Historically, artificial intelligence has drawn much inspiration from neuroscience to fuel advances in the field.
In case the dimensionality is not predefined, this parameter is usually determined using time- and resource-consuming cross-validation.
Active inference is a theory that underpins the way biological agent's perceive and act in the real world.
Music that is generated by recurrent neural networks often lacks a sense of direction and coherence.
Active inference is a process theory of the brain that states that all living organisms infer actions in order to minimize their (expected) free energy.
Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world.
Recurrent neural networks are nowadays successfully used in an abundance of applications, going from text, speech and image processing to recommender systems.
Traditional textual representations, such as tf-idf, have difficulty grasping the semantic meaning of such texts, which is important in applications such as event detection, opinion mining, news recommendation, etc.
In this paper we propose a technique which avoids the evaluation of certain convolutional filters in a deep neural network.
We present four training and prediction schedules from the same character-level recurrent neural network.
We therefore investigated several text representations as a combination of word embeddings in the context of semantic pair matching.