nips13

NeurIPS(NIPS) 2000 论文列表

Hybrid Neural Systems, revised papers from a workshop held at NIPS'08, Denver, CO, USA, December 4-5, 1998.

Application of Neurosymbolic Integration for Environment Modelling in Mobile Robots.
A Cellular Neural Associative Array for Symbolic Vision.
Evolution of Symbolization: Signposts to a Bridge Between Connectionist and Symbolic Systems.
Self-Organizing Maps in Symbol Processing.
Supplementing Neural Reinforcement Learning with Symbolic Methods.
Life, Mind, and Robots: The Ins and Outs of Embodied Cognition.
Holistic Symbol Processing and the Sequential RAAM: An Evalutation.
High Order Eigentensors as Symbolic Rules in Competitive Learning.
Direct Explanations and Knowledge Extraction from a Multilayer Perceptron Network that Performs Low Back Pain Classification.
Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics.
Symbolic Rule Extraction from the DIMLP Neural Network.
Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks.
Integration of Graphical Rules with Adaptive Learning of Structured Information.
Context Vectors: A Step Toward a "Grand Unified Representation".
Large Patterns Make Great Symbols: An Example of Learning from Example.
A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations.
Towards Hybrid Neural Learning Internet Agents.
Combining Maps and Distributed Representations for Shift-Reduce Parsing.
Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective.
Dynamical Recurrent Networks for Sequential Data Processing.
Towards a Hybrid Model of First-Order Theory Refinement.
Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference.
A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning.
A Recursive Neural Network for Reflexive Reasoning.
Types and Quantifiers in SHRUTI: A Connectionist Model of Rapid Reasoning and Relational Processing.
Layered Hybrid Connectionist Models for Cognitive Science.
An Overview of Hybrid Neural Systems.