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Word recognition using hidden Markov models and neural associative memories

Kara Kayikci, ZöhreUniversität Ulm

ulm-diss Repository record for Word recognition using hidden Markov models and neural associative memories (opens in a new tab)

Neural architectures for database query processing, syntax analysis, knowledge representation, and inference

Chen, Chun-Hsien1997

<p>Artificial neural networks (ANN), due to their inherent parallelism, potential for fault tolerance, and adaptation through learning, offer an attractive computational paradigm for a variety of applications in computer science and engineering, artificial intelligence, robotics, and cognitive …

iastate Repository record for Neural architectures for database query processing, syntax analysis, knowledge representation, and inference (opens in a new tab)

Are Associations All You Need? Overcoming the Problem of One-to-Many Association and Nonlinear Relationships with Contiguity in a Bidirectional Associative Memory Framework

Rolon-Mérette, DamiemUniversité d'Ottawa | University of Ottawa2024

… processing model. It focused on using recurrent neural associative memories, specifically, a biological and cognitively plausible Bidirectional Associative Memory framework. The objective was to overcome its current limitations in learning one-to-many associations and nonlinear relationships. It …

ottawa-retro Repository record for Are Associations All You Need? Overcoming the Problem of One-to-Many Association and Nonlinear Relationships with Contiguity in a Bidirectional Associative Memory Framework (opens in a new tab)