Landing Ai

Yu Qing Zhou

Andrew Yan-Tak Ng

Pingyang He

Dongyan Wang

Gopi Prashanth Gopal

Kai Yang

Yiwen Rong

Mark William Sabini

Daniel Bibireata

Sanjeev Satheesh

Dillon Anthony Laird

Alejandro Betancourt

Rustam Hashimov

Karthikeyan Thiruppathisamy Nathillvar

Yan Liu

Francisco Matias Cuenca-Acuna

Dillon Laird

Ankur Rawat

Shankaranand Jagadeesan

Sebastian Rodriguez

Yuxiang Zhang

Quinn Killough

Leela Seshu Reddy Cheedepudi

Camilo Iral

Zeqi Qiu

Mingrui Zhang

Junjie Guan

Aldrin Leal

Ramesh Sampath

Camilo Zapata

Juan Camilo Fernancez

Cristobal Silva

Sanjay Bodhu

Whit Blodgett

A system or method for identifying mislabels in a training dataset. The labeled dataset includes multiple labeled samples, each of which is labeled with a ground-truth label. The labeled samples are divided into multiple training subset and hold-out test subset. For each training subset and corresponding hold-out test subset, a machine learning model is trained using the training subset, and the trained machine learning model is applied to the hold-out test subset to generate prediction labels for samples in the corresponding hold-out test subset. Each prediction label has a confidence score indicating a likelihood of the prediction label is correct. A pair of prediction label and ground truth label corresponding to a same sample is compared to determine whether there is a candidate mislabel. The system determines whether the candidate mislabel is a mislabel based in part on a confidence score of the prediction label.

Data centric mislabel detection

A model management system adaptively refines a training dataset for more effective visual inspection. The system trains a machine learning model using the initial training dataset and sends the trained model to a client for deployment. The deployment process generates outputs that are sent back to the system. The system determines that performance of predictions for noisy data points are inadequate and determines a cause of failure based on a mapping of the noisy data point to a distribution generated for the training dataset across multiple dimensions. The system determines a cause of failure based on an attribute of the noisy datapoint that deviates from the distribution of the training dataset and performs refinement towards the training dataset based on the identified cause of failure. The system retrains the machine learning model with the refined training dataset and sends the retrained machine learning model back to the client for re-deployment.

Model management system for improving training data through machine learning deployment

A process and a system for creating a visual guide for developing training data for a classification of image, where the training data includes images tagged with labels for the classification of the images. A processor may prompt a user to define a framework for the classification. For an initial set of images within the training data, qualified human classifiers are prompted to locate the images within the framework and to tag the images with labels. The processor determines whether the tagged images have consistent labels, and, if so, the processor adds images to the training data. The processor may add the images by providing a visual guide, the visual guide including tagged images arranged according to their locations within the framework their labels, and prompting human classifiers to tag the additional images with labels for the classification, according to the visual guide.

User-generated visual guide for the classification of images

Growing community of inventors

Mountain View, CA, United States of America

Yu Qing Zhou