公开(公告)号：US20210027379A1

公开(公告)日：2021-01-28

申请号：US16523546

申请日：2019-07-26

申请人： International Business Machines Corporation

发明人： Yada Zhu ,  Giovanni Mariani ,  Kumar Bhaskaran ,  Rong N. Chang

IPC分类号： G06Q40/06 ,  G06N3/04 ,  G06F16/904

摘要： A deep-learning neural network can be trained to model a probability distribution of the asset-price trends for a future time period using a training data set, which can include asset-price trends of a plurality of assets over a past time period and a latent vector sampled from a prior distribution associated with the asset-price trends of a plurality of assets. The training data set can represent a time series data. A portfolio optimization can be executed on the modeled probability distribution to estimate expected risks and returns for different portfolio diversification options.

公开(公告)号：US11210578B2

公开(公告)日：2021-12-28

申请号：US16217760

申请日：2018-12-12

申请人： International Business Machines Corporation

发明人： Florian Michael Scheidegger ,  Roxana Istrate ,  Giovanni Mariani ,  Konstantinos Bekas ,  Adelmo Cristiano Innocenza Malossi

IPC分类号： G06N3/04 ,  G06N3/08

摘要： Determining cognitive models to be deployed at auxiliary devices may include maintaining relations, e.g., in a database. The relations map hardware characteristics of auxiliary devices and example datasets to cognitive models. Cognitive models are determined for auxiliary devices, based on said relations, e.g., for each of the auxiliary devices. An input dataset is accessed, which comprises data of interest, e.g., collected at a core computing system (CCS), and hardware characteristics of each of the auxiliary devices. An auxiliary cognitive model is determined based on a core cognitive model run on the input dataset accessed, wherein the core cognitive model has been trained to learn at least part of said relations. Parameters of the auxiliary model determined can be communicated to said each of the auxiliary devices for the latter to deploy the auxiliary model determined. Method may be implemented in a network having an edge computing architecture.

公开(公告)号：US11386496B2

公开(公告)日：2022-07-12

申请号：US16523546

申请日：2019-07-26

申请人： International Business Machines Corporation

发明人： Yada Zhu ,  Giovanni Mariani ,  Kumar Bhaskaran ,  Rong N. Chang

IPC分类号： G06Q20/00 ,  G06Q40/06 ,  G06F16/904 ,  G06N3/04

摘要： A deep-learning neural network can be trained to model a probability distribution of the asset-price trends for a future time period using a training data set, which can include asset-price trends of a plurality of assets over a past time period and a latent vector sampled from a prior distribution associated with the asset-price trends of a plurality of assets. The training data set can represent a time series data. A portfolio optimization can be executed on the modeled probability distribution to estimate expected risks and returns for different portfolio diversification options.