavior of non-Abelian Gauge Fields in Curved Spacetime, Todd K. Leen, Annals of Physics, 147, 417, 1983.
Remote Quantum-mechanical Detection of Gravitational Radiation, Todd K. Leen, Leonard Parker, and L.O. Pimentel, General Relativity and Gravitation, 15, 761, 1983.