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Found 8 papers, 0 papers with code
Knowledge transfer in deep block-modular neural networks
Although deep neural networks (DNNs) have demonstrated impressive results during the last decade, they remain highly specialized tools, which are trained -- often from scratch -- to solve each particular task.
Learning abstract perceptual notions: the example of space
Current machine learning techniques demonstrate astonishing results in extracting patterns in information.
Learning an internal representation of the end-effector configuration space
Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors properties or end-effector position.
Learning agent's spatial configuration from sensorimotor invariants
We show that the notion of space as environment-independent cannot be deduced solely from exteroceptive information, which is highly variable and is mainly determined by the contents of the environment.
Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network
In this paper we present an alternative strategy for fine-tuning the parameters of a network.
Hyper-dimensional computing for a visual question-answering system that is trainable end-to-end
In this work we propose a system for visual question answering.
Block Neural Network Avoids Catastrophic Forgetting When Learning Multiple Task
In the present work we propose a Deep Feed Forward network architecture which can be trained according to a sequential learning paradigm, where tasks of increasing difficulty are learned sequentially, yet avoiding catastrophic forgetting.
Space as an invention of biological organisms
The question of the nature of space around us has occupied thinkers since the dawn of humanity, with scientists and philosophers today implicitly assuming that space is something that exists objectively.
