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公开(公告)号：US20220253426A1
公开(公告)日：2022-08-11
申请号：US17170164
申请日：2021-02-08
申请人： International Business Machines Corporation , The Board of Trustees of the University of Illinois
发明人： Yada Zhu , Jinjun Xiong , Jingrui He , Lecheng Zheng , Xiaodong Cui
摘要： Time series data can be received. A machine learning model can be trained using the time series data. A contaminating process can be estimated based on the time series data, the contaminating process including outliers associated with the time series data. A parameter associated with the contaminating process can be determined. Based on the trained machine learning model and the parameter associated with the contaminating process, a single-valued metric can be determined, which represents an impact of the contaminating process on the machine learning model's future prediction. A plurality of different outlier detecting machine learning models can be used to estimate the contaminating process and the single-valued metric can be determined for each of the plurality of different outlier detecting machine learning models. The plurality of different outlier detecting machine learning models can be ranked according to the associated single-valued metric.
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公开(公告)号：US20220121921A1
公开(公告)日：2022-04-21
申请号：US17074054
申请日：2020-10-19
发明人： Pin-Yu Chen , Yada Zhu , Jinjun Xiong , Kumar Bhaskaran , Yunan Ye , Bo Li
IPC分类号： G06N3/08 , G06Q40/06 , G06Q10/10 , G06F40/279
摘要： A processor training a reinforcement learning model can include receiving a first dataset representing an observable state in reinforcement learning to train a machine to perform an action. The processor receives a second dataset. Using the second dataset, the processor trains a machine learning classifier to make a prediction about an entity related to the action. The processor extracts an embedding from the trained machine learning classifier, and augments the observable state with the embedding to create an augmented state. Based on the augmented state, the processor trains a reinforcement learning model to learn a policy for performing the action, the policy including a mapping from state space to action space.
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公开(公告)号：US11301794B2
公开(公告)日：2022-04-12
申请号：US16005373
申请日：2018-06-11
发明人： Yada Zhu , Xuan Liu , Brian Leo Quanz , Ajay Ashok Deshpande , Ali Koc , Lei Cao , Yingjie Li
摘要： A computer implemented method and system of calculating labor resources for a network of nodes in an omnichannel distribution system. Input parameters are received from a computing device of a user. Historical data related to a network of nodes is received, from a data repository. A synthetic scenario is determined based on the received input parameters and the historical data. For each node, key parameters are identified and set based on a multi-objective optimization, wherein the multi-objective optimization includes a synthetic inventory allocation to the node based on the synthetic scenario. A synthetic labor efficiency is determined for the node from the synthetic scenario. Labor resources are calculated based on the synthetic inventory allocation for the synthetic scenario. The labor resources of at least one node are displayed on a user interface of a user device.
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公开(公告)号：US11138552B2
公开(公告)日：2021-10-05
申请号：US15836814
申请日：2017-12-08
发明人： Lei Cao , Ajay Ashok Deshpande , Ali Koc , Yingjie Li , Xuan Liu , Brian Leo Quanz , Yada Zhu
IPC分类号： G06Q10/08
摘要： Techniques for facilitating estimation of node processing capacity values for order fulfillment are provided. In one example, a computer-implemented method can comprise: generating, by a system operatively coupled to a processor, a current processing capacity value for an entity; and determining, by the system, a future processing capacity value for the entity based on the current processing capacity value and by using a future capacity model that has been explicitly trained to infer respective processing capacity values for the entity. The computer-implemented method can also comprise fulfilling an order of an item, by the system, based on the future processing capacity value.
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公开(公告)号：US20190317728A1
公开(公告)日：2019-10-17
申请号：US15954891
申请日：2018-04-17
发明人： Pin-Yu Chen , Lingfei Wu , Chia-Yu Chen , Yada Zhu
摘要： Techniques that facilitate graph similarity analytics are provided. In one example, a system includes an information component and a similarity component. The information component generates a first information index indicative of a first entropy measure for a first graph-structured dataset associated with a machine learning system. The information component also generates a second information index indicative of a second entropy measure for a second graph-structured dataset associated with the machine learning system. The similarity component determines similarity between the first graph-structured dataset and the second graph-structured dataset based on a graph similarity computation associated with the first information index and the second information index.
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公开(公告)号：US09262255B2
公开(公告)日：2016-02-16
申请号：US13830700
申请日：2013-03-14
发明人： Arun Hampapur , Hongfei Li , Zhiguo Li , Yada Zhu
CPC分类号： G06F11/079 , G06F11/0709 , G06F11/0751 , G06F11/0787 , G06N7/005 , G06N99/005
摘要： A hierarchical multi-stage model of asset failure risk for complex heterogeneously distributed physical assets is built. The hierarchical multi-stage model considers heterogeneity of failure patterns for the assets. At least one data stream is analyzed to determine whether the hierarchical multi-stage model needs to be updated due to a change in the failure patterns. If the analysis indicates that the hierarchical multi-stage model needs to be updated, the hierarchical multi-stage model is dynamically updated to obtain an updated hierarchical multi-stage model.
摘要翻译： 建立了复杂异质分布实物资产资产失效风险的分层多阶段模型。 分层多阶段模型考察资产失效模式的异质性。 分析至少一个数据流以确定由于故障模式的变化而需要更新分级多级模型。 如果分析表明需要更新分级多阶段模型，则动态更新分级多阶段模型以获得更新的分级多阶段模型。
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公开(公告)号：US20220383185A1
公开(公告)日：2022-12-01
申请号：US17334889
申请日：2021-05-31
发明人： Yada Zhu , Wei Zhang , Guangnan Ye , Xiaodong Cui
摘要： Hessian matrix-free sample-based techniques for model explanations that are faithful to the model are provided. In one aspect, a method for explaining a machine learning model {circumflex over (θ)} (e.g., for natural language processing) is provided. The method includes: training the machine learning model {circumflex over (θ)} with training data D; obtaining a decision of the machine learning model {circumflex over (θ)}; and explaining the decision of the machine learning model {circumflex over (θ)} using training examples from the training data D.
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公开(公告)号：US11315066B2
公开(公告)日：2022-04-26
申请号：US16740214
申请日：2020-01-10
发明人： Ajay Ashok Deshpande , Ali Koc , Brian Leo Quanz , Jae-Eun Park , Yada Zhu , Yingjie Li , Christopher Scott Milite , Xuan Liu , Chandrasekhar Narayanaswami
摘要： Embodiments herein describe a return network simulation system that can simulate changes in a retailer's return network to determine the impact of those changes. Advantageously, being able to accurately simulate the retailer's return network means changes can be evaluated without first making those adjustments in the physical return network. Doing so avoids the cost of implementing the changes on the return network without first being able to predict whether the changes will have a net positive result (e.g., a positive result that offsets any negative results). A retailer can first simulate the change on the return network, review how the change affects one or more KPIs, and then decide whether to implement the change in the actual return network. As a result, the retailer has a reliable indicator whether the changes will result in a desired effect.
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公开(公告)号：US11301791B2
公开(公告)日：2022-04-12
申请号：US16005166
申请日：2018-06-11
发明人： Yada Zhu , Xuan Liu , Brian Leo Quanz , Ajay Ashok Deshpande , Ali Koc , Lei Cao , Yingjie Li
IPC分类号： G06Q10/06
摘要： A computer implemented method and system of setting values of parameters of nodes in an omnichannel distribution system, the method comprising is provided. Input parameters are received from a computing device. Historical data related to the network of nodes is received from a data repository. A synthetic scenario is determined based on the received input parameters and the historical data. Each node is clustered into a corresponding category. For each category of nodes, key parameters are identified. A range of each key parameter is determined based on the synthetic scenario. A number of simulations N to perform with data sampled from the synthetic scenario within the determined range of each key parameter is determined. For each of the N simulations, a multi-objective optimization is performed to determine a cost factor of the parameter settings. The parameter settings with a lowest cost factor are selected.
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公开(公告)号：US09569298B2
公开(公告)日：2017-02-14
申请号：US15043532
申请日：2016-02-13
发明人： Arun Hampapur , Hongfei Li , Zhiguo Li , Yada Zhu
CPC分类号： G06F11/079 , G06F11/0709 , G06F11/0751 , G06F11/0787 , G06N7/005 , G06N99/005
摘要： A hierarchical multi-stage model of asset failure risk for complex heterogeneously distributed physical assets is built. The hierarchical multi-stage model considers heterogeneity of failure patterns for the assets. At least one data stream is analyzed to determine whether the hierarchical multi-stage model needs to be updated due to a change in the failure patterns. If the analysis indicates that the hierarchical multi-stage model needs to be updated, the hierarchical multi-stage model is dynamically updated to obtain an updated hierarchical multi-stage model.
摘要翻译： 建立了复杂异质分布实物资产资产失效风险的分层多阶段模型。 分层多阶段模型考察资产失效模式的异质性。 分析至少一个数据流以确定由于故障模式的变化而需要更新分级多级模型。 如果分析表明需要更新分级多阶段模型，则动态更新分级多阶段模型以获得更新的分级多阶段模型。
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