Aspects if the disclosure are directed towards multi-step forecasting via temporal aggregation. An example embodiment includes a method the includes receiving a time series including a first time step value and a second time step value. The method can further include generating a temporally aggregated time series by summing the first time step value and the second time step value to create a third time step value. The method can further include calculating a first set of input values and a second set of input values from the temporally aggregated time series. The method can further include forecasting a fourth time step value using the first set of input values and the second set of input values, and a fifth time step using the second set of input values from the temporally aggregated time series.

Generally, the present disclosure is directed to user interface understanding. More particularly, the present disclosure relates to training and utilization of machine-learned models for user interface prediction and/or generation. A machine-learned interface prediction model can be pre-trained using a variety of pre-training tasks for eventual downstream task training and utilization (e.g., interface prediction, interface generation, etc.).

LK-SURF, Robust Kalman Filter, HAR-SLAM, and Landmark Promotion SLAM methods are disclosed. LK-SURF is an image processing technique that combines Lucas-Kanade feature tracking with Speeded-Up Robust Features to perform spatial and temporal tracking using stereo images to produce 3D features can be tracked and identified. The Robust Kalman Filter is an extension of the Kalman Filter algorithm that improves the ability to remove erroneous observations using Principal Component Analysis and the X84 outlier rejection rule. Hierarchical Active Ripple SLAM is a new SLAM architecture that breaks the traditional state space of SLAM into a chain of smaller state spaces, allowing multiple tracked objects, multiple sensors, and multiple updates to occur in linear time with linear storage with respect to the number of tracked objects, landmarks, and estimated object locations. In Landmark Promotion SLAM, only reliable mapped landmarks are promoted through various layers of SLAM to generate larger maps.

Systems, methods, and computer program products for associating a media content clip(s) with other media content clip(s) having a different modality by determining first embedding vectors of media content items of a first modality, receiving a media content clip of a second modality, determining a second embedding vector of the media content clip of the second modality, ranking the first embedding vectors based on a distance between the embedding vectors and the second embedding vector, and selecting one or more of the media content items of the first modality based on the ranking, thereby pairing media content clips based on emotion.

A system is described which accepts corporate title and employee data associated with that corporate title data at a first company, putting the corporate title and employee data through a configured network and generating a vector of terms and a set of coefficients associated with that title. Information about an employee is put through a second network using those terms and coefficients to determine if the employee would have the same or similar title at the first company.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network. One of the methods includes processing each training input using the neural network and in accordance with the current values of the network parameters to generate a network output for the training input; computing a respective loss for each of the training inputs by evaluating a loss function; identifying, from a plurality of possible perturbations, a maximally non-linear perturbation; and determining an update to the current values of the parameters of the neural network by performing an iteration of a neural network training procedure to decrease the respective losses for the training inputs and to decrease the non-linearity of the loss function for the identified maximally non-linear perturbation.

Cameras capture time-stamped images of predefined areas. The images are processed to make decisions as to when a person depicted in the images takes possession of or is disposed of an item depicted in the images. Possessed items are added to a shopping cart maintained for the person and dispossessed items are removed from the shopping cart.

Systems, methods, and non-transitory computer-readable media are disclosed for extracting material properties from a single digital image portraying one or more materials by utilizing a neural network encoder, a neural network material classifier, and one or more neural network material property decoders. In particular, in one or more embodiments, the disclosed systems and methods train the neural network encoder, the neural network material classifier, and one or more neural network material property decoders to accurately extract material properties from a single digital image portraying one or more materials. Furthermore, in one or more embodiments, the disclosed systems and methods train and utilize a rendering layer to generate model images from the extracted material properties.

A processor-implemented method of implementing an attention mechanism in a neural network includes obtaining key-value coupling data determined based on an operation between new key data determined using a first nonlinear transformation for key data of an attention layer, and value data of the attention layer corresponding to the key data; determining new query data by applying a second nonlinear transformation to query data corresponding to input data of the attention layer; and determining output data of the attention layer based on an operation between the new query data and the key-value coupling data.

Analysis of genetic disease progression may be provided. Data about a set of molecular status may be received. A dynamic prediction model of molecular interactions may be provided over time. The molecular statuses of the set over time may be determined using the dynamic prediction model. The determined molecular statuses may be clustered by applying an interaction-aware metric for the analysis of the genetic disease progression.