Temporal classification: extending the classification paradigm to multivariate time series
Temporal classification: extending the classification paradigm to multivariate time series
Learning invariant features using inertial priors
Annals of Mathematics and Artificial Intelligence
Computer Recognition Systems 3
Computer Recognition Systems 3
Gaze gesture recognition with hierarchical temporal memory networks
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Sensing with artificial tactile sensors: an investigation of spatio-temporal inference
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
Gliding and saccadic gaze gesture recognition in real time
ACM Transactions on Interactive Intelligent Systems (TiiS)
On the optimization of Hierarchical Temporal Memory
Pattern Recognition Letters
Low cost remote gaze gesture recognition in real time
Applied Soft Computing
Evolving hierarchical temporal memory-based trading models
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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Hierarchical Temporal Memory (HTM) is an emerging computational paradigm consisting of a hierarchically connected network of nodes. The hierarchy models a key design principle of neocortical organization. Nodes throughout the hierarchy encode information by means of clustering spatial instances within their receptive fields according to temporal proximity. Literature shows HTMs' robust performance on traditional machine learning tasks such as image recognition. Problems involving multi-variable time series where instances unfold over time with no complete spatial representation at any point in time have proven trickier for HTMs. We have extended the traditional HTMs' principles by means of a top node that stores and aligns sequences of input patterns representing the spatio-temporal structure of instances to be learned. This extended HTM network improves performance with respect to traditional HTMs in machine learning tasks whose input instances unfold over time.