FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling. - Andrew D. Back; Ah Chung Tsoi (Neural Comput., 1991)

An Adaptive Lattice Architecture for Dynamic Multilayer Perceptrons. - Andrew D. Back; Ah Chung Tsoi (Neural Comput., 1992)

New techniques for nonlinear system identification : a rapprochement between neural networks and linear systems / by Andrew D. Back - Andrew D. Back (1992)

A Simplified Gradient Algorithm for IIR Synapse Multilayer Perceptrons. - Andrew D. Back; Ah Chung Tsoi (Neural Comput., 1993)

A Comparison of Discrete-Time Operator Models and for Nonlinear System Identification. - Andrew D. Back; Ah Chung Tsoi (NIPS, 1994)

Locally recurrent globally feedforward networks: a critical review of architectures. - Ah Chung Tsoi; Andrew D. Back (IEEE Trans. Neural Networks, 1994)

The Gamma MLP for Speech Phoneme Recognition. - Steve Lawrence; Ah Chung Tsoi; Andrew D. Back (NIPS, 1995)

Neural Network Classification and Prior Class Probabilities. - Steve Lawrence; Ian Burns; Andrew D. Back; Ah Chung Tsoi; C. Lee Giles (Neural Networks: Tricks of the Trade, 1996)

Approximation of Hybrid Systems by Neural Networks. - Andrew D. Back; Tianping Chen (ICONIP (1), 1997)

Blind Source Separation and Deconvolution of Fast Sampled Signals. - Andrew D. Back; Andrzej Cichocki (ICONIP (1), 1997)

A First Application of Independent Component Analysis to Extracting Structure from Stock Returns. - Andrew D. Back; Andreas S. Weigend (Int. J. Neural Syst., 1997)

Discrete time recurrent neural network architectures: A unifying review. - Ah Chung Tsoi; Andrew D. Back (Neurocomputing, 1997)

On the distribution of performance from multiple neural-network trials. - Steve Lawrence; Andrew D. Back; Ah Chung Tsoi; C. Lee Giles (IEEE Trans. Neural Networks, 1997)

Face recognition: a convolutional neural-network approach. - Steve Lawrence; C. Lee Giles; Ah Chung Tsoi; Andrew D. Back (IEEE Trans. Neural Networks, 1997)

What drives stock returns?-an independent component analysis. - Andrew D. Back; Andreas S. Weigend (CIFEr, 1998)

A Low Sensitivity Recurrent Neural Network. - Andrew D. Back; Ah Chung Tsoi (Neural Comput., 1998)

Input variable selection using independent component analysis. - Andrew D. Back; Thomas Trappenberg (IJCNN, 1999)

Alternative discrete-time operators: an algorithm for optimal selection of parameters. - Andrew D. Back; Bill G. Horne; Ah Chung Tsoi; C. Lee Giles (IEEE Trans. Signal Process., 1999)

A Classification Scheme for Applications with Ambiguous Data. - Thomas Trappenberg; Andrew D. Back (IJCNN (6), 2000)

A spiking neural network architecture for nonlinear function approximation. - Nicolangelo Iannella; Andrew D. Back (Neural Networks, 2001)

Selecting inputs for modeling using normalized higher order statistics and independent component analysis. - Andrew D. Back; Thomas Trappenberg (IEEE Trans. Neural Networks, 2001)

Universal Approximation of Multiple Nonlinear Operators by Neural Networks. - Andrew D. Back; Tianping Chen (Neural Comput., 2002)

Input Variable Selection: Mutual Information and Linear Mixing Measures. - Thomas Trappenberg; Jie Ouyang; Andrew D. Back (IEEE Trans. Knowl. Data Eng., 2006)

Neural Network Classification and Prior Class Probabilities. - Steve Lawrence; Ian Burns; Andrew D. Back; Ah Chung Tsoi; C. Lee Giles (Neural Networks: Tricks of the Trade (2nd ed.), 2012)

PauseCode: Computational Conversation Timing Analysis. - Daniel Angus; Yeyang Yu; Paul Vrbik; Andrew D. Back; Janet Wiles (MA3HMI@ICMI, 2018)

Determining the Number of Samples Required to Estimate Entropy in Natural Sequences. - Andrew D. Back; Daniel Angus; Janet Wiles (IEEE Trans. Inf. Theory, 2019)

A Plan for Developing an Auslan Communication Technologies Pipeline. - Jessica Korte; Axel Bender; Guy Edward Gallasch; Janet Wiles; Andrew D. Back (ECCV Workshops (2), 2020)

Transitive Entropy - A Rank Ordered Approach for Natural Sequences. - Andrew D. Back; Daniel Angus; Janet Wiles (IEEE J. Sel. Top. Signal Process., 2020)

Entropy Estimation Using a Linguistic Zipf-Mandelbrot-Li Model for Natural Sequences. - Andrew D. Back; Janet Wiles (Entropy, 2021)

An Information Theoretic Approach to Symbolic Learning in Synthetic Languages. - Andrew D. Back; Janet Wiles (Entropy, 2022)

Estimating Sentence-like Structure in Synthetic Languages Using Information Topology. - Andrew D. Back; Janet Wiles (Entropy, 2022)

Estimation of Statistical Manifold Properties of Natural Sequences using Information Topology. - Andrew D. Back; Janet Wiles (SSP, 2023)