Abstract: A question answering bot that digests non-relational data tables is provided. A processor receives a question regarding a non-relational data table. A processor extracts at least one feature of the question using a natural language processing (NLP) model. A processor extracts at least one similar feature of the non-relational data table to the extracted at least one feature of the question. A processor determines at least one relevant cell in the non-relational data table based on the at least one feature of the question and the at least one similar feature of the non-relational data table. A processor provides an answer to the question, where the answer is based on the at least one relevant cell.

Abstract: Methods, systems, and computer program products for cooperative neural networks with spatial containment constraints are provided herein. A computer-implemented method includes dividing a processing task into multiple sub-tasks; training multiple independent neural networks, such that at least some of the multiple sub-tasks correspond to different ones of the multiple independent neural networks; defining, via implementing constraint-based domain knowledge related to the processing task in connection with the multiple independent neural networks, a constraint loss for a given one of the multiple sub-tasks, the constraint loss being dependent on output from at least one of the other multiple sub-tasks; and effecting re-training of at least a portion of the multiple independent neural networks by incorporating the constraint loss into at least one of the multiple independent neural networks.

Abstract: One embodiment provides a method, including: receiving two documents, one of the two documents having at least one table that includes the same information as a corresponding table in the other of the two documents, wherein (i) one of the two documents comprises the at least one table in an unstructured table representation and (ii) the other of the two documents comprises the at least one table in a structured table representation; identifying text elements within the at least one table in the unstructured table representation; matching the identified text elements with table elements within the at least one table in the structured table representation; and annotating the at least one table in the structured table representation based upon the matches between the table elements and text elements.

Abstract: A computer-implemented method of automatically generating a machine learning model includes identifying one or more visualization features of a dataset associated with a machine learning model selection process. A plurality of candidate machine learning pipelines are configured to perform respective optimizing strategies in parallel based on the identified visualization features. A machine learning model is automatically generated based on at least one of the generated candidate machine learning pipelines.

Abstract: Embodiments are provided for analysis of errors of a predictive model. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an overview component that causes a client device to present first data identifying an error corresponding to a cell of a confusion matrix for a classification model, the error representing a mismatch between a first label generated by the classification model and a second label corresponding to a ground-truth observation. The computer-executable components also can include an element view component that receives second data defining a root cause of the error. The computer-executable components can further include an error annotation component that can embed the second data into a first data structure containing the first data, resulting in a first annotated data structure.

Abstract: A computer automatically selects a machine learning model pipeline using a meta-learning machine learning model. The computer receives ground truth data and pipeline preference metadata. The computer determines a group of pipelines appropriate for the ground truth data, and each of the pipelines includes an algorithm. The pipelines may include data preprocessing routines. The computer generates hyperparameter sets for the pipelines. The computer applies preprocessing routines to ground truth data to generate a group of preprocessed sets of said ground truth data and ranks hyperparameter set performance for each pipeline to establish a preferred set of hyperparameters for each of pipeline. The computer selects favored data features and applies each of the pipelines, with associated sets of preferred hyperparameters, to score the favored data features of the preprocessed ground truth data. The computer ranks pipeline performance and selects a candidate pipeline according to the ranking.

Abstract: Methods, systems, and computer program products for cooperative neural networks with spatial containment constraints are provided herein. A computer-implemented method includes dividing a processing task into multiple sub-tasks; training multiple independent neural networks, such that at least some of the multiple sub-tasks correspond to different ones of the multiple independent neural networks; defining, based at least in part on constraint-based domain knowledge related to the processing task, at least one constraint loss for a given one of the multiple sub-tasks, the at least one constraint loss being dependent on output from at least one of the other multiple sub-tasks; and re-training at least a portion of the multiple independent neural networks, the re-training being dependent on using the at least one constraint loss.

Abstract: One embodiment provides a method, including: receiving two documents, one of the two documents having at least one table that includes the same information as a corresponding table in the other of the two documents, wherein (i) one of the two documents comprises the at least one table in an unstructured table representation and (ii) the other of the two documents comprises the at least one table in a structured table representation; identifying text elements within the at least one table in the unstructured table representation; matching the identified text elements with table elements within the at least one table in the structured table representation; and annotating the at least one table in the structured table representation based upon the matches between the table elements and text elements.

Abstract: An example operation includes one or more of receiving, by a data processing node, inference data object from a multi-channel data server over a blockchain, sorting, by the data processing node, longitudinal records contained in the inference data object, linking, by the data processing node, transaction outcomes and inferences data from the inference data object to the sorted longitudinal records, and recording linked data onto a blockchain ledger. The data processing node serves as a validator of data from a robo-advisory using natural language (NL) processing to reduce bias and measure effectiveness of inference from the robo-advisory.

Abstract: In a web-page-based system for designing database driven web applications, a page is initiated containing one or more top level iterators. A user introduces fields to the page from a palette including: input, display, hyperlink, iterator. In one case, the user creates iterators nested in a user-selected iterator, and retaining context of the selected iterator, where the system accommodates iterators that are recursive. In an alternative embodiment, the user adds both display and entry fields pertaining to a given user-selected iterator, retaining context of the selected iterator. Responsive to user introduced fields, the system automatically creates representative data structures in a database and automatically relates fields of the pages to the data structures in accordance with a predetermined logic.

Abstract: In a web-page-based system for designing database driven web applications, a page is initiated containing one or more top level iterators. A user introduces fields to the page from a palette including: input, display, hyperlink, iterator. In one case, the user creates iterators nested in a user-selected iterator, and retaining context of the selected iterator, where the system accommodates iterators that are recursive. In an alternative embodiment, the user adds both display and entry fields pertaining to a given user-selected iterator, retaining context of the selected iterator. Responsive to user introduced fields, the system automatically creates representative data structures in a database and automatically relates fields of the pages to the data structures in accordance with a predetermined logic.

Abstract: In a web-page-based system for designing database driven web applications, a page is initiated containing one or more top level iterators. A user introduces fields to the page from a palette including: input, display, hyperlink, iterator. In one case, the user creates iterators nested in a user-selected iterator, and retaining context of the selected iterator, where the system accommodates iterators that are recursive. In an alternative embodiment, the user adds both display and entry fields pertaining to a given user-selected iterator, retaining context of the selected iterator. Responsive to user introduced fields, the system automatically creates representative data structures in a database and automatically relates fields of the pages to the data structures in accordance with a predetermined logic.