Nasdaq, Inc.

Massachusetts Institute of Technology

Hyunsoo Jeong

Douglas Hamilton

Tudor Morosan

Xuyang Lin

Anand Dwivedi

Leonid Rosenfeld

Jonathan Rivers

Shihui Chen

Michael O'Rourke

William Dague

Josep Puig Ruiz

Yongheng Zhang

Yu-Hsuan Jou

Niharika Sharma

A computer system is provided and programmed to assemble a plurality of synthetic datasets and blend those synthetic datasets into a synthesized dataset. An evaluation is then performed to determine whether an existing model should be associated with the synthesized dataset or a new model should be trained from an existing model using the synthesized dataset.

Systems and methods for generating datasets for model retraining

A computer system is provided that is designed to handle multi-label classification. The computer system includes multiple processing instances that are arranged in a hierarchal manner and execute differently trained classification models. The classification task of one processing instance and the executed model therein may rely on the results of classification performed by another processing instance. Each of the models may be associated with a different threshold value that is used to binarize the probability output from the classification model.

Modular machine learning systems and methods

A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

System and methods for data model detection and surveillance

Systems and methods that provide improved (e.g., higher accuracy and time-efficient) aggregation of report data are described. Example embodiments effectively combine machine learning and deep learning in a processing pipeline to classify unlabeled source data to determine a reliable classification, or a labeling, of that source data. The classified or labeled source data can be used to generate reports that provide extensive insight into the aggregated source data.

Natural language inferencing and deep learning-based grouping of unlabeled text

A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

Systems and methods of generating datasets from heterogeneous sources for machine learning

Natural language processing techniques provide sentence level analysis on one or more topics that are associated with keywords. Indirect learning is used to expand the understanding of the keywords and associated topics. Semantic similarity is used on a sentence-level to assess whether a given sentence relates or mentions a particular topic. In some examples, additional keywords are suggested using filtering techniques in connection with graph embedding-based entity linking techniques.

Systems and methods of natural language processing

A CNC machine shaping tool is efficiently used by monitoring human audible sound during shaping. A sound information set is created for a tool shaping a workpiece. Shaping sounds are recorded and sliced into short term units. A human operator assigns tool condition labels to each slice. Short term units are combined into mid term units. Noise is reduced by profiling. Mid term sound related features of time and frequency domains are extracted. Dimensionality is reduced by robust principal component analysis. The principal component set is balanced, e.g. by SMOTE. A classifier and principal components are selected. An information set of patterns of values of selected principal components for the tool is created. In an industrial setting, shaping sounds are recorded, noise reduced and select principal component vector values are compared to the tool condition labeled patterns of values in the information set to identify tool condition before deterioration.

Method and apparatus for efficient use of CNC machine shaping tool including cessation of use no later than the onset of tool deterioration by monitoring audible sound during shaping

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Boston, MA, United States of America

Hyunsoo Jeong