Abstract: Embodiments of the present disclosure include systems and methods for training neural networks. In one embodiment, data for an artificial intelligence model is processed in a first plurality of stages and in a second plurality of stages. The first and second pluralities of stages form a pipeline. One or more of the first plurality of stages uses at least one memory associated with a corresponding one or more of the second plurality of stages to balance memory across the pipeline.

Abstract: Systems, methods, and software are disclosed herein for enhancing functions and formulas in spreadsheets. A computing apparatus detects a beginning of a formula in a cell of a spreadsheet. In response to detecting the beginning of the formula, the computing apparatus identifies one or more vector representations of a context of the cell and supplies the one or more vector representations as input to one or more machine learning models. The one or more machine learning models produce output comprising one or more suggested functions for the formula and one or more suggested operands, which the computing apparatus presents in a user interface to the spreadsheet.

Abstract: A computation graph of a machine learning model is accessed from memory and a constraint solver is used to compute a partition of the computation graph into ordered stages of an execution pipeline. In use, when inference or training of the machine learning model takes place by executing the pipeline, execution cost of the stages are balanced according to the computed partition.

Abstract: Systems, methods, and software are disclosed herein for enhancing functions and formulas in spreadsheets. A computing apparatus detects a beginning of a formula in a cell of a spreadsheet. In response to detecting the beginning of the formula, the computing apparatus identifies a vector representation of a context of the cell and supplies the vector representation as input to one or more machine learning models. The one or more machine learning models produce output comprising one or more suggested functions for the formula and one or more suggested operands, which the computing apparatus presents in a user interface to the spreadsheet.

Abstract: Systems, methods, and software are disclosed herein for enhancing functions and formulas in spreadsheets. A computing apparatus detects a beginning of a formula in a cell of a spreadsheet. In response to detecting the beginning of the formula, the computing apparatus identifies one or more vector representations of a context of the cell and supplies the one or more vector representations as input to one or more machine learning models. The one or more machine learning models produce output comprising one or more suggested functions for the formula and one or more suggested operands, which the computing apparatus presents in a user interface to the spreadsheet.

Abstract: Systems, methods, and software are disclosed herein for enhancing functions and formulas in spreadsheets. A computing apparatus detects a beginning of a formula in a cell of a spreadsheet. In response to detecting the beginning of the formula, the computing apparatus identifies a vector representation of a context of the cell and supplies the vector representation as input to one or more machine learning models. The one or more machine learning models produce output comprising one or more suggested functions for the formula and one or more suggested operands, which the computing apparatus presents in a user interface to the spreadsheet.