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公开(公告)号：US11586915B2
公开(公告)日：2023-02-21
申请号：US16772094
申请日：2018-12-12
Applicant: D-WAVE SYSTEMS INC.
Inventor： William G. Macready , Jason T. Rolfe
Abstract: Collaborative filtering systems based on variational autoencoders (VAEs) are provided. VAEs may be trained on row-wise data without necessarily training a paired VAE on column-wise data (or vice-versa), and may optionally be trained via minibatches. The row-wise VAE models the output of the corresponding column-based VAE as a set of parameters and uses these parameters in decoding. In some implementations, a paired VAE is provided which receives column-wise data and models row-wise parameters; each of the paired VAEs may bind their learned column- or row-wise parameters to the output of the corresponding VAE. The paired VAEs may optionally be trained via minibatches. Unobserved data may be explicitly modelled. Methods for performing inference with such VAE-based collaborative filtering systems are also disclosed, as are example applications to search and anomaly detection.
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公开(公告)号：US11481669B2
公开(公告)日：2022-10-25
申请号：US16336625
申请日：2017-09-26
Applicant: D-WAVE SYSTEMS INC.
Inventor： Jason T. Rolfe , William G. Macready , Mani Ranjbar , Mayssam Mohammad Nevisi
Abstract: A digital processor runs a machine learning algorithm in parallel with a sampling server. The sampling sever may continuously or intermittently draw samples for the machine learning algorithm during execution of the machine learning algorithm, for example on a given problem. The sampling server may run in parallel (e.g., concurrently, overlapping, simultaneously) with a quantum processor to draw samples from the quantum processor.
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公开(公告)号：US11238131B2
公开(公告)日：2022-02-01
申请号：US15399461
申请日：2017-01-05
Applicant: D-Wave Systems Inc.
Inventor： Firas Hamze , James King , Evgeny Andriyash , Catherine McGeoch , Jack Raymond , Jason Rolfe , William G. Macready , Aaron Lott , Murray C. Thom
Abstract: The systems, devices, articles, and methods generally relate to sampling from an available probability distribution. The samples maybe used to create a desirable probability distribution, for instance for use in computing values used in computational techniques including: Importance Sampling and Markov chain Monte Carlo systems. An analog processor may operate as a sample generator, for example by: programming the analog processor with a configuration of the number of programmable parameters for the analog processor, which corresponds to a probability distribution over qubits of the analog processor, evolving the analog processor, and reading out states for the qubits. The states for the qubits in the plurality of qubits correspond to a sample from the probability distribution. Operation of the sampling device may be summarized as including updating a set of samples to include the sample from the probability distribution, and returning the set of samples.
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公开(公告)号：US10817796B2
公开(公告)日：2020-10-27
申请号：US15452438
申请日：2017-03-07
Applicant: D-Wave Systems Inc.
Inventor： William G. Macready , Firas Hamze , Fabian A. Chudak , Mani Ranjbar , Jack R. Raymond , Jason T. Rolfe
IPC: G06N10/00 , G06N7/00 , G06N20/00 , G06K9/62 , G06F111/10
Abstract: A hybrid computer comprising a quantum processor can be operated to perform a scalable comparison of high-entropy samplers. Performing a scalable comparison of high-entropy samplers can include comparing entropy and KL divergence of post-processed samplers. A hybrid computer comprising a quantum processor generates samples for machine learning. The quantum processor is trained by matching data statistics to statistics of the quantum processor. The quantum processor is tuned to match moments of the data.
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15.发明申请公开(公告)号：US20180101784A1
公开(公告)日：2018-04-12
申请号：US15725600
申请日：2017-10-05
Applicant: D-Wave Systems Inc.
Inventor： Jason Rolfe , William G. Macready , Zhengbing Bian , Fabian A. Chudak
CPC classification number: G06N10/00 , G06F15/80 , G06K9/00986 , G06K9/6256 , G06N3/0454 , G06N3/0472 , G06N3/084 , G06N3/088 , G06N7/005 , G06N20/00
Abstract: A computational system can include digital circuitry and analog circuitry, for instance a digital processor and a quantum processor. The quantum processor can operate as a sample generator providing samples. Samples can be employed by the digital processing in implementing various machine learning techniques. For example, the computational system can perform unsupervised learning over an input space, for example via a discrete variational auto-encoder, and attempting to maximize the log-likelihood of an observed dataset. Maximizing the log-likelihood of the observed dataset can include generating a hierarchical approximating posterior. Unsupervised learning can include generating samples of a prior distribution using the quantum processor. Generating samples using the quantum processor can include forming chains of qubits and representing discrete variables by chains.
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Patent Agency Ranking
公开(公告)号：US20170351974A1
公开(公告)日：2017-12-07
申请号：US15641030
申请日：2017-07-03
Applicant: D-Wave Systems Inc.
Inventor： Geordie Rose , Suzanne Gildert , William G. Macready , Dominic Christoph Walliman
CPC classification number: G06N20/00 , G06K9/00986 , G06K9/6247 , G06K9/6249 , G06K9/6255 , G06N10/00
Abstract: Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.
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公开(公告)号：US20150269124A1
公开(公告)日：2015-09-24
申请号：US14676605
申请日：2015-04-01
Applicant: D-Wave Systems Inc.
Inventor： Firas Hamze , James King , Evgeny Andriyash , Catherine McGeoch , Jack Raymond , Jason Rolfe , William G. Macready , Aaron Lott , Murray C. Thom
IPC: G06F17/18
CPC classification number: G06F17/18 , G06N99/002 , G06N99/005
Abstract: The systems, devices, articles, and methods generally relate to sampling from an available probability distribution. The samples maybe used to create a desirable probability distribution, for instance for use in computing values used in computational techniques including: Importance Sampling and Markov chain Monte Carlo systems. An analog processor may operate as a sample generator, for example by: programming the analog processor with a configuration of the number of programmable parameters for the analog processor, which corresponds to a probability distribution over qubits of the analog processor, evolving the analog processor, and reading out states for the qubits. The states for the qubits in the plurality of qubits correspond to a sample from the probability distribution. Operation of the sampling device may be summarized as including updating a set of samples to include the sample from the probability distribution, and returning the set of samples.
Abstract translation: 系统，设备，物品和方法通常涉及从可用概率分布中的采样。 样本可以用于创建期望的概率分布，例如用于计算技术中使用的计算值，包括：重要性采样和马尔可夫链蒙特卡洛系统。 模拟处理器可以作为采样发生器操作，例如通过以下方式来对模拟处理器进行编程：模拟处理器的可编程参数数量的配置，其对应于模拟处理器的量子位上的概率分布，演进模拟处理器， 并读出量子位的状态。 多个量子位中的量子位的状态对应于来自概率分布的样本。 采样装置的操作可以被概括为包括更新一组样本以包括来自概率分布的样本，并返回该组样本。
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公开(公告)号：US20150006443A1
公开(公告)日：2015-01-01
申请号：US14316372
申请日：2014-06-26
Applicant: D-Wave Systems Inc.
Inventor： Geordie Rose , Suzanne Gildert , William G. Macready , Dominic Christoph Walliman
CPC classification number: G06N99/005 , G06K9/00986 , G06K9/6247 , G06K9/6249 , G06K9/6255 , G06N99/002
Abstract: Systems, methods and aspects, and embodiments thereof relate to unsupervised or semi-supervised features learning using a quantum processor. To achieve unsupervised or semi-supervised features learning, the quantum processor is programmed to achieve Hierarchal Deep Learning (referred to as HDL) over one or more data sets. Systems and methods search for, parse, and detect maximally repeating patterns in one or more data sets or across data or data sets. Embodiments and aspects regard using sparse coding to detect maximally repeating patterns in or across data. Examples of sparse coding include L0 and L1 sparse coding. Some implementations may involve appending, incorporating or attaching labels to dictionary elements, or constituent elements of one or more dictionaries. There may be a logical association between label and the element labeled such that the process of unsupervised or semi-supervised feature learning spans both the elements and the incorporated, attached or appended label.
Abstract translation: 系统，方法和方面及其实施例涉及使用量子处理器学习的无监督或半监督特征。 为了实现无监督或半监督特征学习，量子处理器被编程为通过一个或多个数据集实现分层深度学习（称为HDL）。 系统和方法搜索，解析和检测一个或多个数据集或数据或数据集中的最大重复模式。 使用稀疏编码来检测数据中或跨数据的最大重复模式的实施例和方面。 稀疏编码的例子包括L0和L1稀疏编码。 一些实现可以涉及将标签附加，附加或附加到字典元素或一个或多个字典的组成元素。 标签和元素之间可能存在逻辑关联，标记为使得无监督或半监督特征学习的过程跨越了元素和合并，附加或附加的标签。
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公开(公告)号：US12198051B2
公开(公告)日：2025-01-14
申请号：US18096198
申请日：2023-01-12
Applicant: D-WAVE SYSTEMS INC.
Inventor： William G. Macready , Jason T. Rolfe
IPC: G06N3/047 , G06F18/214 , G06N3/045 , G06N3/08 , G06N10/00
Abstract: Collaborative filtering systems based on variational autoencoders (VAEs) are provided. VAEs may be trained on row-wise data without necessarily training a paired VAE on column-wise data (or vice-versa), and may optionally be trained via minibatches. The row-wise VAE models the output of the corresponding column-based VAE as a set of parameters and uses these parameters in decoding. In some implementations, a paired VAE is provided which receives column-wise data and models row-wise parameters; each of the paired VAEs may bind their learned column- or row-wise parameters to the output of the corresponding VAE. The paired VAEs may optionally be trained via minibatches. Unobserved data may be explicitly modelled. Methods for performing inference with such VAE-based collaborative filtering systems are also disclosed, as are example applications to search and anomaly detection.
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公开(公告)号：US20230222337A1
公开(公告)日：2023-07-13
申请号：US18096198
申请日：2023-01-12
Applicant: D-WAVE SYSTEMS INC.
Inventor： William G. Macready , Jason T. Rolfe
CPC classification number: G06N3/08 , G06N10/00 , G06N3/045 , G06F18/2148
Abstract: Collaborative filtering systems based on variational autoencoders (VAEs) are provided. VAEs may be trained on row-wise data without necessarily training a paired VAE on column-wise data (or vice-versa), and may optionally be trained via minibatches. The row-wise VAE models the output of the corresponding column-based VAE as a set of parameters and uses these parameters in decoding. In some implementations, a paired VAE is provided which receives column-wise data and models row-wise parameters; each of the paired VAEs may bind their learned column- or row-wise parameters to the output of the corresponding VAE. The paired VAEs may optionally be trained via minibatches. Unobserved data may be explicitly modelled. Methods for performing inference with such VAE-based collaborative filtering systems are also disclosed, as are example applications to search and anomaly detection.
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