Abstract. By embedding the boolean space Z2 as an orthonormal basis in a vec-tor space we can treat the RAM based neuron as a matrix (operator) acting on the vector space. We show how this model (inspired by our research on quantum neu-ral networks) is of sufficient generality as to have classical weighted (perceptron-like), classical weightless (RAM-based, PLN, etc), quantum weighted and quantum weightless neural models as particular cases. It is also indicated how one could use it to polynomially solve 3-SAT and briefly mention how could one train this novel model.
The learning process for multilayered neural networks with many nodes makes heavy demands on computa...
Abstract. There has been a growing interest in articial neural networks (ANNs) based on quantum theo...
Artificial neural networks are the heart of machine learning algorithms and artificial intelligence....
Abstract. Random Access Memory (RAM) nodes can play the role of artificial neurons that are addresse...
This paper presents a survey of a class of neural models known as Weightless Neural Networks (WNNs)....
Abstract: Quantum computation uses microscopic quantum level effects to perform computational tasks ...
Abstract: Quantum computation uses microscopic quantum level effects to perform computational tasks ...
Orthogonal neural networks have recently been introduced as a new type of neural networks imposing o...
AbstractThis paper initiates the study of quantum computing within the constraints of using a polylo...
We present a memory-efficient quantum algorithm implementing the action of an artificial neuron acco...
Abstract. This chapter outlines the research, development and perspectives of quantum neural network...
We propose a quantum generalisation of a classical neural network. The classical neurons are firstly...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
A common framework for architectures combining multiple vector-quantization of the input space with ...
The human brain is a complex system composed of a network of hundreds of billions of dis-crete neuro...
The learning process for multilayered neural networks with many nodes makes heavy demands on computa...
Abstract. There has been a growing interest in articial neural networks (ANNs) based on quantum theo...
Artificial neural networks are the heart of machine learning algorithms and artificial intelligence....
Abstract. Random Access Memory (RAM) nodes can play the role of artificial neurons that are addresse...
This paper presents a survey of a class of neural models known as Weightless Neural Networks (WNNs)....
Abstract: Quantum computation uses microscopic quantum level effects to perform computational tasks ...
Abstract: Quantum computation uses microscopic quantum level effects to perform computational tasks ...
Orthogonal neural networks have recently been introduced as a new type of neural networks imposing o...
AbstractThis paper initiates the study of quantum computing within the constraints of using a polylo...
We present a memory-efficient quantum algorithm implementing the action of an artificial neuron acco...
Abstract. This chapter outlines the research, development and perspectives of quantum neural network...
We propose a quantum generalisation of a classical neural network. The classical neurons are firstly...
A new architecture for networks of RAM-based Boolean neurons is presented which, whilst retaining le...
A common framework for architectures combining multiple vector-quantization of the input space with ...
The human brain is a complex system composed of a network of hundreds of billions of dis-crete neuro...
The learning process for multilayered neural networks with many nodes makes heavy demands on computa...
Abstract. There has been a growing interest in articial neural networks (ANNs) based on quantum theo...
Artificial neural networks are the heart of machine learning algorithms and artificial intelligence....