公开(公告)号：US11816550B1

公开(公告)日：2023-11-14

申请号：US16933215

申请日：2020-07-20

Applicant: Amazon Technologies, Inc.

Inventor： Deepak Gupta ,  Anirban Majumder ,  Prateek Sircar ,  Rajeev Ramnarain Rastogi

IPC: G06N20/20 ,  G06N5/04 ,  G06N5/01

CPC classification number: G06N20/20 ,  G06N5/01 ,  G06N5/04

Abstract: Devices and techniques are generally described for generating confidence scores for boosting-based tree machine learning models. In various examples, a first record comprising a plurality of input variables may be received. In another example, a boosting-based tree machine learning model may generate, for the first record, a base model score. In various examples, the base model score may be generated based on the plurality of input variables and the base model score may represent a likelihood that the first record is associated with a first class. In some examples, a score confidence machine learning model may generate a confidence score for the first record. The confidence score may indicate a confidence in the base model score. In various examples, the first record may be processed based at least in part on the confidence score.

公开(公告)号：US20200065710A1

公开(公告)日：2020-02-27

申请号：US16672243

申请日：2019-11-01

Applicant: Amazon Technologies, Inc.

Inventor： Gowda Dayannda Anjaneyapura Range ,  Rajeev Ramnarain Rastogi

IPC: G06N20/00 ,  G06F16/35

Abstract: Respective correlation metrics between token groups of a particular text attribute of a data set and a prediction target attribute are computed. Based on the correlation metrics, a predictive token group list is created. For various observation records of the data set, values of a derived categorical attribute corresponding to the particular text attribute are determined based on matches between the particular text attribute value and the predictive token group list. A measure of the predictive utility of the particular text attribute is obtained using correlations between the categorical attribute and the prediction target attribute.

公开(公告)号：US10380236B1

公开(公告)日：2019-08-13

申请号：US15712933

申请日：2017-09-22

Applicant: Amazon Technologies, Inc.

Inventor： Hrishikesh Vidyadhar Ganu ,  Rajeev Ramnarain Rastogi ,  Subhajit Sanyal

IPC: G06F17/24 ,  G06N3/02 ,  G06F17/27 ,  G06F17/22

Abstract: Systems and methods are disclosed to implement a machine learning system that is trained to assign annotations to text fragments in an unstructured sequence of text. The system employs a neural model that includes an encoder recurrent neural network (RNN) and a decoder RNN. The input text sequence is encoded by the encoder RNN into successive encoder hidden states. The encoder hidden states are then decoded by the decoder RNN to produce a sequence of annotations for text fragments within the text sequence. In embodiments, the system employs a fixed-attention window during the decoding phase to focus on a subset of encoder hidden states to generate the annotations. In embodiments, the system employs a beam search technique to track a set of candidate annotation sequences before the annotations are outputted. By using a decoder RNN, the neural model is better equipped to capture long-range annotation dependencies in the text sequence.