Numenta, Inc.

Yahoo! Inc.

Oath Inc.

Robert Gilchrist Jaros

Dileep George

Jeffrey Charles Hawkins

Frank Emmanuel Astier

Simon Kayode Osindero

Jeffrey L Edwards

Anastasia Alexeevna Svetlichnaya

Clayton Ellis Mellina

Eric Willis

Anosh Raj

Charles Curry

A representation of a collection of content items is generated for display by a computing device. The representation includes a two-or-more-dimensional arrangement including representations of the content items. The representations of the content items are positioned relative to one another based, at least in part, on values of one or more attributes of the digital content items. The representation is dynamically adjusted based, at least in part, on a user interaction with a representation of one of the content items.

Dynamic content mapping

Briefly, embodiments disclosed herein may relate to dynamic display of representations of digital content for user interaction on computing devices, for example, including displaying a collection of content items on a display of a computing device, wherein a two-or-more-dimensional spatial arrangement of individual content items of the displayed collection comprises individual content items being positioned relative to other individual content items based, at least in part, on analytic characterizations of one or more attributes of the individual content items.

An adaptive pattern recognition system optimizes an invariance objective and an input fidelity objective to accurately recognize input patterns in the presence of arbitrary input transformations. A fixed state or value of a feature output can nonlinearly reconstruct or generate multiple spatially distant input patterns and respond similarly to multiple spatially distant input patterns, while preserving the ability to efficiently evaluate the input fidelity objective. Exemplary networks, including a novel factorization of a third-order Boltzmann machine, exhibit multilayered, unsupervised learning of arbitrary transformations, and learn rich, complex features even in the absence of labeled data. These features are then used to classify unknown input patterns, to perform dimensionality reduction or compression.

Method and system for invariant pattern recognition

A hierarchy of computing modules is configured to learn a cause of input data sensed over space and time, and is further configured to determine a cause of novel sensed input data dependent on the learned cause. At least one of the computing modules has a sequence learner module configured to associate sequences of input data received by the computing module to a set of causes previously learned in the hierarchy.

Sequence learning in a hierarchical temporal memory based system

A spatio-temporal learning node is a type of HTM node which learns both spatial and temporal groups of sensed input patterns over time. Spatio-temporal learning nodes comprise spatial poolers which are used to determine spatial groups in a set of sensed input patterns. The spatio-temporal learning nodes further comprise temporal poolers which are used to determine groups of sensed input patterns that temporally co-occur. A spatio-temporal learning network is a hierarchical network including a plurality of spatio-temporal learning nodes.

Spatio-temporal learning algorithms in hierarchical temporal networks

A temporal pooler for a Hierarchical Temporal Memory network is provided. The temporal pooler is capable of storing information about sequences of co-occurrences in a higher-order Markov chain by splitting a co-occurrence into a plurality of sub-occurrences. Each split sub-occurrence may be part of a distinct sequence of co-occurrences. The temporal pooler receives the probability of spatial co-occurrences in training patterns and tallies counts or frequency of transitions from one sub-occurrence to another sub-occurrence in a connectivity matrix. The connectivity matrix is then processed to generate temporal statistics data. The temporal statistics data is provided to an inference engine to perform inference or prediction on input patterns. By storing information related to a higher-order Markov model, the temporal statistics data more accurately reflects long temporal sequences of co-occurrences in the training patterns.

Hierarchical temporal memory system with higher-order temporal pooling capability

A node, a computer program storage medium, and a method for a hierarchical temporal memory (HTM) network where at least one of its nodes generates a top-down message and sends the top-down message to one or more children nodes in the HTM network. The first top-down message represents information about the state of a node and functions as feedback information from a current node to its child node. The node may also maintain history of the input patterns or co-occurrences so that temporal relationships between input patterns or co-occurrences may be taken into account in an inference stage. By providing the top-town message and maintaining history of previous input patterns, the HTM network may, among others, (i) perform more accurate inference based on temporal history, (ii) make predictions, (iii) discriminate between spatial co-occurrences with different temporal histories, (iv) detect 'surprising' temporal patterns, (v) generate examples from a category, and (vi) fill in missing or occluded data.

Hierarchical temporal memory system with enhanced inference capability

A Hierarchical Temporal Memory (HTM) network has at least first nodes and a second node at a higher level than the first nodes. The second node provides an inter-node feedback signal to the first nodes for grouping patterns and sequences (or co-occurrences) in input data received at the first nodes at the first nodes. The second node collects forward signals from the first nodes; and thus, the second node has information about the grouping of the patterns and sequences (or co-occurrences) at the first nodes. The second node provides inter-node feedback signals to the first nodes based on which the first nodes may perform the grouping of the patterns and sequences (or co-occurrences) at the first nodes.

Feedback in group based hierarchical temporal memory system

A Hierarchical Temporal Memory (HTM) network has at least first nodes and a second node at a higher level than the first nodes. The second node provides an inter-node feedback signal to the first nodes for grouping patterns and sequences (or co-occurrences) in input data received at the first nodes at the first nodes. The second node collects forward signals from the first nodes; and thus, the second node has information about the grouping of the patterns and sequences (or co-occurrences) at the first nodes. The second node provides inter-node feedback signals to the first nodes based on which the first nodes may perform the grouping of the patterns and sequences (or co-occurrences) at the first nodes. Also, a node in a Hierarchical Temporal Memory (HTM) network comprising a co-occurrence detector and a group learner coupled to the co-occurrence detector. The group learner provides an intra-node feedback signal to the co-occurrence detector including information on the grouping of the co-occurrences. The co-occurrence detector may select co-occurrences to be split, merged, retained or discarded based on the intra-node feedback signals.

An HTM node learns a plurality of groups of sensed input patterns over time based on the frequency of temporal adjacency of the input patterns. An HTM node receives a new sensed input, the HTM node assigns probabilities as to the likelihood that the new sensed input matches each of the plurality of learned groups. The HTM node then combines this probability distribution (may be normalized) with previous state information to assign probabilities as to the likelihood that the new sensed input is part of each of the learned groups of the HTM node. Then, as described above, the distribution over the set of groups learned by the HTM node is passed to a higher level node. This process is repeated at higher level nodes to infer a cause of the newly sensed input.

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Palo Alto, CA, United States of America

Robert Gilchrist Jaros