Hypernet Labs, Inc.

Ivan James Ravlich

Todd Allen Chapman

Christopher Taylor Hansen

Daniel Maren

A system and a method are disclosed containerizing a source code file. In some embodiments, the system detects a command to navigate a user interface to a machine station. The system responsively generates for display using the user interface a station identifier corresponding to the machine station and a drag-and-drop interface. The system receives a source code file by way of a drag-and-drop operation being performed with respect to the drag-and-drop interface. The system selects a machine of the machine station to execute the source code file, containerizes the source code file based on a language used by the selected machine, and commands the selected machine to execute the containerized source code file. The system generates for display results of the executed containerized source code file using the user interface.

Containerizing source code for execution in different language using drag-and-drop operation

A distributed computing device stores user preference data representing preferences of a user with respect to a portion of a set of items. The distributed computing device randomly samples the user preference data to calculate sampled user preference data. The distributed computing device iteratively executes, in conjunction with additional distributed computing devices connected by a network, a process to determine a consensus result for the sampled user preference data. The consensus result is based on the sampled user preference data and additional sampled user preference data calculated by the additional distributed computing devices and based on preferences of additional users. The distributed computing device determines a recommendation model based on the consensus result, the recommendation model reflecting preferences of the user and additional users. The distributed computing device identifies an item to provide to the user as a recommendation based on the recommendation model, and provides recommendation to the user.

Decentralized recommendations using distributed average consensus

A distributed computing device generates a gradient descent matrix based on data received by the distributed computing device and a model stored on the distributed computing device. The distributed computing device calculates a sampled gradient descent matrix based on the gradient descent matrix and a random matrix. The distributed computing device iteratively executes a process to determine a consensus gradient descent matrix in conjunction with a plurality of additional distributed computing devices connected by a network to the distributed computing device. The consensus gradient descent matrix is based on the sampled gradient descent matrix and a plurality of additional sampled gradient decent matrices calculated by the plurality of additional distributed computing devices. The distributed computing device updates the model stored on the distributed computing device based on the consensus gradient descent matrix.

Coordinated learning using distributed average consensus

Each of a plurality of distributed computing devices receives a respective data partition of a plurality of data partitions for a computing task. A first distributed computing device generates a first partial result of a plurality of partial results generated by the plurality of distributed computing devices. The first computing device iteratively executes a distributed average consensus (DAC) process. At each iteration, the first computing device transmits the first partial result to a second computing device, receives a second partial result generated by the second computing device, and updates the first partial result by computing an average of the first and second partial results. In response to determining that respective partial results of the plurality of distributed computing devices have reached a consensus value, the first computing device stops executing the DAC process, and generates a final result of the computing task based on the consensus value.

Distributed computing using distributed average consensus

A distributed computing device calculates word counts for each of a set of documents. The word counts are represented as values, each representing a number of times a corresponding word appears in one of the set of documents. The distributed computing device randomly samples the word counts to calculate sampled word counts. The distributed computing device and additional distributed computing devices iteratively execute a process to determine a consensus result for the sampled word counts based on the sampled word counts and additional sampled word counts calculated by the additional distributed computing devices. The distributed computing device determines a latent semantic index (LSI) subspace based on the consensus result for the sampled word count and reflecting contents of the set and additional sets of documents. The distributed computing device projects a document into the LSI subspace to determine the latent semantic content of the document.

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Los Altos, CA, United States of America

Ivan James Ravlich