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ORCIDHybrid Neural Systems@NIPS 1998
- Stefan Wermter, Ron Sun:
Hybrid Neural Systems, revised papers from a workshop held at NIPS'08, Denver, CO, USA, December 4-5, 1998. Lecture Notes in Computer Science 1778, Springer 2000, ISBN 3-540-67305-9 - Stefan Wermter, Ron Sun:
An Overview of Hybrid Neural Systems. 1-13
Structured Connectionism and Rule Representation
- Jerome A. Feldman, David Bailey:
Layered Hybrid Connectionist Models for Cognitive Science. 14-27 - Lokendra Shastri:
Types and Quantifiers in SHRUTI: A Connectionist Model of Rapid Reasoning and Relational Processing. 28-45 - Steffen Hölldobler, Yvonne Kalinke, Jörg Wunderlich:
A Recursive Neural Network for Reflexive Reasoning. 46-62 - Rafal Bogacz, Christophe G. Giraud-Carrier
:
A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning. 63-77 - Nam Seog Park:
Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference. 78-91 - Nelson A. Hallack, Gerson Zaverucha, Valmir Carneiro Barbosa:
Towards a Hybrid Model of First-Order Theory Refinement. 92-106
Distributed Neural Architectures and Language Processing
- Stefan C. Kremer, John F. Kolen:
Dynamical Recurrent Networks for Sequential Data Processing. 107-122 - Christian W. Omlin, C. Lee Giles, Karvel K. Thornber:
Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective. 123-143 - Marshall R. Mayberry, Risto Miikkulainen:
Combining Maps and Distributed Representations for Shift-Reduce Parsing. 144-157 - Stefan Wermter, Garen Arevian, Christo Panchev:
Towards Hybrid Neural Learning Internet Agents. 158-174 - William C. Morris, Garrison W. Cottrell, Jeffrey L. Elman:
A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations. 175-193 - Pentti Kanerva:
Large Patterns Make Great Symbols: An Example of Learning from Example. 194-203 - Stephen I. Gallant:
Context Vectors: A Step Toward a "Grand Unified Representation". 204-210 - Paolo Frasconi, Marco Gori, Alessandro Sperduti:
Integration of Graphical Rules with Adaptive Learning of Structured Information. 211-225
Transformation and Explanation
- Alan B. Tickle, Frédéric Maire, Guido Bologna, Robert Andrews, Joachim Diederich:
Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks. 226-239 - Guido Bologna:
Symbolic Rule Extraction from the DIMLP Neural Network. 240-254 - Peter Tiño, Georg Dorffner, Christian Schittenkopf:
Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics. 255-269 - Marylin L. Vaughn, Steven J. Cavill, Stewart J. Taylor, Michael A. Foy, Anthony J. B. Fogg:
Direct Explanations and Knowledge Extraction from a Multilayer Perceptron Network that Performs Low Back Pain Classification. 270-285 - Hod Lipson, Hava T. Siegelmann:
High Order Eigentensors as Symbolic Rules in Competitive Learning. 286-297 - James Alistair Hammerton, Barry L. Kalman:
Holistic Symbol Processing and the Sequential RAAM: An Evalutation. 298-312
Robotics, Vision and Cognitive Approaches
- Noel E. Sharkey, Tom Ziemke:
Life, Mind, and Robots: The Ins and Outs of Embodied Cognition. 313-332 - Ron Sun:
Supplementing Neural Reinforcement Learning with Symbolic Methods. 333-347 - Timo Honkela:
Self-Organizing Maps in Symbol Processing. 348-362 - Ronan Reilly:
Evolution of Symbolization: Signposts to a Bridge Between Connectionist and Symbolic Systems. 363-371 - Christos Orovas, Jim Austin:
A Cellular Neural Associative Array for Symbolic Vision. 372-386 - Gerhard K. Kraetzschmar, Stefan Sablatnög, Stefan Enderle, Günther Palm:
Application of Neurosymbolic Integration for Environment Modelling in Mobile Robots. 387-401
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