Abstract: Systems and methods for selecting a neural network for a machine learning problem are disclosed. A method includes accessing an input matrix. The method includes accessing a machine learning problem space associated with a machine learning problem and multiple untrained candidate neural networks for solving the machine learning problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the machine learning problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the machine learning problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the machine learning problem.

Abstract: Solutions for adapting machine learning (ML) models to neural networks (NNs) include receiving an ML pipeline comprising a plurality of operators; determining operator dependencies within the ML pipeline; determining recognized operators; for each of at least two recognized operators, selecting a corresponding NN module from a translation dictionary; and wiring the selected NN modules in accordance with the operator dependencies to generate a translated NN. Some examples determine a starting operator for translation, which is the earliest recognized operator having parameters. Some examples connect inputs of the translated NN to upstream operators of the ML pipeline that had not been translated. Some examples further tune the translated NN using backpropagation. Some examples determine whether an operator is trainable or non-trainable and flag related parameters accordingly for later training.

Abstract: Methods, systems, and computer program products are provided for generating a neural network model. A ML pipeline parser is configured to identify a set of ML operators for a previously trained ML pipeline, and map the set of ML operators to a set of neural network operators. The ML pipeline parser generates a first neural network representation using the set of neural network operators. A neural network optimizer is configured to perform an optimization on the first neural network representation to generate a second neural network representation. A tensor set provider outputs a set of tensor operations based on the second neural network representation for execution on a neural network framework. In this manner, a traditional ML pipeline can be converted into a neural network pipeline that may be executed on an appropriate framework, such as one that utilizes specialized hardware accelerators.

Abstract: Described herein is a system and method for training cardinality models in which workload data is analyzed to extract and compute features of subgraphs of queries. Using a machine learning algorithm, the cardinality models are trained based on the features and actual runtime statistics included in the workload data. The trained cardinality models are stored. Further described herein is a system and method of predicting cardinality of subgraphs of a query. Features for the subgraphs of the query are extracted and computed. Cardinality models are retrieved based on the features of the subgraphs of the query. Cardinalities of the subgraphs of the query are predicted using the retrieved cardinality models. One of the subgraphs of the query is selected to be utilized for execution of the query based on the predicted cardinalities.

Abstract: Solutions for adapting machine learning (ML) models to neural networks (NNs) include receiving an ML pipeline comprising a plurality of operators; determining operator dependencies within the ML pipeline; determining recognized operators; for each of at least two recognized operators, selecting a corresponding NN module from a translation dictionary; and wiring the selected NN modules in accordance with the operator dependencies to generate a translated NN. Some examples determine a starting operator for translation, which is the earliest recognized operator having parameters. Some examples connect inputs of the translated NN to upstream operators of the ML pipeline that had not been translated. Some examples further tune the translated NN using backpropagation. Some examples determine whether an operator is trainable or non-trainable and flag related parameters accordingly for later training.

Abstract: Systems and methods for selecting a neural network for a machine learning problem are disclosed. A method includes accessing an input matrix. The method includes accessing a machine learning problem space associated with a machine learning problem and multiple untrained candidate neural networks for solving the machine learning problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the machine learning problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the machine learning problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the machine learning problem.

Abstract: Described herein is a system and method for training cardinality models in which workload data is analyzed to extract and compute features of subgraphs of queries. Using a machine learning algorithm, the cardinality models are trained based on the features and actual runtime statistics included in the workload data. The trained cardinality models are stored. Further described herein is a system and method of predicting cardinality of subgraphs of a query. Features for the subgraphs of the query are extracted and computed. Cardinality models are retrieved based on the features of the subgraphs of the query. Cardinalities of the subgraphs of the query are predicted using the retrieved cardinality models. One of the subgraphs of the query is selected to be utilized for execution of the query based on the predicted cardinalities.