Adobe, Inc.

Gautam Choudhary

Iftikhar Ahamath Burhanuddin

Ryan A Rossi

Eunyee Koh

Fan Du

Atanu Ranjan Sinha

Sumit Shekhar

Subrata Mitra

Sunav Choudhary

Abhilasha Sancheti

Omar Rahman

Vivek Sinha

Scott Trafton

Shubhanshu Agarwal

Sanjay Sukumaran

E Santhosh Kumar

Jashn Arora

Munish Gupta

Rahul Saxena

Atharv Tyagi

Priyanshu Yadav

Liana Vagharshakian

Camille Girabawe

The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements a secure distributed data collaboration architecture for generating synthetic datasets. For example, the disclosed system sends a request to perform a data collaboration with a first dataset of a first local node and a second dataset of a second local node. The disclosed system receives intermediate feature maps from the local nodes that correspond with the datasets and generates a combined feature map. Further, the disclosed system generates a synthetic dataset from the combined feature map by utilizing a central generative model. Moreover, the synthetic dataset generated by the disclosed system is statistically representative of the first dataset and the second dataset.

Providing data collaboration via a distributed secure collaboration framework

In implementations of systems for generating node embeddings for multiple roles, a computing device implements an embeddings system to cluster nodes of a graph into clusters. An initial role membership vector is computed for each of the nodes based on the clusters. The embeddings system generates a first set of role embeddings for a particular node of the nodes based on the initial role membership vector for the particular node and nodes connected to the particular node in the graph. The embeddings system determines an indication of at least one of a node classification or a link prediction for the graph based on the first set of role embeddings and a second set of role embeddings for an additional node of the nodes.

Generating node embeddings for multiple roles

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently predicting conversion probability scores and key personas for target entities utilizing an artificial intelligence approach. For example, the disclosed systems utilize a conversion activity score neural network to predict conversion activity probability scores for target entities and utilize a persona prediction machine learning model to predict key personas associated with target entities. In particular, the disclosed systems utilize the conversion activity score neural network to generate a predicted conversion activity probability score for a target entity from input data including client device interactions of digital profiles belonging to the target entity as well as an entity feature vector representing characteristics of the target entity. The disclosed systems also (or alternatively) utilize a persona prediction machine learning model to determine a set of key personas for the target entity from the entity feature vector.

Artificial intelligence approaches for predicting conversion activity probability scores and key personas for target entities

The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate expressive audio for input texts based on a word-level analysis of the input text. For example, the disclosed systems can utilize a multi-channel neural network to generate a character-level feature vector and a word-level feature vector based on a plurality of characters of an input text and a plurality of words of the input text, respectively. In some embodiments, the disclosed systems utilize the neural network to generate the word-level feature vector based on contextual word-level style tokens that correspond to style features associated with the input text. Based on the character-level and word-level feature vectors, the disclosed systems can generate a context-based speech map. The disclosed systems can utilize the context-based speech map to generate expressive audio for the input text.

Expressive text-to-speech utilizing contextual word-level style tokens

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Sri Ganganagar, India

Gautam Choudhary