Aspects of the present disclosure relate generally to systems and methods for use in the implementation and/or operation of quantum information processing (QIP) systems, and more particularly, to the computation of joint probability distributions with quantum computers. Improvements in the computation of joint probability distributions are described by designing quantum machine learning algorithms to model copulas. Moreover, any copula is shown to be naturally mapped to a multipartite maximally entangled state. A variational ansatz referred to herein as a “qopula” creates arbitrary correlations between variables while maintaining the copula structure starting from a set of Bell pairs for two variables, or Greenberger-Horne-Zeilinger (GHZ) states for multiple variables. Generative learning algorithms may be demonstrated on quantum computers, and more particularly, in trapped-ion quantum computers. The approach described herein is shown to have advantages over classical models.

Methods and systems are provided for detecting and responding to dangerous road users. In some aspects, a process can include steps for receiving sensor data of a detected object from an autonomous vehicle, determining whether the detected object is exhibiting a dangerous attribute, generating output data based on the determining of whether the detected object is exhibiting the dangerous attribute, and updating a machine learning model based on the output data relating to the dangerous attribute.

Methods and systems are provided for detecting and responding to dangerous road users. In some aspects, a process can include steps for receiving sensor data of a detected object from an autonomous vehicle, determining whether the detected object is exhibiting a dangerous attribute, generating output data based on the determining of whether the detected object is exhibiting the dangerous attribute, and updating a machine learning model based on the output data relating to the dangerous attribute.

A method, computer program, and computer system is provided for identifying a speaker in at text based work. Labeled and unlabeled instances corresponding to one or more speakers are extracted. Pseudo-labels are inferred for the extracted unlabeled instances based on the labeled instances. One or more of the unlabeled instances are labeled based on the inferred pseudo-labels.

A method, computer program, and computer system is provided for identifying a speaker in at text based work. Labeled and unlabeled instances corresponding to one or more speakers are extracted. Pseudo-labels are inferred for the extracted unlabeled instances based on the labeled instances. One or more of the unlabeled instances are labeled based on the inferred pseudo-labels.

In an approach to jointly learning uncertainty-aware trend-informed neural network for a demand forecasting model, a machine learning model is trained to capture uncertainty in input forecasts. The uncertainty in a latent space is represented using an auto-encoder based neural architecture. The uncertainty-aware latent space is modeled and optimized to generate an embedding space. A time-series regressor model is learned from the embedding space. A machine learning model is trained for trend-aware demand forecasting based on said time-series regressor model.

In an approach to jointly learning uncertainty-aware trend-informed neural network for a demand forecasting model, a machine learning model is trained to capture uncertainty in input forecasts. The uncertainty in a latent space is represented using an auto-encoder based neural architecture. The uncertainty-aware latent space is modeled and optimized to generate an embedding space. A time-series regressor model is learned from the embedding space. A machine learning model is trained for trend-aware demand forecasting based on said time-series regressor model.

Systems as described herein determine merchant enforced transaction rules. A determination server may receive transaction data associated with a plurality of merchants. The determination server may generate a histogram of payments associated with a merchant category and filter out transaction data having purchase amounts above or below a predetermined threshold. The determination server may determine a first average purchase amount associated with merchants in the merchant category and a second average purchase amount associated with each merchant in the merchant category. The determination server may determine user spending patterns and that a first merchant in the merchant category enforces one or more card-based transaction rules using machine learning models. After determining that a user device is proximately located to the first merchant, a notification indicating the one or more card-based transaction rules associated with the first merchant may be sent to the user device.

Systems as described herein determine merchant enforced transaction rules. A determination server may receive transaction data associated with a plurality of merchants. The determination server may generate a histogram of payments associated with a merchant category and filter out transaction data having purchase amounts above or below a predetermined threshold. The determination server may determine a first average purchase amount associated with merchants in the merchant category and a second average purchase amount associated with each merchant in the merchant category. The determination server may determine user spending patterns and that a first merchant in the merchant category enforces one or more card-based transaction rules using machine learning models. After determining that a user device is proximately located to the first merchant, a notification indicating the one or more card-based transaction rules associated with the first merchant may be sent to the user device.

Group-level privacy preservation is implemented within federated machine learning. An aggregation server may distribute a machine learning model to multiple users each including respective private datasets. The private datasets may individually include multiple items associated with a single group. Individual users may train the model using their local, private dataset to generate one or more parameter updates and to determine a count of the largest number of items associated with any single group of a number of groups in the dataset. Parameter updates generated by the individual users may be modified by applying respective noise values to individual ones of the parameter updates according to the respective counts to ensure differential privacy for the groups of the dataset. The aggregation server may aggregate the updates into a single set of parameter updates to update the machine learning model.