Patent
Neural associative memories based on optimal bayesian learning
TLDR
In this paper, a neural associative memory structure for storing and maintaining associations between memory address patterns and memory content patterns using a neural network, as well as methods for storing, retrieving and storing such associations.Abstract:
This invention is in the field of machine learning and neural associative memory. In particular the invention discloses a neural associative memory structure for storing and maintaining associations between memory address patterns and memory content patterns using a neural network, as well as methods for storing and retrieving such associations. Bayesian learning is applied to achieve non-linear learning.read more
Citations
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Journal ArticleDOI
Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN.
TL;DR: In this paper, the authors optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes CNN more suitable for short text classification and deceptive opinions detection.
Patent
Methods and systems for artificial cognition
Chris Eliasmith,Terrence C. Stewart,Feng-Xuan Choo,Trevor Bekolay,Travis Crncich-DeWolf,Yichuan Tang,Daniel Rasmussen +6 more
TL;DR: Semantic pointers as mentioned in this paper can be thought of as elements of a neural vector space, and can implement a form of abstraction level filtering or compression, in which high-dimensional structures can be abstracted one or more times thereby reducing the number of dimensions needed to represent a particular structure.
Patent
Determining Context Specific Content
TL;DR: In this paper, a computer-implemented method for identifying program components, generates associations between the program components stored in a computer usable storage medium to create a first set of relationships, and assigns, by the processor unit, a probability to each relationship in the first sets of relationships.
Patent
Managing and maintaining scope in a service oriented architecture industry model repository
TL;DR: In this article, a method of managing and maintaining scope in a of service oriented architecture industry model repository comprising defining boundaries and definitions of scope and scope propagation of topics, associations, and occurrences within the SOA IMR is presented.
Patent
Temporal scope translation of meta-models using semantic web technologies
Anthony L. Carrato,Peter A. Coldicott,Raymond Walter Ellis,Richard A. Hopkins,Brad N. Jones,Edward E. Kelley,Eoin Lane,Ian J. Scott,Franciscus J. J. van Ham,Anthony J. Young-Garder +9 more
TL;DR: In this paper, the authors describe the implementation of a meta-model service of a service oriented architecture industry model repository into a web ontology language representation of at least one topic map meta model into a plurality of temporal scope meta-models.
References
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Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Journal ArticleDOI
Non-holographic associative memory
TL;DR: The features of a hologram that commend it as a model of associative memory can be improved on by other devices.
Book ChapterDOI
Preliminary discussion of the logical design of an electronic computing instrument (1946)
TL;DR: It is intended that the machine be fully automatic in character, i.e. independent of the human operator after the computation starts.
Journal ArticleDOI
Learning optimized features for hierarchical models of invariant object recognition
Heiko Wersing,Edgar Körner +1 more
TL;DR: This work proposes a feedforward model for recognition that shares components like weight sharing, pooling stages, and competitive nonlinearities with earlier approaches but focuses on new methods for learning optimal feature-detecting cells in intermediate stages of the hierarchical network.