B. Horne

发表

Learning long-term dependencies is not as difficult with NARX recurrent neural networks

Peter Tiňo, Tsung-Nan Lin, C. L. Giles, 1995 .

An analysis of noise in recurrent neural networks: convergence and generalization

C. Lee Giles, Bill G. Horne, Kam-Chuen Jim, 1996, IEEE Trans. Neural Networks.

Learning long-term dependencies in NARX recurrent neural networks

Peter Tiño, C. Lee Giles, Tsungnan Lin, 1996, IEEE Trans. Neural Networks.

Progress in supervised neural networks

D.R. Hush, B.G. Horne, D. Hush, 1993, IEEE Signal Processing Magazine.

Neural Information Processing Systems 7

C. Lee Giles, Bill G. Horne, Kam-Chuen Jim, 1995 .

Lower bounds for the spectral radius of a matrix

B. Horne, 1997 .

How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies

C. Lee Giles, Tsungnan Lin, Bill G. Horne, 1998, Neural Networks.

Finite State Machines and Recurrent Neural Networks -- Automata and Dynamical Systems Approaches

Peter Tiňo, C. Lee Giles, Bill G. Horne, 1995 .

Error surfaces for multilayer perceptrons

Don R. Hush, Bill G. Horne, John M. Salas, 1992, IEEE Trans. Syst. Man Cybern..

Financial Time Series Forecasting Using K-nearest Neighbors Classiication

C. L. Giles, B. Horne, Marco Maggini, 1997 .

Predicting Multiprocessor Memory Access Patterns with Learning Models

Donald M. Chiarulli, Steven P. Levitan, C. Lee Giles, 1997, ICML.

Alternative discrete-time operators and their application to nonlinear models

C. Lee Giles, A. Tsoi, A. Back, 1997 .

Learning long { term dependencies is not asdi cult with NARX recurrent neural

C. L. Giles, B. Horne, networksTsungnan Lin, 1996 .

1 Predictive Control of OptoElectronic Reconfigurable Interconnection Networks Using Neural Networks *

C. Lee Giles, B. Horne, Marco Maggini, 1995 .

Attractive Periodic Sets in Discrete-Time Recurrent Networks (with Emphasis on Fixed-Point Stability and Bifurcations in Two-Neuron Networks)

Peter Tiño, C. Lee Giles, Bill G. Horne, 2001, Neural Computation.

Bounds on the complexity of recurrent neural network implementations of finite state machines

Don R. Hush, Bill G. Horne, D. Hush, 1993, Neural Networks.

On the node complexity of neural networks

Don R. Hush, Bill G. Horne, D. Hush, 1994, Neural Networks.

Efficient algorithms for function approximation with piecewise linear sigmoidal networks

Don R. Hush, Bill G. Horne, D. Hush, 1998, IEEE Trans. Neural Networks.

The recursive neural network

Don R. Hush, Chaouki T. Abdallah, Bill G. Horne, 2012 .

The recursive neural network and its applications in control theory

Don R. Hush, Chaouki T. Abdallah, Bill G. Horne, 1993 .

Learning a class of large finite state machines with a recurrent neural network

C. Lee Giles, Tsungnan Lin, Bill G. Horne, 1995, Neural Networks.

Learning with Product Units

C. Lee Giles, Marwan A. Jabri, Bill G. Horne, 1994, NIPS.

An experimental comparison of recurrent neural networks

C. Lee Giles, Bill G. Horne, B. Horne, 1994, NIPS.

On The Computational Complexity Of The Manufacturing Job Shop And Reentrant Flow Line

Frank L. Lewis, Chaouki T. Abdallah, Bill G. Horne, 2012 .

Computational complexity of determining resource loops in re-entrant flow lines

Frank L. Lewis, Chaouki T. Abdallah, Bill G. Horne, 2000, IEEE Trans. Syst. Man Cybern. Part A.

Computational capabilities of recurrent NARX neural networks

Hava T. Siegelmann, C. Lee Giles, Bill G. Horne, 1997, IEEE Trans. Syst. Man Cybern. Part B.

Neural networks in robotics: A survey

Mo M. Jamshidi, Bill G. Horne, Nader Vadiee, 1990, J. Intell. Robotic Syst..

A delay damage model selection algorithm for NARX neural networks

Sun-Yuan Kung, C. Lee Giles, Tsungnan Lin, 1997, IEEE Trans. Signal Process..

Fixed points in two-neuron discrete time recurrent networks: stability and bifurcation considerations

Peter Tiňo, C. Lee Giles, Bill G. Horne, 1995 .

Representation and Induction of Finite State Machines using Time-Delay Neural Networks

Garrison W. Cottrell, C. Lee Giles, Bill G. Horne, 1996, NIPS.

Silent Shredder: Zero-Cost Shredding for Secure Non-Volatile Main Memory Controllers

Yan Solihin, Stuart Haber, Amro Awad, 2016, ASPLOS.

Product Unit Learning

C. L. Giles, Marwan A. Jabri, Bill G. Horne, 1998 .

Error surfaces for multi-layer perceptrons

D. R. Hush, J. M. Slas, B. Horne, 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.