Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using a multi-context convolutional self-attention machine learning framework that comprises a shared token embedding machine learning model, a plurality of context-specific self-attention machine learning models, and a cross-context representation inference machine learning model, where each context-specific self-attention machine learning model is configured to generate, for each input text token of an input text sequence, a context-specific token representation using a context-specific self-attention mechanism that is associated with the respective distinct context window size for the context-specific self-attention machine learning model.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using a multi-context convolutional self-attention machine learning framework that comprises a shared token embedding machine learning model, a plurality of context-specific self-attention machine learning models, and a cross-context representation inference machine learning model, where each context-specific self-attention machine learning model is configured to generate, for each input text token of an input text sequence, a context-specific token representation using a context-specific self-attention mechanism that is associated with the respective distinct context window size for the context-specific self-attention machine learning model.

Abstract: Various embodiments of the present disclosure provide machine learning using map representations of categorical data to provide classification predictions. In one example, an embodiment provides for generating a first map representation of a first categorical input feature set for categorical data based on a first coding standard. A second map representation of a second categorical input feature set for the categorical data may also be generated based on a second coding standard. Additionally, at least one machine learning model may be applied to the first map representation and the second map representation to generate the prediction output. Based on the prediction output one or more prediction-based actions may also be performed.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using an attention-based text encoder machine learning model that is trained using a multi-task training routine that is associated with two or more training tasks (e.g., a multi-task training routine that is associated with two or more sequential training tasks, a multi-training routine that is associated with two or more concurrent training tasks, and/or the like).

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing (NLP) operations on multi-segment documents. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform NLP operations on multi-segment documents by generating document segmentation machine learning models, using document segmentation machine learning models to determine document segments of input multi-segment documents, enabling adaptive multi-segment summarization of multi-segment documents, and enabling guided interaction with multi-segment documents.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using an attention-based text encoder machine learning model that is trained using a multi-task training routine that is associated with two or more training tasks (e.g., a multi-task training routine that is associated with two or more sequential training tasks, a multi-training routine that is associated with two or more concurrent training tasks, and/or the like).

Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of categorical/scalar data. Various embodiments of the present invention address one or more of the noted technical challenges. In one example, a method comprises receiving the one or more categorical input features; generating an image representation of the one or more categorical input features, wherein the image representation comprises image region values each associated with a categorical input feature, and further wherein each image region value of the one or more image region values is determined based at least in part on the corresponding categorical input feature associated with the image region value; and processing the image representation using an image-based machine learning model to generate the image-based predictions.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using an attention-based text encoder machine learning model that is trained using a multi-task training routine that is associated with two or more training tasks (e.g., a multi-task training routine that is associated with two or more sequential training tasks, a multi-training routine that is associated with two or more concurrent training tasks, and/or the like).

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using an attention-based text encoder machine learning model that is trained using a multi-task training routine that is associated with two or more training tasks (e.g., a multi-task training routine that is associated with two or more sequential training tasks, a multi-training routine that is associated with two or more concurrent training tasks, and/or the like).

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using an attention-based text encoder machine learning model that is trained using a multi-task training routine that is associated with two or more training tasks (e.g., a multi-task training routine that is associated with two or more sequential training tasks, a multi-training routine that is associated with two or more concurrent training tasks, and/or the like).

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing operations using a multi-context convolutional self-attention machine learning framework that comprises a shared token embedding machine learning model, a plurality of context-specific self-attention machine learning models, and a cross-context representation inference machine learning model, where each context-specific self-attention machine learning model is configured to generate, for each input text token of an input text sequence, a context-specific token representation using a context-specific self-attention mechanism that is associated with the respective distinct context window size for the context-specific self-attention machine learning model.

Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of categorical data. In one example, embodiments comprise receiving a categorical input feature, generating an image representation of the categorical input feature, generating an image-based prediction based at least in part on the image representation, and performing one or more prediction-based actions based at least in part on the image-based prediction.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing natural language processing (NLP) operations on multi-segment documents. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform NLP operations on multi-segment documents by generating document segmentation machine learning models, using document segmentation machine learning models to determine document segments of input multi-segment documents, enabling adaptive multi-segment summarization of multi-segment documents, and enabling guided interaction with multi-segment documents.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis with respect to input data entities that describe temporal relationships across a large number of prediction input codes. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis by using hybrid prediction scores that are determined based at least in part on co-occurrence-based prediction scores and temporal prediction scores, where the co-occurrence-based prediction scores are determined based at least in part on co-occurrence-based historical representation of a sequence of prediction input codes and temporal historical representation of the sequence of prediction input codes.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing health-related predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis by using at least one of shared segment embedding machine learning models or transformer-based machine learning models.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing health-related predictive data analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis by using at least one of segment-wise feature processing machine learning models or a multi-segment representation machine learning model.

Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of categorical/scalar data. Various embodiments of the present invention address one or more of the noted technical challenges. In one example, a method comprises receiving the one or more categorical input features; generating an image representation of the one or more categorical input features, wherein the image representation comprises image region values each associated with a categorical input feature, and further wherein each image region value of the one or more image region values is determined based at least in part on the corresponding categorical input feature associated with the image region value; and processing the image representation using an image-based machine learning model to generate the image-based predictions.

Abstract: Various embodiments of the present invention describe techniques for generating a polygenic risk score generation machine learning framework that integrates an optimal genetic variant refinement model without requiring brute-force traversal of potential parameter spaces defined by various distinct genetic variant sets. In response, various embodiments of the present invention use holistic Bayesian sampling routines to efficiently generate Bayesian evidence numerical estimates for various genetic variant refinement models and select an optimal genetic variant refinement model accordingly. This enables enhancing the accuracy of polygenic risk score generation machine learning frameworks without resorting to computationally resource-intensive traversals of potential parameter spaces defined by various distinct genetic variant sets.

Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of genetic variant data. In one example, embodiments comprise receiving an input feature, generating one or more image representations of the input feature, generating a tensor representation of the one or more image representations, generating a plurality of positional encoding maps, generating an image-based prediction based at least in part on the image representation, and performing one or more prediction-based actions based at least in part on the image-based prediction.

Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating image representations of categorical/scalar data. Various embodiments of the present invention address one or more of the noted technical challenges. In one example, a method comprises receiving the one or more categorical input features; generating an image representation of the one or more categorical input features, wherein the image representation comprises image region values each associated with a categorical input feature, and further wherein each image region value of the one or more image region values is determined based at least in part on the corresponding categorical input feature associated with the image region value; and processing the image representation using an image-based machine learning model to generate the image-based predictions.