Adobe, Inc.

Ritwick Chaudhry

Shiv Kumar Saini

Harvineet Singh

Sumit Shekhar

Ayush Chauhan

Bhavya Bahl

Sriya Sainath

Savya Sindhu Gupta

Parth Gupta

Archiki Prasad

Amireddy Prashanth Reddy

Zoya Bylinskii

Ajay Shridhar Joshi

Utkarsh Gupta

Ayush Goyal

Tushar Gurjar

Prann Bansal

Pradeep Dogga

This disclosure describes methods, systems, and non-transitory computer readable media for automatically parsing infographics into segments corresponding to structured groups or lists and displaying the identified segments or reflowing the segments into various computing tasks. For example, the disclosed systems may utilize a novel infographic grouping taxonomy and annotation system to group elements within infographics. The disclosed systems can train and apply a machine-learning-detection model to generate infographic segments according to the infographic grouping taxonomy. By generating infographic segments, the disclosed systems can facilitate computing tasks, such as converting infographics into digital presentation graphics (e.g., slide carousels), reflow the infographic into query-and-response models, perform search functions, or other computational tasks.

Parsing and reflowing infographics using structured lists and groups

A system implements a key value memory network including a key matrix with key vectors learned from training static feature data and time-series feature data, a value matrix with value vectors representing time-series trends, and an input layer to receive, for a target entity, input data comprising a concatenation of static feature data of the target entity, time-specific feature data, and time-series feature data for the target entity. The key value memory network also includes an entity-embedding layer to generate an input vector from the input data, a key-addressing layer to generate a weight vector indicating similarities between the key vectors and the input vector, a value-reading layer to compute a context vector from the weight and value vectors, and an output layer to generate predicted time-series data for a target metric of the target entity by applying a continuous activation function to the context vector and the input vector.

Key-value memory network for predicting time-series metrics of target entities

Techniques for exchanging data segments between data aggregators and data consumers. In an embodiment, a value of an arbitrary data segment selected by a data consumer is computed. In particular, an individual user value is calculated for each user represented in the data segment, wherein the individual user value is a weighted sum (or other function) of the one or more features of the data segment attributable to that user, plus an additive gaussian noise. The overall value of the data segment is the sum of the individual user values. An offer price for the data segment can then be calculated using the overall value. Once a request is received from the consumer to purchase the data segment at the offer price, the data segment can be exchanged between the aggregator and consumer. Thus, a data marketplace or platform for the exchange of data segments is enabled.

System to facilitate exchange of data segments between data aggregators and data consumers

This disclosure involves using key-value memory networks to predict time-series data. For instance, a computing system retrieves, for a target entity, static feature data and target time-series feature data. The computing system can normalize the target time-series feature data based on a normalization scale. The computing system also generates input data by, for example, concatenating the static feature data, the normalized time-series feature data, and time-specific feature data. The computing system generates predicted time-series data for the target metric of the target entity by applying a key-value memory network to the input data. The key-value memory network can include a key matrix learned from training static feature data and training time-series feature data, a value matrix representing time-series trends, and an output layer with a continuous activation function for generating predicted time-series data.

A method, apparatus, and non-transitory computer readable medium for chart question answering are described. The method, apparatus, and non-transitory computer readable medium may receive a text query about a chart, identify a plurality of chart elements in the chart, associate a text string from the text query with corresponding chart elements from the plurality of chart elements, replace the text string in the text query with arbitrary rare words based on the association to produce an encoded query, generate an embedded query based on the encoded query, generate an image feature vector based on the chart, combine the embedded query and the image feature vector to produce a combined feature vector, compute an answer probability vector based on the combined feature vector, and provide an answer to the text query based on the answer probability vector.

Chart question answering

Techniques are described for jointly modeling knowledge tracing and hint-taking propensity. During a read phase, a co-learning model accepts as inputs an identification of a question and the current knowledge state for a learner, and the model predicts probabilities that the learner will answer the question correctly and that the learner will use a learning aid (e.g., accept a hint). The predictions are used to personalize an e-learning plan, for example, to provide a personalized assessment. By using these predictions to personalize a learner's experience, for example, by offering hints at optimal times, the co-learning system increases efficiencies in learning and improves learning outcomes. Once a learner has interacted with a question, the interaction is encoded and provided to the co-learning model to update the learner's knowledge state during an update phase.

Personalized e-learning using a deep-learning-based knowledge tracing and hint-taking propensity model

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Pittsburgh, PA, United States of America

Ritwick Chaudhry