Abstract: Unidimensional embedding using multi-modal deep learning models. An autoencoder executing on a processor may receive transaction data for a plurality of transactions, the transaction data including a plurality of fields, the plurality of fields including a plurality of different data types. An embeddings layer of the autoencoder may generate an embedding vector for a first transaction, the embedding vector includes floating point values to represent the plurality of data types of the transaction data. One or more fully connected layers of the autoencoder may generate, based on the embedding vector, a plurality of statistical distributions for the first transaction, each statistical distribution includes a respective embedding vector. A sampling layer of the autoencoder may sample a first statistical distribution of the plurality of statistical distributions. A decoder of the autoencoder may decode the first statistical distribution to generate an output representing the first transaction.

Abstract: A framework is presented for designing a class of distributed, asynchronous optimization algorithms realized as signal processing architectures utilizing various conservation principles. The architectures are specifically based on stationary conditions pertaining to primal and dual variables in a class of generally nonconvex optimization problems. The stationary conditions, which are closely related to the principles of stationary content and co-content that naturally arise from Tellegen's theorem in electrical networks, are transformed via a linear change of coordinates to obtain a set of linear and nonlinear maps that form the basis for implementation. The resulting algorithms can operate by processing a linear superposition of primal and dual decision variables using the associated maps, coupled using synchronous or asynchronous delay elements to form a distributed system.

Abstract: A framework is presented for designing a class of distributed, asynchronous optimization algorithms realized as signal processing architectures utilizing various conservation principles. The architectures are specifically based on stationary conditions pertaining to primal and dual variables in a class of generally nonconvex optimization problems. The stationary conditions, which are closely related to the principles of stationary content and co-content that naturally arise from Tellegen's theorem in electrical networks, are transformed via a linear change of coordinates to obtain a set of linear and nonlinear maps that form the basis for implementation. The resulting algorithms can operate by processing a linear superposition of primal and dual decision variables using the associated maps, coupled using synchronous or asynchronous delay elements to form a distributed system.

Abstract: A framework is presented for designing a class of distributed, asynchronous optimization algorithms realized as signal processing architectures utilizing various conservation principles. The architectures are specifically based on stationary conditions pertaining to primal and dual variables in a class of generally nonconvex optimization problems. The stationary conditions, which are closely related to the principles of stationary content and co-content that naturally arise from Tellegen's theorem in electrical networks, are transformed via a linear change of coordinates to obtain a set of linear and nonlinear maps that form the basis for implementation. The resulting algorithms can operate by processing a linear superposition of primal and dual decision variables using the associated maps, coupled using synchronous or asynchronous delay elements to form a distributed system.

Abstract: A framework is presented for designing a class of distributed, asynchronous optimization algorithms realized as signal processing architectures utilizing various conservation principles. The architectures are specifically based on stationary conditions pertaining to primal and dual variables in a class of generally nonconvex optimization problems. The stationary conditions, which are closely related to the principles of stationary content and co-content that naturally arise from Tellegen's theorem in electrical networks, are transformed via a linear change of coordinates to obtain a set of linear and nonlinear maps that form the basis for implementation. The resulting algorithms can operate by processing a linear superposition of primal and dual decision variables using the associated maps, coupled using synchronous or asynchronous delay elements to form a distributed system.