Abstract: This document relates to context-based evaluations of objects in an image repository. For example, the disclosed implementations can employ image classification and/or object detection models to filter images in an image repository to obtain a filtered subset of images. Then, a context-weighted metric can be determined for a designated object subclassification of objects in the subset. For instance, the context-weighted metric can represent the significance of the designated subclassification relative to one or more other object subclassifications from a larger object classification. The context-weighted metric can be employed for operations such as responding to queries over the image repository.

Abstract: This disclosure provides a mechanism for the enrichment of sparse datasets using language models. By training language models on the specific distribution of known values in a dataset, missing values can be predicted, and the predicted values added, thereby resulting in a more complete dataset. This method also facilitates the enhancement and augmentation of datasets by predicting values for new properties that were not previously available. The approach proves particularly effective at scale, transforming large sparse datasets into more complete and enhanced datasets. Masking language modeling may be employed to train language models capable of generating representations of technical data. Training data includes corpuses of technical data that may be represented as text strings. These pretrained models are fine-tuned to predict various properties. The resulting models can predict missing values in large technical datasets, providing valuable data for guiding scientific research.

Abstract: This disclosure provides a mechanism for the enrichment of sparse datasets using language models. By training language models on the specific distribution of known values in a dataset, missing values can be predicted, and the predicted values added, thereby resulting in a more complete dataset. This method also facilitates the enhancement and augmentation of datasets by predicting values for new properties that were not previously available. The approach proves particularly effective at scale, transforming large sparse datasets into more complete and enhanced datasets. Masking language modeling may be employed to train language models capable of generating representations of technical data. Training data includes corpuses of technical data that may be represented as text strings. These pretrained models are fine-tuned to predict various properties. The resulting models can predict missing values in large technical datasets, providing valuable data for guiding scientific research.

Abstract: This document relates to context-based evaluations of objects in an image repository. For example, the disclosed implementations can employ image classification and/or object detection models to filter images in an image repository to obtain a filtered subset of images. Then, a context-weighted metric can be determined for a designated object subclassification of objects in the subset. For instance, the context-weighted metric can represent the significance of the designated subclassification relative to one or more other object subclassifications from a larger object classification. The context-weighted metric can be employed for operations such as responding to queries over the image repository.

Abstract: This disclosure provides a mechanism for the enrichment of sparse datasets using language models. By training language models on the specific distribution of known values in a dataset, missing values can be predicted, and the predicted values added, thereby resulting in a more complete dataset. This method also facilitates the enhancement and augmentation of datasets by predicting values for new properties that were not previously available. The approach proves particularly effective at scale, transforming large sparse datasets into more complete and enhanced datasets. Masking language modeling may be employed to train language models capable of generating representations of technical data. Training data includes corpuses of technical data that may be represented as text strings. These pretrained models are fine-tuned to predict various properties. The resulting models can predict missing values in large technical datasets, providing valuable data for guiding scientific research.

Abstract: A machine learning model trained with a triplet loss function classifies input strings into one of multiple hierarchical categories. The machine learning model is pre-trained using masking language modeling on a corpus of unlabeled strings. The machine learning module includes an attention-based bi-directional transformer layer. Following initial training, the machine learning model is refined by additional training with a loss function that includes cross-entropy loss and triplet loss. This provides a deep learning solution to classify input strings into one or more hierarchical categories. Embeddings generated from inputs to the machine learning model capture language similarities that can be visualized in a cartesian plane where strings with similar meanings are grouped together.

Abstract: The present disclosure relates to generating a complex entity index based on a combination of atomic and deep learned attributes associated with instances of a complex entity. For example, systems described herein generate a multi-dimensional representation of entity instances based on evaluation of digital content associated with the respective entity instances. Systems described herein further generate an index representation in which similarity of entity instances are illustrated and presented via an interactive presentation that enables a user to traverse instances of an entity to observe similarities and differences between instances of an entity that have similar embeddings to one another within a multi-dimensional index space.

Abstract: The present disclosure relates to generating a complex entity index based on a combination of atomic and deep learned attributes associated with instances of a complex entity. For example, systems described herein generate a multi-dimensional representation of entity instances based on evaluation of digital content associated with the respective entity instances. Systems described herein further generate an index representation in which similarity of entity instances are illustrated and presented via an interactive presentation that enables a user to traverse instances of an entity to observe similarities and differences between instances of an entity that have similar embeddings to one another within a multi-dimensional index space.