A system for time series analysis using attention models is disclosed. The system may capture dependencies across different variables through input embedding and may map the order of a sample appearance to a randomized lookup table via positional encoding. The system may capture capturing dependencies within a single sequence through a self-attention mechanism and determine a range of dependency to consider for each position being analyzed. The system may obtain an attention weighting to other positions in the sequence through computation of an inner product and utilize the attention weighting to acquire a vector representation for a position and mask the sequence to enable causality. The system may employ a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation and a linear layer to obtain logits from the single vector representation. The system may use a type dependent final prediction layer.

A computer implemented method of generating a gradient boosting decision tree for obtaining predictions includes finding split points by sorting variable values of a feature by their gradient during training of the gradient boosting decision tree, performing a linear search to find a subset of variables with maximum split gain, and modifying a node of the gradient boosting decision tree to have multiple split points on the node for a feature as a function of the linear search. In a further example, a computer implemented method of controlling overfitting in a gradient boosting decision tree includes combining values of low population feature values into a virtual bin, fanning out the virtual bin into feature values having a low population, and including the low population feature values into multiple split points on a node of the gradient boosting decision tree.

A computer implemented method of generating a gradient boosting decision tree for obtaining predictions includes finding split points by sorting variable values of a feature by their gradient during training of the gradient boosting decision tree, performing a linear search to find a subset of variables with maximum split gain, and modifying a node of the gradient boosting decision tree to have multiple split points on the node for a feature as a function of the linear search. In a further example, a computer implemented method of controlling overfitting in a gradient boosting decision tree includes combining values of low population feature values into a virtual bin, fanning out the virtual bin into feature values having a low population, and including the low population feature values into multiple split points on a node of the gradient boosting decision tree.

A method for three-dimensional modeling. The method may include: acquiring coordinate points of obstacles in a surrounding environment of an autonomous driving vehicle in a vehicle coordinate system; determining a position of eyes of a passenger in the autonomous driving vehicle, and establishing an eye coordinate system using the position of the eyes as a coordinate origin; converting the coordinate points of the obstacles in the vehicle coordinate system to coordinate points in the eye coordinate system, and determining a visualization distance between the obstacles in the surrounding environment based on an observation angle of the eyes; and performing three-dimensional modeling of the surrounding environment, based on visualization distance between the coordinate points of the obstacles in the eye coordinate system and the obstacles.

A method for three-dimensional modeling. The method may include: acquiring coordinate points of obstacles in a surrounding environment of an autonomous driving vehicle in a vehicle coordinate system; determining a position of eyes of a passenger in the autonomous driving vehicle, and establishing an eye coordinate system using the position of the eyes as a coordinate origin; converting the coordinate points of the obstacles in the vehicle coordinate system to coordinate points in the eye coordinate system, and determining a visualization distance between the obstacles in the surrounding environment based on an observation angle of the eyes; and performing three-dimensional modeling of the surrounding environment, based on visualization distance between the coordinate points of the obstacles in the eye coordinate system and the obstacles.

Systems and methods include determination, for each of a plurality of discrete features, of statistics based on a number of occurrences of each discrete value of the discrete feature in the data, determination of first summary statistics based on the determined statistics, determine of a dissimilarity for each discrete feature based on the first summary statistics and on the statistics determined for the discrete feature, determination of candidate discrete features based on the determined dissimilarities, determination, for each of the candidate discrete features, of second summary statistics based on values of a continuous feature associated with each discrete value of the candidate discrete feature, determination of a deviation score for each of the candidate discrete features based on the second summary statistics, and transmission of the candidate discrete features for display in association with the continuous feature based on the determined deviation scores.

A device includes image generation circuitry and convolutional-neural-network circuitry. The image generation circuitry, in operation, generates a digital image representation of a wafer defect map (WDM). The convolutional-neural-network circuitry, in operation, generates a defect classification associated with the WDM based on: the digital image representation of the WDM and a data-driven model associating WDM images with classes of a defined set of classes of wafer defects and generated using a training data set augmented based on defect pattern orientation types associated with training images.

A system can include a computer including a processor and a memory, the memory storing instructions executable by the processor to receive point cloud data. The instructions further include instructions to generate a plurality of feature maps based on the point cloud data, each feature map of the plurality of feature maps corresponding to a parameter of the point cloud data. The instructions further include instructions to aggregate the plurality of feature maps into an aggregated feature map. The instructions further include instructions to generate, via a feedforward neural network, at least one of a segmentation output or a classification output based on the aggregated feature map.

A system can include a computer including a processor and a memory, the memory storing instructions executable by the processor to receive point cloud data. The instructions further include instructions to generate a plurality of feature maps based on the point cloud data, each feature map of the plurality of feature maps corresponding to a parameter of the point cloud data. The instructions further include instructions to aggregate the plurality of feature maps into an aggregated feature map. The instructions further include instructions to generate, via a feedforward neural network, at least one of a segmentation output or a classification output based on the aggregated feature map.

There is provided a method and apparatus for generating a competition commentary based on artificial intelligence, and a storage medium. The method comprises: obtaining commentator's words commentaries and structured data of historical competitions; generating a commentating model according to obtained information; during live broadcast of a competition, determining a corresponding words commentary according to the commentating model with respect to the structured data obtained each time.