621 research outputs found
Continual Lifelong Learning with Neural Networks: A Review
Humans and animals have the ability to continually acquire, fine-tune, and
transfer knowledge and skills throughout their lifespan. This ability, referred
to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms
that together contribute to the development and specialization of our
sensorimotor skills as well as to long-term memory consolidation and retrieval.
Consequently, lifelong learning capabilities are crucial for autonomous agents
interacting in the real world and processing continuous streams of information.
However, lifelong learning remains a long-standing challenge for machine
learning and neural network models since the continual acquisition of
incrementally available information from non-stationary data distributions
generally leads to catastrophic forgetting or interference. This limitation
represents a major drawback for state-of-the-art deep neural network models
that typically learn representations from stationary batches of training data,
thus without accounting for situations in which information becomes
incrementally available over time. In this review, we critically summarize the
main challenges linked to lifelong learning for artificial learning systems and
compare existing neural network approaches that alleviate, to different
extents, catastrophic forgetting. We discuss well-established and emerging
research motivated by lifelong learning factors in biological systems such as
structural plasticity, memory replay, curriculum and transfer learning,
intrinsic motivation, and multisensory integration
Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning
Cortical plasticity is one of the main features that enable our ability to
learn and adapt in our environment. Indeed, the cerebral cortex self-organizes
itself through structural and synaptic plasticity mechanisms that are very
likely at the basis of an extremely interesting characteristic of the human
brain development: the multimodal association. In spite of the diversity of the
sensory modalities, like sight, sound and touch, the brain arrives at the same
concepts (convergence). Moreover, biological observations show that one
modality can activate the internal representation of another modality when both
are correlated (divergence). In this work, we propose the Reentrant
Self-Organizing Map (ReSOM), a brain-inspired neural system based on the
reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose
and compare different computational methods for unsupervised learning and
inference, then quantify the gain of the ReSOM in a multimodal classification
task. The divergence mechanism is used to label one modality based on the
other, while the convergence mechanism is used to improve the overall accuracy
of the system. We perform our experiments on a constructed written/spoken
digits database and a DVS/EMG hand gestures database. The proposed model is
implemented on a cellular neuromorphic architecture that enables distributed
computing with local connectivity. We show the gain of the so-called hardware
plasticity induced by the ReSOM, where the system's topology is not fixed by
the user but learned along the system's experience through self-organization.Comment: Preprin
A generative-discriminative learning model for noisy information fusion
International audienceThis article is concerned with the acquisition of multimodal integration capacities by learning algorithms. Humans seem to perform statistically optimal fusion, and this ability may be gradually learned from experience. In order to stress the advantage of learning approaches in contrast to hand-coded models, we propose a generative-discriminative learning architecture that avoids simplifying assumptions on prior distributions and copes with realistic relationships between observations and underlying values. We base our investigation on a simple self-organized approach, for which we show statistical near-optimality properties by explicit comparison to an equivalent Bayesian model on a realistic artificial dataset
Sensorimotor representation learning for an "active self" in robots: A model survey
Safe human-robot interactions require robots to be able to learn how to
behave appropriately in \sout{humans' world} \rev{spaces populated by people}
and thus to cope with the challenges posed by our dynamic and unstructured
environment, rather than being provided a rigid set of rules for operations. In
humans, these capabilities are thought to be related to our ability to perceive
our body in space, sensing the location of our limbs during movement, being
aware of other objects and agents, and controlling our body parts to interact
with them intentionally. Toward the next generation of robots with bio-inspired
capacities, in this paper, we first review the developmental processes of
underlying mechanisms of these abilities: The sensory representations of body
schema, peripersonal space, and the active self in humans. Second, we provide a
survey of robotics models of these sensory representations and robotics models
of the self; and we compare these models with the human counterparts. Finally,
we analyse what is missing from these robotics models and propose a theoretical
computational framework, which aims to allow the emergence of the sense of self
in artificial agents by developing sensory representations through
self-exploration
Sensorimotor Representation Learning for an “Active Self” in Robots: A Model Survey
Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration.Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft
http://dx.doi.org/10.13039/501100001659Projekt DEALPeer Reviewe
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