C. L. Giles

发表

Predictive control of opto-electronic reconfigurable interconnection networks using neural networks

Donald M. Chiarulli, Majd Sakr, S. P. Levitan, 1995, Proceedings of Second International Workshop on Massively Parallel Processing Using Optical Interconnections.

Correctness, efficiency, extendability and maintainability in neural network simulation

Ah Chung Tsoi, C. L. Giles, Steve Lawrence, 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

Searching for Tables in Digital Documents

Ying Liu, C. L. Giles, Kun Bai, 2007 .

Can recurrent neural networks learn natural language grammars?

Sandiway Fong, C. L. Giles, Steve Lawrence, 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

Neural network routing for multiple stage interconnection networks

C. L. Giles, M. W. Goudreau, 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

Constructing deterministic finite-state automata in sparse recurrent neural networks

C. L. Giles, Christian W. Omlin, Colin Giles, 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

Inserting rules into recurrent neural networks

C. L. Giles, Christian W. Omlin, Colin Giles, 1992, Neural Networks for Signal Processing II Proceedings of the 1992 IEEE Workshop.

Online prediction of multiprocessor memory access patterns

Donald M. Chiarulli, Steven P. Levitan, C. L. Giles, 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

Time series prediction and attractor learning by the error propagating neural network

C. L. Giles, C. M. van den Bleek, R. Bakker, 1998 .

Low sensitivity time delay neural networks with cascade form structure

Ah Chung Tsoi, Andrew D. Back, C. L. Giles, 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

The gamma MLP-using multiple temporal resolutions for improved classification

Ah Chung Tsoi, Andrew D. Back, C. L. Giles, 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

Local minima and generalization

Ah Chung Tsoi, C. L. Giles, Steve Lawrence, 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

Equivalence in knowledge representation: automata, recurrent neural networks, and dynamical fuzzy systems

Karvel K. Thornber, C. L. Giles, Christian W. Omlin, 1999, Proc. IEEE.

Pruning recurrent neural networks for improved generalization performance

C. L. Giles, C. Lee Giles, Christian W. Omlin, 1994, Proceedings of IEEE Workshop on Neural Networks for Signal Processing.

Heuristics for the extraction of rules from discrete-time recurrent neural networks

C. L. Giles, Christian W. Omlin, C. B. Miller, 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

Learning, extracting, inserting and verifying grammatical information in recurrent neural networks

C. L. Giles, 1993 .

Representation of fuzzy finite state automata in continuous recurrent, neural networks

Karvel K. Thornber, C. L. Giles, Christian W. Omlin, 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

Searching the Web: general and scientific information access

C. L. Giles, C. Lee Giles, Steve Lawrence, 1999, First IEEE/POPOV Workshop on Internet Technologies and Services. Proceedings (Cat. No.99EX391).

Proceedings of the 5th International Conference on Neural Information Processing Systems

C. L. Giles, Jack D. Cowan, Stephen José Hanson, 1992 .

Remembering the past: the role of embedded memory in recurrent neural network architectures

Tsung-Nan Lin, C. L. Giles, Bill G. Horne, 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

Training recurrent neural networks with temporal input encodings

Tsung-Nan Lin, C. L. Giles, Bill G. Horne, 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

Rule refinement with recurrent neural networks

C. L. Giles, Christian W. Omlin, 1993, IEEE International Conference on Neural Networks.

SEARCHING THE WEB, CONTINUED. AUTHORS' REPLY

C. L. Giles, M. Molloy, S. Lawrence, 1998 .

Constructive learning of recurrent neural networks

C. L. Giles, Y. C. Lee, Y. C. Lee, 1993, IEEE International Conference on Neural Networks.

On recurrent neural networks and representing finite-state recognizers

C. L. Giles, M. W. Goudreau, 1993 .

Second-order recurrent neural networks for grammatical inference

C. L. Giles, Y. C. Lee, G. Z. Sun, 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

The past is important: a method for determining memory structure in NARX neural networks

Sun-Yuan Kung, Tsung-Nan Lin, C. L. Giles, 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

Connectionist symbol processing : dead or alive ?

Lokendra Shastri, Dan Roth, C. L. Giles, 1999 .

Adaptive stochastic cellular automata: theory

C. L. Giles, H. H. Chen, Y. C. Lee, 1991 .

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

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

Sequence Learning - Paradigms, Algorithms, and Applications

C. L. Giles, Ron Sun, C. Lee Giles, 2001 .

Dynamic recurrent neural networks: Theory and applications

C. L. Giles, C. Lee Giles, Gary M. Kuhn, 1994, IEEE Trans. Neural Networks Learn. Syst..

Machine learning using higher order correlation networks

C. L. Giles, Y. C. Lee, Gary D. Doolen, 1986 .

High order correlation model for associative memory

C. L. Giles, Y. C. Lee, G. Z. Sun, 1987 .

Financial Time Series Forecasting Using K-nearest Neighbors Classiication

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

Semi-supervised Clustering for High-dimensional and Sparse Features a Dissertation in Information Sciences and Technology

C. L. Giles, T. Mullen, J. Barlow, 2022 .

Dynamic Adaptation of Recurrent Neural Network Architectures Guided by Symbolic Knowledge

C. L. Giles, W. Omlin, 1999 .

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

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

A Comparative Study on Representation of Web Pages in Automatic Text Categorization

C. L. Giles, S. Ertekin, 2006 .

Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting

C. L. Giles, Daniel Kifer, Alexander Ororbia, 2019, 1905.10696.

Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting

C. L. Giles, Daniel Kifer, Alexander Ororbia, 2019, NeurIPS.

Reports of the 2016 AAAI Workshop Program

Bruno Bouchard, Parisa Kordjamshidi, Toby Walsh, 2016, AI Mag..

Provably Stable Interpretable Encodings of Context Free Grammars in RNNs with a Differentiable Stack

C. L. Giles, J. Stogin, A. Mali, 2020, ArXiv.

A provably stable neural network Turing Machine

C. L. Giles, J. Stogin, A. Mali, 2020, 2006.03651.

Modelling and Personalization of Users

Amanda Spink, C. L. Giles, C. Lee Giles, 2006 .

Proceedings of the Second international conference on Advances in social network mining and analysis

John Yen, C. L. Giles, C. Lee Giles, 2008 .

Advances in Social Network Mining and Analysis

John Yen, C. L. Giles, Haizheng Zhang, 2011 .

Investigating Backpropagation Alternatives when Learning to Dynamically Count with Recurrent Neural Networks

C. L. Giles, Daniel Kifer, Alexander Ororbia, 2021, ICGI.

Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack pdf

Daniel Kifer, C. L. Giles, C. Lee Giles, 2020, ICGI.

Bi-LSTM-CRF Sequence Labeling for Keyphrase Extraction from Scholarly Documents

Cornelia Caragea, C. L. Giles, Rabah A. Al-Zaidy, 2019, WWW.

An Entropy Metric for Regular Grammar Classification and Learning with Recurrent Neural Networks

C. L. Giles, Qinglong Wang, Kaixuan Zhang, 2021, Entropy.

CiteSeerX: AI in a Digital Library Search Engine

Cornelia Caragea, Prasenjit Mitra, Hung-Hsuan Chen, 2014, AI Mag..

Better Naive Bayes classification for high-precision spam detection

Yang Song, C. L. Giles, Aleksander Kolcz, 2009 .

Recognizing and Verifying Mathematical Equations using Multiplicative Differential Neural Units pdf

Daniel Kifer, C. L. Giles, Alexander Ororbia, 2021, AAAI.

Neural learning of chaotic dynamics: the error propagation algorithm

C. L. Giles, C.L. Giles, R. Bakker, 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

Reducing the Computational Burden of Deep Learning with Recursive Local Representation Alignment pdf

Daniel Kifer, C. L. Giles, Alexander Ororbia, 2020, ArXiv.

Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment.

Daniel Kifer, C. L. Giles, Alexander Ororbia, 2020 .

Accelerating Substructure Similarity Search for Formula Retrieval

C. L. Giles, Richard Zanibbi, C. Lee Giles, 2020, ECIR.

Understanding and Predicting Retractions of Published Work

Anna Cinzia Squicciarini, C. L. Giles, Jian Wu, 2021, SDU@AAAI.

Acknowledgement Entity Recognition in CORD-19 Papers

Jian Wu, C. L. Giles, Pei Wang, 2020, SDP.

Routing in Optical Multistage Interconnection Networks: a Neural Network Solution UMIACS-TR-94-21 AN

C. L. Giles, C. Lee Giles, Mark W. Goudreau, 1994 .