Landing Ai

Mark William Sabini

Daniel Bibireata

Kai Yang

Andrew Yan-Tak Ng

Dillon Laird

Yuxiang Zhang

Shankaranand Jagadeesan

Sebastian Rodriguez

Quinn Killough

Ankur Rawat

Yan Liu

Gopi Prashanth Gopal

Francisco Matias Cuenca-Acuna

Yu Qing Zhou

Sanjeev Satheesh

Pingyang He

Sanjay Boddhu

Sanjay Bodhu

Cristobal Silva

Gregory Diamos

Whit Blodgett

YuQing Zhou

Juan Camilo Fernancez

Camilo Zapata

Ramesh Sampath

Aldrin Leal

Junjie Guan

Mingrui Zhang

Zeqi Qiu

Camilo Iral

Leela Seshu Reddy Cheedepudi

Yazhou Cao

Whitney Blodgett

Hao Sheng

Yuanzhe Dong

Rahul Devraj Solanki

Wei Fu

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 model management system performs error analysis on results predicted by a machine learning model. The model management system identifies an incorrectly classified image outputted from a machine learning model and identifies using the Neural Template Matching (NTM) algorithm, an additional image correlated to the selected image. The system outputs correlated images based on a given image and a selection by a user through a user interface of a region of interest (ROI) of the given image. The region is defined by a bounding polygon input and the correlated images include features correlated to the features within the ROI. The system prompts a task associated with the additional image. The system receives a response that includes an indication that the additional image is incorrectly labeled and including a replacement label and instruct that the machine learning model be retrained using an updated training dataset that includes the replacement label.

Guided workflow for deep learning error analysis

A processor receives an image of a syringe. After identifying a background and foreground of the image, where the foreground indicates pixels that may be associated with a defect, the processor subtracts the background to generate an updated image with an accentuated foreground. The processor applies a bounding box to a group of pixels in the foreground and inputs the bounding box into a classifier. The classifier outputs a label indicating whether the syringe is defective.

Automated visual inspection of syringes

Growing community of inventors

River Edge, NJ, United States of America

Mark William Sabini