Abstract: A system uses data captured by vehicle-mounted sensors to generate a view of a ground surface. The system does this by receiving digital image frames and associating a location and pose of the vehicle that captured the image with each digital image frame. The system will access a three dimensional (3D) ground surface estimation model of the ground surface, select a region of interest (ROI) of the ground surface, and select a vehicle pose. The system will identify digital image frames that are associated with the pose and also with a location that corresponds to the ROI. The system will generate a visual representation of the ground surface in the ROI by projecting ground data for the ROI from the ground surface estimation model to normalized 2D images that are created from the digital image frames. The system will save the visual representation to a two-dimensional grid.

Abstract: A system uses data captured by vehicle-mounted sensors to generate a view of a ground surface. The system does this by receiving digital image frames and associating a location and pose of the vehicle that captured the image with each digital image frame. The system will access a three dimensional (3D) ground surface estimation model of the ground surface, select a region of interest (ROI) of the ground surface, and select a vehicle pose. The system will identify digital image frames that are associated with the pose and also with a location that corresponds to the ROI. The system will generate a visual representation of the ground surface in the ROI by projecting ground data for the ROI from the ground surface estimation model to normalized 2D images that are created from the digital image frames. The system will save the visual representation to a two-dimensional grid.

Abstract: A system comprising at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to: receive, as input, a plurality of electronic documents, apply a trained machine learning classifier to automatically classify at least some of said plurality of electronic documents, wherein said machine learning classifier comprises two or more attention layers, and wherein at least one of the attention layers comprises an adjustable parameter which controls a distribution of attention weights assigned by said attention layer.

Abstract: A method comprising receiving a dictionary comprising a plurality of entities, wherein each entity has a length of between 1 and n tokens; constructing a probabilistic data representation model comprising n Bloom filter (BF) pairs indexed from 1 to n; populating said probabilistic data representation model with a data representation of said entities, wherein, with respect to each BF pair indexed i: (i) a first BF is populated with the first i tokens of all said entities having at least i+1 tokens, and (ii) a second BF in populated with all said entities having exactly i tokens; receiving a text corpus, wherein said text corpus is segmented into tokens; and automatically matching each token in said text corpus against said populated probabilistic data representation model, wherein said matching comprises sequentially querying each said BF pair in the order of said indexing, to determine a match.

Abstract: A system comprising at least one hardware processor; and a non-transitory computer-readable storage medium having stored thereon program instructions, the program instructions executable by the at least one hardware processor to: receive, as input, a plurality of electronic documents, apply a trained machine learning classifier to automatically classify at least some of said plurality of electronic documents, wherein said machine learning classifier comprises two or more attention layers, and wherein at least one of the attention layers comprises an adjustable parameter which controls a distribution of attention weights assigned by said attention layer.

Abstract: A method comprising receiving a dictionary comprising a plurality of entities, wherein each entity has a length of between 1 and n tokens; constructing a probabilistic data representation model comprising n Bloom filter (BF) pairs indexed from 1 to n; populating said probabilistic data representation model with a data representation of said entities, wherein, with respect to each BF pair indexed i: (i) a first BF is populated with the first i tokens of all said entities having at least i+1 tokens, and (ii) a second BF in populated with all said entities having exactly i tokens; receiving a text corpus, wherein said text corpus is segmented into tokens; and automatically matching each token in said text corpus against said populated probabilistic data representation model, wherein said matching comprises sequentially querying each said BF pair in the order of said indexing, to determine a match.

Abstract: A method comprising operating at least one hardware processor for: receiving, as input, a plurality of electronic documents, training a machine learning classifier based, at least on part, on a training set comprising: (i) labels associated with the electronic documents, (ii) raw text from each of said plurality of electronic documents, and (iii) a rasterized version of each of said plurality of electronic documents, and applying said machine learning classifier to classify one or more new electronic documents.

Abstract: A method comprising receiving a dictionary comprising a plurality of entities, wherein each entity has a length of between 1 and n tokens; constructing a probabilistic data representation model comprising n Bloom filter (BF) pairs indexed from 1 to n; populating said probabilistic data representation model with a data representation of said entities, wherein, with respect to each BF pair indexed i: (i) a first BF is populated with the first i tokens of all said entities having at least i+1 tokens, and (ii) a second BF in populated with all said entities having exactly i tokens; receiving a text corpus, wherein said text corpus is segmented into tokens; and automatically matching each token in said text corpus against said populated probabilistic data representation model, wherein said matching comprises sequentially querying each said BF pair in the order of said indexing, to determine a match.

Abstract: A method comprising operating at least one hardware processor for: receiving, as input, a plurality of electronic documents, training a machine learning classifier based, at least on part, on a training set comprising: (i) labels associated with the electronic documents, (ii) raw text from each of said plurality of electronic documents, and (iii) a rasterized version of each of said plurality of electronic documents, and applying said machine learning classifier to classify one or more new electronic documents.