Abstract: A computer-implemented system and method for estimating a Cumulative Distribution Function (CDF) are provided. The method includes: receive input data representing a volume V of a target space indicating a future target event; compute, using the trained neural network, an estimation of a first flux through a boundary of the volume V; compute, using the trained neural network, an estimation of a second flux through a boundary of a volume W of a base space based on the estimation of the first flux through the boundary of the volume V; generate, using the trained neural network, an estimation of a CDF for the volume V based on the second flux through the boundary of the volume W; compute a probability for the future target event based on the estimated CDF for the volume V; and generate a control command based on the probability for the future target event.

Abstract: The methods and systems are directed to computational approaches for training and using machine learning algorithms to predict the conditional marginal distributions of the position of agents at flexible evaluation horizons and can enables more efficient path planning. These methods model agent movement by training a deep neural network to predict the position of an agent through time. A neural ordinary differential equation (neural ODE) that represents this neural network can be used to determine the log-likelihood of the agent's position as it moves in time.

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: A computer system and method for predicting an output for an input are provided. The system comprises at least one processor and a memory storing instructions which when executed by the processor configure the processor to perform the method. The method comprises at least one of estimating a posterior for a plurality of inputs and associated outputs, or providing a point estimate without sampling. The method also comprises predicting the output for a new observation input.

Abstract: A computer implemented system for generating one or more data structures is described, the one or more data structures representing an unseen composition based on a first category and a second category observed individually in a training data set. During training of a generator, a proposed framework utilizes at least one of the following discriminators—three pixel-centric discriminators, namely, frame discriminator, gradient discriminator, video discriminator; and one object-centric relational discriminator. The three pixel-centric discriminators ensure spatial and temporal consistency across the frames, and the relational discriminator leverages spatio-temporal scene graphs to reason over the object layouts in videos ensuring the right interactions among objects.

Abstract: Systems and methods for unsupervised multi-object scene decomposition that involve a spatio-temporal amortized inference model for multi-object video decomposition. Systems and methods involve a new spatio-temporal iterative inference framework to jointly model complex multi-object representations and the explicit temporal dependencies between the frames. Those dependencies improve overall quality of decomposition, encode information about object dynamics and can be used to predict future trajectories of each object separately. Additionally, the model can generate precise estimations and output data even without color information. The model has scene decomposition, segmentation and future prediction capabilities. The processor can use the model to simulate future frames of the scene 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 computer implemented system for generating one or more data structures is described, the one or more data structures representing an unseen composition based on a first category and a second category observed individually in a training data set. During training of a generator, a proposed framework utilizes at least one of the following discriminators—three pixel-centric discriminators, namely, frame discriminator, gradient discriminator, video discriminator; and one object-centric relational discriminator. The three pixel-centric discriminators ensure spatial and temporal consistency across the frames, and the relational discriminator leverages spatio-temporal scene graphs to reason over the object layouts in videos ensuring the right interactions among objects.