A processor-implemented neural network method includes: determining a respective probability density function (PDF) of normalizing a statistical distribution of parameter values, for each channel of each of a plurality of feature maps of a pre-trained neural network; determining, for each channel, a corresponding first quantization range for performing quantization of corresponding parameter values, based on a quantization error and a quantization noise of the respective determined PDF; determining, for each channel, a corresponding second quantization range, based on a signal-to-quantization noise ratio (SQNR) of the respective determined PDF; correcting, for each channel, the corresponding first quantization range based on the corresponding second quantization range; and generating a quantized neural network, based on the corrected first quantization range corresponding for each channel.

An approach is disclosed that inputs data points to a trained artificial intelligence (AI) model with an outlier model that identifies data points on which the AI model has been trained. A value is received from the outlier model corresponding to each of the data points with the received value being a prediction of whether the AI model has been trained on the respective data point. A bias analysis is performed on the trained AI model using a subset of the data points that received a prediction that indicates that the trained AI model was trained with the respective data point.

Methods and systems are provided for facilitating time zone prediction using electronic communication data. Electronic message data associated with a message recipient of electronic communications is obtained. The electronic message data includes message delivery data associated with an electronic message and message response data associated with a response, by the message recipient, to a received electronic message. Using a machine learning model and based on the message delivery data and the message response data, a time-zone score is determined for a time zone. Such a time-zone score can indicate a probability the time zone corresponds with the message recipient. Based on the time-zone score, the time zone is identified as corresponding with the message recipient.

Methods and systems are provided for facilitating time zone prediction using electronic communication data. Electronic message data associated with a message recipient of electronic communications is obtained. The electronic message data includes message delivery data associated with an electronic message and message response data associated with a response, by the message recipient, to a received electronic message. Using a machine learning model and based on the message delivery data and the message response data, a time-zone score is determined for a time zone. Such a time-zone score can indicate a probability the time zone corresponds with the message recipient. Based on the time-zone score, the time zone is identified as corresponding with the message recipient.

System, processes and devices for monitoring betting activities using bet recognition devices and a server. Each bet recognition device has an imaging component for capturing image data for a gaming table surface. The bet recognition device receives calibration data for calibrating the bet recognition device. A server processor coupled to a data store processes the image data received from the bet recognition devices over the network to detect, for each betting area, a number of chips and a final bet value for the chips.

Provided an apparatus that receives time series data from a data storage unit storing time series of sample data or feature values calculated from the sample data, computes a measure indicating change and repetition characteristics of the time series data, based on sample value distribution thereof, selects a state model structure to be used for model learning and estimation, from state models including a fully connected state model and a one way direction state model, based on the measure and stores the selected state model in a model storage unit.

Provided is a method for developing a geographic agnostic machine learning model. The method may include selecting transaction data associated with payment transactions conducted by a first plurality of users, wherein the transaction data includes first transaction data associated with payment transactions conducted by a first plurality of users in a first geographic area and second transaction data associated with payment transactions conducted by a second plurality of users in a second geographic area, formatting the first transaction data associated with payment transactions conducted by the first plurality of users in the first geographic area and the second transaction data associated with payment transactions conducted by the second plurality of users in the second geographic area to provide training data, and generating the geographic agnostic machine learning model using the training data. A system and computer program product are also disclosed.

In at least one embodiment, the present description is directed to a computer system, having at least components of a server, including a processor and a non-transient storage subsystem, storing a computer program including instructions that, when executed by the processor, cause the processor to at least: electronically receive a model for one or more operating conditions, one or more threshold criteria, and facility operating data for each respective facility of a plurality of facilities; validate the one or more threshold criteria to be one or more acceptable bias criteria; iteratively perform one or more iterations of outlier bias reduction in the facility operating data based on the model; determine, based on non-biased facility operating data, a non-biased performance standard for the one or more operating conditions; and track, based on the non-biased performance standard and the facility operating data, operating performance of each respective facility of the plurality of facilities.

A method, system and computer-readable storage medium for performing a cognitive information processing operation. The cognitive information processing operation includes: receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system executing on a hardware processor of an information processing system, the augmented intelligence system and the information processing system providing a cognitive computing function; performing a robustness assessment operation, the robustness assessment operation assessing robustness of the cognitive computing function, the robustness assessment operation generating a robustness score representing robustness of the cognitive computing function; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

Aspects of the present disclosure relate to machine learning techniques for identifying collections of items, such as furniture items, that are visually complementary. These techniques can rely on computer vision and item imagery. For example, a first portion of a machine learning system can be trained to extract aesthetic item qualities or attributes from pixel values of images of the items. A second portion of the machine learning system can learn correlations between these extracted aesthetic qualities and the level of visual coordination between items. Thus, the disclosed techniques use computer vision machine learning to programmatically determine whether items visually coordinate with one another based on pixel values of images of those items.