Adobe, Inc.

Sunav Choudhary

Subrata Mitra

Shiv Kumar Saini

Atanu Ranjan Sinha

Gaurush Hiranandani

Ayush Chauhan

Ankur Garg

Chris Challis

Saurabh Kumar Mishra

Manoj Ghuhan A

William Brandon George

Fan Du

Ritwik Sinha

John Bevil Bates

Sanket Vaibhav Mehta

Kanak Vivek Mahadik

Ivan Andrus

Said Kobeissi

Sapthotharan Krishnan Nair

Manoj Ghuhan Arivazhagan

Aishwarya Asesh

Prithvi Bhutani

Sai Shashank Kalakonda

Sarthak Chakraborty

Shivi Gupta

Yuvraj

Atharva Anand Joshi

Kartavya Jain

Purnima Grover

Liza Dahiya

Samir Khuller

Atharv Tyagi

Harvineet Singh

Shaddy Garg

Vishal Babu Bhavani

Anuj Jitendra Diwan

Arpit Aggarwal

Kishalay Raj

Prateek Jain

Piyush Kumar Maurya

Sheng Yang

Systems and methods for identifying key moments, such as key moments within a livestream, are described. Embodiments of the present disclosure obtain video data and text data. In some cases, the text data is aligned with a timeline of the video data. The system then computes a moment importance score for a time of the video data using a machine learning model based on the video data and the text data, and presents content to a user at the time of the video data based on the moment importance score.

Livestream key moment identification

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

A resource control system is described that is configured to control scheduling of executable jobs by compute instances of a service provider system. In one example, the resource control system outputs a deployment user interface to obtain job information. Upon receipt of the job information, the resource control system communicates with a service provider system to obtain logs from compute instances implemented by the service provider system for the respective executable jobs. The resource control system uses data obtained from the logs to estimate utility indicating status of respective executable jobs and an amount of time to complete the executable jobs by respective compute instances. The resource control system then employs a machine-learning module to generate an action to be performed by compute instances for respective executable jobs.

Scheduling and control of executable jobs over compute instances

Techniques are provided for scheduling multiple jobs on one or more cloud computing instances, which provide the ability to select a job for execution from among a plurality of jobs, and to further select a designated instance from among a plurality of cloud computing instances for executing the selected job. The job and the designated instance are each selected based on a probability distribution that a cost of executing the job on the designated instance does not exceed the budget. The probability distribution is based on several factors including a cost of prior executions of other jobs on the designated instance and a utility function that represents a value associated with a progress of each job. By scheduling select jobs on discounted cloud computing instances, the aggregate utility of the jobs can be maximized or otherwise improved for a given budget.

Scheduling jobs on interruptible cloud computing instances

A method performed by one or more processors that preserves a machine learning model comprises accessing model parameters associated with a machine learning model. The model parameters are determined responsive to training the machine learning model. The method comprises generating a plurality of model parameter sets, where each of the plurality of model parameter sets comprises a separate portion of the set of model parameters. The method comprises determining one or more parity sets comprising values calculated from the plurality of model parameter sets. The method comprises distributing the plurality of model parameter sets and the one or more parity sets among a plurality of computing devices, where each of the plurality of computing devices stores a model parameter set of the plurality of model parameter sets or a parity set of the one or more parity sets. The method comprises accessing, from the plurality of computing devices, a number of sets comprising model parameter sets and at least one parity set. The method comprises reconstructing the machine learning model from the number of sets accessed from the plurality of computing devices.

Managing machine learning model reconstruction

Certain embodiments involve extracting seasonal, level, and spike components from a time series of metrics data, which describe interactions with an online service over a time period. For example, an analytical system decomposes the time series into latent components that include a seasonal component series, a level component series, a spike component series, and an error component series. The decomposition involves configuring an optimization algorithm with a constraint indicating that the time series is a sum of these latent components. The decomposition also involves executing the optimization algorithm to minimize an objective function subject to the constraint and identifying, from the executed optimization algorithm, the seasonal component series, the level component series, the spike component series, and the error component series that minimize the objective function. The analytical system outputs at least some latent components for anomaly-detection or data-forecasting.

Extracting seasonal, level, and spike components from a time series of metrics data

Determination of high value customer journey sequences is performed by determining customer interactions that are most frequent as length N=1 sub-sequences, recursively determining most frequent length N+1 sub-sequences that start with the length N sub-sequences, determining a first count indicating how often one of the sub-sequences appears in the sequences, determining a second count indicating how often the one sub-sequence resulted in the goal, and using the counts to determine the most or least effective sub-sequences for achieving the goal.

Intelligent customer journey mining and mapping

This disclosure relates to methods, non-transitory computer readable media, and systems that asynchronously train a machine learning model across client devices that implement local versions of the model while preserving client data privacy. To train the model across devices, in some embodiments, the disclosed systems send global parameters for a global machine learning model from a server device to client devices. A subset of the client devices uses local machine learning models corresponding to the global model and client training data to modify the global parameters. Based on those modifications, the subset of client devices sends modified parameter indicators to the server device for the server device to use in adjusting the global parameters. By utilizing the modified parameter indicators (and not client training data), in certain implementations, the disclosed systems accurately train a machine learning model without exposing training data from the client device.

Asynchronously training machine learning models across client devices for adaptive intelligence

Systems and techniques are described herein for updating a machine learning model on edge servers. Local parameters of the machine learning model are updated at a plurality of edge servers using fresh data on the edge servers, rather than waiting for the data to reach a global server to update the machine learning model. Hence, latency is significantly reduced, making the systems and techniques described herein suitable for real-time services that support streaming data. Moreover, by updating global parameters of the machine learning model at a global server in a deterministic manner based on parameter updates from the edge servers, rather than by including randomization steps, global parameters of the converge quickly to their optimal values. The global parameters are sent from the global server to the plurality of edge servers at each iteration, thereby synchronizing the machine learning model on the edge servers.

Updating machine learning models on edge servers

Graphing services are provided to a device cooperative that includes data contributors, e.g., website hosts. Anonymized user data, provided by the data contributors, is accessed, via a blockchain, decrypted, and aggregated. A device graph is generated based on the aggregated user data. Contribution metrics are provided to the data contributors. A first contribution metric for a first data contributor indicates a contribution to the device graph of a first portion of the user data that was provided by the first data contributor. In response to receiving a request for a verification of the first contribution metric, a zero knowledge proof of the first contribution metric is generated and provided to the first data contributor. The first data contributor is enabled to evaluate the zero knowledge proof independent of access to a second portion of the user data that was provided by a second data contributor of the device cooperative.

Cooperative platform for generating, securing, and verifying device graphs and contributions to device graphs

The present disclosure describes systems, non-transitory computer-readable media, and methods for determining latent components of a metrics time series and identifying anomalous data within the metrics time series based on one or both of spikes/dips and level changes from the latent components satisfying significance thresholds. To identify such latent components, in some cases, the disclosed systems account for a range of value types by intelligently subjecting real values to a latent-component constraint for decomposing the time series and intelligently excluding non-real values from the latent-component constraint. The disclosed systems can further identify significant anomalous data values from latent components of the metrics time series by jointly determining whether one or both of a subseries of a spike-component series and a level change from a level-component series satisfy significance thresholds.

Robust anomaly and change detection utilizing sparse decomposition

Growing community of inventors

Bokaro Steel, India

Sunav Choudhary