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公开(公告)号：US08666917B2
公开(公告)日：2014-03-04
申请号：US13604543
申请日：2012-09-05
申请人： Robert G. Jaros , Dileep George , Jeffrey Hawkins , Frank Astier
发明人： Robert G. Jaros , Dileep George , Jeffrey Hawkins , Frank Astier
IPC分类号： G06F15/18
CPC分类号： G06N3/08 , G06F15/18 , G06N3/04 , G06N3/0454 , G06N3/0472 , G06N3/049 , G06N5/02 , G06N7/005 , G06N99/005
摘要： 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.
摘要翻译： 计算模块的层次结构被配置为学习在空间和时间上感测的输入数据的原因，并且还被配置为根据学习的原因确定新感测的输入数据的原因。 计算模块中的至少一个具有序列学习器模块，其被配置为将由计算模块接收的输入数据的序列与先前在层次结构中学习的一组原因相关联。
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2.发明申请HIERARCHICAL TEMPORAL MEMORY SYSTEM WITH HIGHER-ORDER TEMPORAL POOLING CAPABILITY 有权具有较高时间平移能力的分层时间记忆系统公开(公告)号：US20090313193A1
公开(公告)日：2009-12-17
申请号：US12483642
申请日：2009-06-12
申请人： Jeffrey C. Hawkins , Dileep George , Charles Curry , Frank E. Astier , Anosh Raj , Robert G. Jaros
发明人： Jeffrey C. Hawkins , Dileep George , Charles Curry , Frank E. Astier , Anosh Raj , Robert G. Jaros
CPC分类号： G06N3/049
摘要： 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.
摘要翻译： 提供了一种用于分层时域存储器网络的时间池。 时间池能够通过将共现分裂为多个次出现来存储关于高阶马尔可夫链中的共现序列的信息。 每个分裂子事件可以是共同出现的不同序列的一部分。 时间分组器接收训练模式中的空间共现概率，并且在连接矩阵中从一个子出现转换到另一个子出现的计数计数或转换频率。 然后处理连通性矩阵以生成时间统计数据。 将时间统计数据提供给推理机以对输入模式执行推断或预测。 通过存储与高阶马尔科夫模型相关的信息，时间统计数据更准确地反映训练模式中共同出现的长时间序列。
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公开(公告)号：US20150227849A1
公开(公告)日：2015-08-13
申请号：US12962632
申请日：2010-12-07
CPC分类号： G06N7/005 , G06N3/02 , G06N3/0454 , G06N3/08 , G06N99/005
摘要： 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,
摘要翻译： 自适应模式识别系统优化不变性目标和输入保真度目标，以便在存在任意输入变换的情况下准确地识别输入模式。 特征输出的固定状态或值可以非线性地重建或生成多个空间遥远的输入模式，并且类似地响应于多个空间上较远的输入模式，同时保持有效评估输入保真度目标的能力。 示例性网络，包括三阶玻尔兹曼机器的新因子分解，展现了任意变换的多层次，无监督学习，并且即使在没有标记数据的情况下也学习丰富复杂的特征。 然后将这些特征用于对未知输入模式进行分类，进行降维或压缩，
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公开(公告)号：US08504494B2
公开(公告)日：2013-08-06
申请号：US13227355
申请日：2011-09-07
摘要： 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.
摘要翻译： 时空学习节点是一种HTM节点，可随时间学习感测输入模式的空间和时间组。 空间学习节点包括用于确定一组感测输入模式中的空间组的空间分组器。 时空学习节点还包括用于确定时间上共同发生的感测输入模式的组的时间分组器。 时空学习网络是包括多个时空学习节点的分层网络。
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公开(公告)号：US20110231351A1
公开(公告)日：2011-09-22
申请号：US13151928
申请日：2011-06-02
申请人： Dileep George , Robert G. Jaros
发明人： Dileep George , Robert G. Jaros
IPC分类号： G06F15/18
摘要： 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.
摘要翻译： 分级时间存储器（HTM）网络至少具有第一节点和比第一节点更高的第二节点。 第二节点向第一节点提供节点间反馈信号，用于对在第一节点处的第一节点处接收的输入数据中的模式和序列（或共同出现）进行分组。 第二节点从第一个节点收集前向信号; 因此，第二节点具有关于第一节点处的模式和序列（或共同出现）的分组的信息。 第二节点向第一节点提供节点间反馈信号，基于该节点，第一节点可以在第一节点处执行在第一节点处的模式和序列（或共同出现）的分组。
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公开(公告)号：US20080208783A1
公开(公告)日：2008-08-28
申请号：US12039630
申请日：2008-02-28
IPC分类号： G06N3/08
摘要： 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.
摘要翻译： 时空学习节点是一种HTM节点，可随时间学习感测输入模式的空间和时间组。 空间学习节点包括用于确定一组感测输入模式中的空间组的空间分组器。 时空学习节点还包括用于确定时间上共同发生的感测输入模式的组的时间分组器。 时空学习网络是包括多个时空学习节点的分层网络。
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7.发明授权Hierarchical temporal memory system with higher-order temporal pooling capability 有权具有较高阶时间池能力的分层时间记忆系统公开(公告)号：US08407166B2
公开(公告)日：2013-03-26
申请号：US12483642
申请日：2009-06-12
申请人： Jeffrey C. Hawkins , Dileep George , Charles Curry , Frank E. Astier , Anosh Raj , Robert G. Jaros
发明人： Jeffrey C. Hawkins , Dileep George , Charles Curry , Frank E. Astier , Anosh Raj , Robert G. Jaros
CPC分类号： G06N3/049
摘要： 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.
摘要翻译： 提供了一种用于分层时域存储器网络的时间池。 时间池能够通过将共现分裂为多个次出现来存储关于高阶马尔可夫链中的共现序列的信息。 每个分裂子事件可以是共同出现的不同序列的一部分。 时间分组器接收训练模式中的空间共现概率，并且在连接矩阵中从一个子出现转换到另一个子出现的计数计数或转换频率。 然后处理连通性矩阵以生成时间统计数据。 将时间统计数据提供给推理机以对输入模式执行推断或预测。 通过存储与高阶马尔科夫模型相关的信息，时间统计数据更准确地反映训练模式中共同出现的长时间序列。
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公开(公告)号：US08285667B2
公开(公告)日：2012-10-09
申请号：US12576966
申请日：2009-10-09
CPC分类号： G06N3/08 , G06F15/18 , G06N3/04 , G06N3/0454 , G06N3/0472 , G06N3/049 , G06N5/02 , G06N7/005 , G06N99/005
摘要： 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.
摘要翻译： 计算模块的层次结构被配置为学习在空间和时间上感测的输入数据的原因，并且还被配置为根据学习的原因确定新感测的输入数据的原因。 计算模块中的至少一个具有序列学习器模块，其被配置为将由计算模块接收的输入数据的序列与先前在层次结构中学习的一组原因相关联。
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公开(公告)号：US20120005134A1
公开(公告)日：2012-01-05
申请号：US13227355
申请日：2011-09-07
IPC分类号： G06F15/18
摘要： 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.
摘要翻译： 时空学习节点是一种HTM节点，可随时间学习感测输入模式的空间和时间组。 空间学习节点包括用于确定一组感测输入模式中的空间组的空间分组器。 时空学习节点还包括用于确定时间上共同发生的感测输入模式的组的时间分组器。 时空学习网络是包括多个时空学习节点的分层网络。
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公开(公告)号：US20090240639A1
公开(公告)日：2009-09-24
申请号：US12053204
申请日：2008-03-21
申请人： Dileep George , Robert G. Jaros
发明人： Dileep George , Robert G. Jaros
IPC分类号： G06F15/18
摘要： 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.
摘要翻译： 分级时间存储器（HTM）网络至少具有第一节点和比第一节点更高的第二节点。 第二节点向第一节点提供节点间反馈信号，用于对在第一节点处的第一节点处接收的输入数据中的模式和序列（或共同出现）进行分组。 第二节点从第一个节点收集前向信号; 因此，第二节点具有关于第一节点处的模式和序列（或共同出现）的分组的信息。 第二节点向第一节点提供节点间反馈信号，基于该节点，第一节点可以在第一节点处执行在第一节点处的模式和序列（或共同出现）的分组。 此外，分层时间存储器（HTM）网络中的节点包括共同检测器和耦合到同现检测器的组学习者。 组合学习者向共生检测器提供节点间反馈信号，其包括关于共同出现的分组的信息。 共同检测器可以基于节点内反馈信号来选择要分裂，合并，保留或丢弃的共现。
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