Abstract: A method and a system for using implicit neural representations for generation of interpretable time series are provided. The method includes: receiving time series information, such as pairings of time coordinate values with time series signal values, that relates to an event sequence; generating, based on the time series information, an implicit neural representation of the event sequence that includes a plurality of embedded values and a corresponding plurality of weights; and using the implicit neural representation to predict at least one item of information that relates to the event sequence and is not included in the received time series information, such as an interpolation or an extrapolation of the time series.

Abstract: Systems, methods, apparatuses, and computer program products for neural networks. In accordance with some example embodiments, an operational neuron model may comprise an artificial neuron comprising a composite nodal operator, a pool-operator, and an activation function operator. The nodal operator may comprise a linear function or non-linear function. In accordance with certain example embodiments, a generative neuron model may include a composite nodal-operator generated during the training using Taylor polynomial approximation without restrictions. In accordance with various example embodiments, a self-organized operational neural network (Self-ONN) may include one or more layers of generative neurons.

Abstract: Certain embodiments may generally relate to various techniques for machine learning. Feed-forward, fully-connected Artificial Neural Networks (ANNs), or the so-called Multi-Layer Perceptrons (MLPs) are well-known universal approximators. However, their learning performance may vary significantly depending on the function or the solution space that they attempt to approximate for learning. This is because they are based on a loose and crude model of the biological neurons promising only a linear transformation followed by a nonlinear activation function. Therefore, while they learn very well those problems with a monotonous, relatively simple and linearly separable solution space, they may entirely fail to do so when the solution space is highly nonlinear and complex. In order to address this drawback and also to accomplish a more generalized model of biological neurons and learning systems, Generalized Operational Perceptrons (GOPs) may be formed and they may encapsulate many linear and nonlinear operators.

Abstract: Certain embodiments may generally relate to various techniques for machine learning. Feed-forward, fully-connected Artificial Neural Networks (ANNs), or the so-called Multi-Layer Perceptrons (MLPs) are well-known universal approximators. However, their learning performance may vary significantly depending on the function or the solution space that they attempt to approximate for learning. This is because they are based on a loose and crude model of the biological neurons promising only a linear transformation followed by a nonlinear activation function. Therefore, while they learn very well those problems with a monotonous, relatively simple and linearly separable solution space, they may entirely fail to do so when the solution space is highly nonlinear and complex. In order to address this drawback and also to accomplish a more generalized model of biological neurons and learning systems, Generalized Operational Perceptrons (GOPs) may be formed and they may encapsulate many linear and nonlinear operators.