Intuit, Inc.

Lalla Mouatadid

Tharathorn Joy Rimchala

Sricharan Kallur Palli Kumar

Kamalika Das

Jiaxin Zhang

Glenn Carter Scott

Kevin Michael Furbish

Sudhir Agarwal

Jay Jie-Bing Yu

Anu Sreepathy

A method optimizes questions to retain engagement. The method includes generating, using a machine learning model, a churn risk from user interaction data. The method includes selecting, when the churn risk satisfies a threshold, a field, from multiple fields, using multiple prediction confidences corresponding to multiple prediction values generated for the multiple fields. The method includes obtaining a prediction value for the field and obtaining a question, corresponding to the field, using the prediction value. The method includes presenting the question and receiving a user input in response to the question.

Optimizing questions to retain engagement

Systems and methods are disclosed for model based document image enhancement. Instead of requiring paired dirty and clean images for training a model to clean document images (which may cause privacy concerns), two models are trained on the unpaired images such that only the dirty images are accessed or only the clean images are accessed at one time. One model is a first implicit model to translate the dirty images from a source space to a latent space, and the other model is a second implicit model to translate the images from the latent space to clean images in a target space. The second implicit model is trained based on translating electronic document images in the target space to the latent space. In some implementations, the implicit models are diffusion models, such as denoising diffusion implicit models based on solving ordinary differential equations.

Model based document image enhancement

Systems and methods for training machine learning models based on domain constraints are disclosed. An example method includes receiving a plurality of images, each image associated with a cluster of a plurality of clusters, the plurality of clusters representing an output of a second machine learning model, assigning a label to each cluster of the plurality of clusters based at least in part on a plurality of constraints, identifying, based at least in part on the plurality of constraints, a first label mismatch for a first image, the first label mismatch indicating that the first image belongs to a first cluster but should be assigned to a second cluster different from the first cluster, reassigning the first image to the second cluster, and training the first machine learning model, based on the labeled clusters of the plurality of clusters, to predict labels associated with subsequently received image data.

Scalable weak-supervised learning with domain constraints

A computer implemented method includes obtaining an original graph data structure including multiple stored nodes connected by multiple edges. The stored nodes include multiple operation stored nodes and multiple data stored nodes. The method further includes generating an auxiliary graph data structure from the original graph data structure. The auxiliary graph data structure includes the operation stored nodes. The method further includes executing a pattern mining tool on the auxiliary graph data structure to obtain a pattern list, traversing the auxiliary graph data structure to identify multiple instances of identified patterns in the pattern list, and presenting the instances.

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Lalla Mouatadid