Abstract: Various disclosed embodiments are directed to deriving, via a language model, a summary of data by converting or encoding table data into one or more natural language sentences, which are then used as input to the language model for generating the summary. One or more embodiments are additionally or alternatively directed to deriving, via a language model, a response to a user question or command via a chat interface by providing the language model with the generated summary as input. In this way, for example, the language model can use the summary as a prompt or other target context for providing a response.

Abstract: Systems and methods for generating synthetic tabular data for machine learning and other applications are provided. In some embodiments, a variational autoencoder is trained to learn inter-feature correlations found in tabular data collected from real data sources. The trained variational autoencoder is used to train a generator model of a Generative Adversarial Network (GAN) to generate synthetic tabular data that exhibits the inter-feature correlation distribution found in the tabular data collected from real data sources.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive a reference image depicting a reference object with a target spatial attribute; generate object saliency noise based on the reference image by updating random noise to resemble the reference image; and generate an output image based on the object saliency noise, wherein the output image depicts an output object with the target spatial attribute.

Abstract: Certain aspects and features of the present disclosure relate to providing contextually grounded recommendations using a large language model. For example, a method involves receiving domain specific data for a simulation and transforming the domain specific data into a labeled, natural language description of the domain specific data. The method also involves providing the labeled, natural language description and a classification task prompt with interaction history to a large language model (LLM) to generate a contextually enhanced LLM configured to produce context-aware output. The method further involves outputting, using the contextually enhanced LLM, an interactive list of scored actions corresponding to the simulation. The interactive list can be used to produce a sequence of actions to direct a process or control a machine.

Abstract: Various disclosed embodiments are directed to deriving, via a language model, a summary of data by converting or encoding table data into one or more natural language sentences, which are then used as input to the language model for generating the summary. One or more embodiments are additionally or alternatively directed to deriving, via a language model, a response to a user question or command via a chat interface by providing the language model with the generated summary as input. In this way, for example, the language model can use the summary as a prompt or other target context for providing a response.

Abstract: Systems and methods provide a generative recommendation model that leverages verbalizations generated from sequential data. In accordance with some aspects, sequential data for a trajectory comprising a plurality of steps is accessed, in which the sequential data comprises a tuple for each step of the trajectory. Verbalized sequential data is generated from the sequential data, in which the verbalized sequential data for each step of the trajectory comprises one or more natural language sentences generated from the tuple for the step. A generative model is trained on the verbalized sequential data to provide a trained generative model that generates a recommended action given a prompt specifying a current state.

Abstract: Systems and methods for generating synthetic tabular data for machine learning and other applications are provided. In some embodiments, a variational autoencoder is trained to learn inter-feature correlations found in tabular data collected from real data sources. The trained variational autoencoder is used to train a generator model of a Generative Adversarial Network (GAN) to generate synthetic tabular data that exhibits the inter-feature correlation distribution found in the tabular data collected from real data sources.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive a reference image depicting a reference object with a target spatial attribute; generate object saliency noise based on the reference image by updating random noise to resemble the reference image; and generate an output image based on the object saliency noise, wherein the output image depicts an output object with the target spatial attribute.