Abstract: A computer-implemented system and method for training a neural network with enforced monotonicity are disclosed. An example system includes at least one processor and memory in communication with said at least one processor, wherein the memory stores instructions for providing a data model representing a neural network for predicting an outcome based on input data, the instructions when executed at said at least one processor causes said system to: receive a feature data as input data; predict an outcome based on the input data using the neural network; compute a loss function based on the predicted outcome and an expected outcome associated with the input data, the loss function being dependent on a monotonicity penalty ? computed based on a set of training data including the feature data and on a set of random data; and update weights of the neural network based on the loss function.

Abstract: A system for machine learning architecture for time series data prediction. The system may be configured to: maintain a data set representing a neural network having a plurality of weights; obtain time series data associated with a data query; generate, using the neural network and based on the time series data, a predicted value based on a sampled realization of the time series data and a normalizing flow model, the normalizing flow model based on a latent continuous-time stochastic process having a stationary marginal distribution and bounded variance; and generate a signal providing an indication of the predicted value associated with the data query.

Abstract: Methods are provided for automatically analyzing and understanding activities and interactions. One method comprises receiving at least location information for one or more individual objects in a scene at a given time; applying at least one machine learning or artificial intelligence technique to automatically learn an informative representation of location trajectory data for each object; and identifying and analyzing individual and group activities in the scene based on the trajectory data.

Abstract: Systems and methods for machine learning architecture for time series data prediction. The system may include a processor and a memory storing processor-executable instructions. The processor-executable instructions, when executed, may configure the processor to: obtain time series data associated with a data query; generate a predicted value based on a sampled realization of the time series data and a continuous time generative model, the continuous time generative model trained to define an invertible mapping to maximize a log-likelihood of a set of predicted values for a time range associated with the time series data; and generate a signal providing an indication of the predicted value associated with the data query.

Abstract: A system and method are provided for object tracking in a sequence of images of a scene acquired by an imaging device. The method includes generating a representation of an object and its surroundings; generating a tracking score based on the representation of the object and its surroundings, and a classification scheme; detecting a peak in the tracking score corresponding to a location of the object in the image; and adjusting the representation of the object and its surroundings and the classification scheme according to one or more new appearances of the object.

Abstract: Methods are provided for automatically analyzing and understanding activities and interactions. One method comprises receiving at least location information for one or more individual objects in a scene at a given time; applying at least one machine learning or artificial intelligence technique to automatically learn an informative representation of location trajectory data for each object; and identifying and analyzing individual and group activities in the scene based on the trajectory data.

Abstract: A system and method are provided for object tracking in a sequence of images of a scene acquired by an imaging device. The method includes generating a representation of an object and its surroundings; generating a tracking score based on the representation of the object and its surroundings, and a classification scheme; detecting a peak in the tracking score corresponding to a location of the object in the image; and adjusting the representation of the object and its surroundings and the classification scheme according to one or more new appearances of the object.