Adobe, Inc.

Sapthotharan Krishnan Nair

Atanu Ranjan Sinha

Shivakumar Vaithyanathan

Niyati Himanshu Chhaya

Anup Bandigadi Rao

Fan Du

Shiv Kumar Saini

Tung Mai

Subrata Mitra

Sunav Choudhary

Nikhil Sheoran

Aparna Garimella

Saurabh Mahapatra

Sharmila Reddy Nangi

Sagnik Mukherjee

Manoj Ghuhan Arivazhagan

Ayush Chauhan

Thomas W Jacobs

Laxmikant Mishra

Atharva Anand Joshi

Saarthak Sandip Marathe

Brahmbhatt Paresh Anand

Manupriya Gupta

Snehal Raj

Jay Mundra

Ghetia Siddharth

Atharv Tyagi

Sarthak Chakraborty

Jatin Varshney

Shivi Gupta

Vikas Maddukuri

Yuvraj

A system includes a representation generator subsystem configured to execute a user representation model and a task prediction model to generate a user representation for a user. The user representation model receives user event sequence data comprises a sequence of user interactions with the system. The task prediction model is configured to train the user representation model. The user representation includes a vector of a predetermined size that represents the user event sequence data and is generated by applying the trained user representation model to the user event sequence data. A storage requirement of the user representation is less than a storage space requirement of the user event sequence data. The system includes a data store configured for storing the user representation in a user profile associated with the user.

Generating concise and common user representations for edge systems from event sequence data stored on hub systems

In implementations of systems for cloud-based resource allocation using meters, a computing device implements a resource system to receive resource data describing an amount of cloud-based resources reserved for consumption by client devices during a period of time and a total amount of cloud-based resources consumed by the client devices during the period of time. The resource system determines a consumption distribution using each meter included in a set of meters. Each of the consumption distributions allocates a portion of the total amount of the cloud-based resources consumed to each client device of the client devices. A particular meter used to determine a particular consumption distribution is selected based on a Kendall Tau coefficient of the particular consumption distribution. An amount of cloud-based resources to allocate for a future period of time is estimated using the particular meter and an approximate Shapley value.

Cloud-based resource allocation using meters

A text summarization system auto-generates text summarization models using a combination of neural architecture search and knowledge distillation. Given an input dataset for generating/training a text summarization model, neural architecture search is used to sample a search space to select a network architecture for the text summarization model. Knowledge distillation includes fine-tuning a language model for a given text summarization task using the input dataset, and using the fine-tuned language model as a teacher model to inform the selection of the network architecture and the training of the text summarization model. Once a text summarization model has been generated, the text summarization model can be used to generate summaries for given text.

Auto-creation of custom models for text summarization

In some embodiments, a model training system trains a sample generation model configured to generate synthetic data entries for a dataset. The sample generation model includes a prior model for generating an estimated latent vector from a partially observed data entry, a proposal model for generating a latent vector from a data entry of the dataset and a mask corresponding to the partially observed data entry, and a generative model for generating the synthetic data entries from the latent vector and the partially observed data entry. The model training system trains the sample generation model to optimize an objective function that includes a first term determined using the synthetic data entries and a second term determined using the estimated latent vector and the latent vector. The trained sample generation model can be executed on a client computing device to service queries using the generated synthetic data entries.

Query-oriented approximate query processing based on machine learning techniques

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Santa Clara, CA, United States of America

Sapthotharan Krishnan Nair