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Informed machine learning–a taxonomy and survey of integrating prior knowledge into learning systems
Despite its great success, machine learning can have its limits when dealing with insufficient
training data. A potential solution is the additional integration of prior knowledge into the …
training data. A potential solution is the additional integration of prior knowledge into the …
[HTML][HTML] The power of Deep Learning techniques for predicting student performance in Virtual Learning Environments: A systematic literature review
With the advances in Artificial Intelligence (AI) and the increasing volume of online
educational data, Deep Learning techniques have played a critical role in predicting student …
educational data, Deep Learning techniques have played a critical role in predicting student …
Logicseg: Parsing visual semantics with neural logic learning and reasoning
Current high-performance semantic segmentation models are purely data-driven sub-
symbolic approaches and blind to the structured nature of the visual world. This is in stark …
symbolic approaches and blind to the structured nature of the visual world. This is in stark …
Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning
Current advances in Artificial Intelligence and machine learning in general, and deep
learning in particular have reached unprecedented impact not only across research …
learning in particular have reached unprecedented impact not only across research …
Chapter 1. Neural-Symbolic Learning and Reasoning: A Survey and Interpretation 1
The study and understanding of human behaviour is relevant to computer science, artificial
intelligence, neural computation, cognitive science, philosophy, psychology, and several …
intelligence, neural computation, cognitive science, philosophy, psychology, and several …
End-to-end differentiable proving
We introduce deep neural networks for end-to-end differentiable theorem proving that
operate on dense vector representations of symbols. These neural networks are recursively …
operate on dense vector representations of symbols. These neural networks are recursively …
[ספר][B] Lifelong machine learning
Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …
learning paradigm that continuously learns by accumulating past knowledge that it then …
Deep learning in neural networks: An overview
In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …
numerous contests in pattern recognition and machine learning. This historical survey …
Logical neural networks
We propose a novel framework seamlessly providing key properties of both neural nets
(learning) and symbolic logic (knowledge and reasoning). Every neuron has a meaning as a …
(learning) and symbolic logic (knowledge and reasoning). Every neuron has a meaning as a …
Learning to compose neural networks for question answering
We describe a question answering model that applies to both images and structured
knowledge bases. The model uses natural language strings to automatically assemble …
knowledge bases. The model uses natural language strings to automatically assemble …