An intrinsic value system for developing multiple invariant representations with incremental slowness learning

By Matthew D. Luciw, Varun Raj Kompella, Sohrob Kazerounian, J. Urgen Schmidhuber. 2013

35 citations recorded by the source corpus.

Curiosity Driven Modular Incremental Slow Feature Analysis (CD-MISFA;) is a recently introduced model of intrinsically-motivated invariance learning. Artificial curiosity enables the orderly formation of multiple stable sensory representations to simplify the agent's complex sensory input. We discuss computational properties of the CD-MISFA model itself as well as neurophysiological analogs fulfilling similar functional roles. CD-MISFA combines 1. unsupervised representation learning through the slowness principle, 2. generation of an intrinsic reward signal through learning progress of the developing features, and 3. balancing of exploration and exploitation to maximize learning progress and quickly learn multiple feature sets for perceptual simplification. Experimental results on synthetic observations and on the iCub robot show that the intrinsic value system is essential for representation learning. Representations are typically explored and learned in order from least to most costly, as predicted by the theory of curiosity.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “, 2012) enabled a real humanoid robot to learn skills from raw high-dimensional video streams (Luciw et al., 2013).”
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models (2015, methodology): “RL combined with unsupervised learning based on Slow Feature Analysis [318, 131] enabled a humanoid robot to learn skills from raw video streams [154].”
Value and reward based learning in neurorobots (2013, methodology): “In a model called CURIOUSity-DRiven, Modular, Incremental Slow Feature Analysis (Curious Dr. MISFA), Luciw and colleagues showed that curiosity could shape the behavior of an iCub robot in a multi-context environment (Luciw et al., 2013).”