Abstract: The present disclosure relates to efficiently receiving and processing input tasks in a way that is scalable and which reduces both the quantity of tokens processed by a foundation model (e.g., an LLM) as well as the number of API calls that are made in processing the input tasks. A system batches a set of inputs to provide as a single batch of input(s) into an LLM. The system generates one or more permutations of the batched input(s) to determine outputs based on variable orders in which the input data is provided within the respective permutations of the batched inputs. The system further may eliminate one or more of the data inputs within the respective batches to facilitate smaller batched inputs without sacrificing accuracy in a set of outputs generated by the LLM responsive to the batch permutations.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for utilizing a concept graphing system to determine and provide relationships between concepts within document collections or corpora. For example, the concept graphing system can generate and utilize machine-learning models, such as a sparse graph recovery machine-learning model, to identify less-obvious correlations between concepts, including positive and negative concept connections, as well as provide these connections within a visual concept graph. Additionally, the concept graphing system can provide a visual concept graph that determines and displays concept correlations based on the input of a single concept, multiple concepts, or no concepts.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for utilizing an interactive graphing system to achieve improved dataset exploration utilizing an intelligent workflow and an interactive user interface. More specifically, the interactive graphing system facilitates generating updated network graphs that include inferred user influences based on implicit user action. Indeed, the interactive graphing system can automatically generate and present a user with an updated network graph that includes added, removed, or subsetted elements and relationships that are otherwise hidden from a user. Additionally, the interactive graphing system facilitates network graph exploration and processing of customized combined network graphs that join otherwise separate network graphs.

Abstract: A system for implementing object state distributions obtains a first object state distribution associated with a first image set. The first object state distribution includes a first plurality of object state vectors generated based upon (i) a first set of object type classifications associated with the first image set and (ii) a first set of image information based on the first image set. The system obtains a second object state distribution associated with a second image set. The second object state distribution comprises a second plurality of object state vectors generated based upon (i) a second set of object type classifications associated with the second image set and (ii) a second set of image information based on the second image set. The system determines a distance measure between the first object state distribution and the second object state distribution and assigns a label based upon the distance measure.

Abstract: A system for implementing object state distributions obtains a first object state distribution associated with a first image set. The first object state distribution includes a first plurality of object state vectors generated based upon (i) a first set of object type classifications associated with the first image set and (ii) a first set of image information based on the first image set. The system obtains a second object state distribution associated with a second image set. The second object state distribution comprises a second plurality of object state vectors generated based upon (i) a second set of object type classifications associated with the second image set and (ii) a second set of image information based on the second image set. The system determines a distance measure between the first object state distribution and the second object state distribution and assigns a label based upon the distance measure.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for utilizing a concept graphing system to determine and provide relationships between concepts within document collections or corpora. For example, the concept graphing system can generate and utilize machine-learning models, such as a sparse graph recovery machine-learning model, to identify less-obvious correlations between concepts, including positive and negative concept connections, as well as provide these connections within a visual concept graph. Additionally, the concept graphing system can provide a visual concept graph that determines and displays concept correlations based on the input of a single concept, multiple concepts, or no concepts.

Abstract: A machine learning model is used to generate molecular images by text-to-image diffusion techniques based on natural language text inputs. The machine learning model is trained on combinations of molecule images and corresponding text such that representatives of both are embedded in latent space. Users provide natural language text describing molecular characteristics and the machine learning model generates an image of a molecule with those characteristics. Existing molecular images or those generated by the system can be further edited and refined with additional natural language text instructions. The system also uses machine vision techniques to understand the molecule represented by a molecular image and translate that image into other representations of the molecule.

Abstract: The present disclosure relates to utilizing an embedding space relationship query exploration system to explore embedding spaces generated by machine-learning models. For example, the embedding space relationship query exploration system facilitates efficiently and flexibly revealing relationships that are encoded in a machine-learning model during training and inferencing. In particular, the embedding space relationship query exploration system utilizes various embeddings relationship query models to explore and discover the relationship types being learned and preserved within the embedding space of a machine-learning model.

Abstract: The present disclosure relates to utilizing a style-matching image generation system to generate large datasets of style-matching images having matching styles and content to an initial small sample set of input images. For example, the style-matching image generation system utilizes a selection of style-mixed stored images with a generative machine-learning model to produce large datasets of synthesized images. Further, the style-matching image generation system utilizes the generative machine-learning model to conditionally sample synthesized images that accurately match the style, content, characteristics, and patterns of the initial small sample set and that also provide added variety and diversity to the large image dataset.

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 interpretation of a graph data structure represented on a computing system in which the connection between a pair of nodes in the graph may be interpreted by which intermediary entity (node or edge) on a path (e.g., a shortest path) between the node pair is most dominant. That is, if the intermediary entity were not present, a detour path is determined. The greater the difference between the detour path and the original path, the more significant that intermediary entity is. The significance of multiple intermediary entities in the original path may be determined in this way.

Abstract: A data processing system implements obtaining attention matrices from a first machine learning model that is pretrained and includes a plurality of self-attention layers. The data processing system further implements analyzing the attention matrices to generate a computation graph based on the attention matrices. The computation graph provides a representation of behavior of the first machine learning model across the plurality of self-attention layers. The data processing system is further implements analyzing the computation graph using a second machine learning model. The second machine learning model is trained to receive the computation graph to output model behavior information. The model behavior information identifying which layers of model performed specific tasks associated with generating predictions by the first machine learning model.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for utilizing an interactive graphing system to achieve improved dataset exploration utilizing an intelligent workflow and an interactive user interface. More specifically, the interactive graphing system facilitates generating updated network graphs that include inferred user influences based on implicit user action. Indeed, the interactive graphing system can automatically generate and present a user with an updated network graph that includes added, removed, or subsetted elements and relationships that are otherwise hidden from a user. Additionally, the interactive graphing system facilitates network graph exploration and processing of customized combined network graphs that join otherwise separate network graphs.

Abstract: The present disclosure relates to systems, methods, and computer-readable media for utilizing a concept graphing system to determine and provide relationships between concepts within document collections or corpora. For example, the concept graphing system can generate and utilize machine-learning models, such as a sparse graph recovery machine-learning model, to identify less-obvious correlations between concepts, including positive and negative concept connections, as well as provide these connections within a visual concept graph. Additionally, the concept graphing system can provide a visual concept graph that determines and displays concept correlations based on the input of a single concept, multiple concepts, or no concepts.

Abstract: The interpretation of a graph data structure represented on a computing system in which the connection between a pair of nodes in the graph may be interpreted by which intermediary entity (node or edge) on a path (e.g., a shortest path) between the node pair is most dominant. That is, if the intermediary entity were not present, a detour path is determined. The greater the difference between the detour path and the original path, the more significant that intermediary entity is. The significance of multiple intermediary entities in the original path may be determined in this way.

Abstract: A system for generating object state distributions receives an image set and generates a set of object type classifications. The set of object type classifications includes an object type classification for one or more objects represented in the image set. The set of object type classifications is generated by utilizing the image set as input to one or more object detection modules. The system generates an object state vector for each object type classification of the set of object type classifications. The object state vector(s) is/are generated by utilizing (i) the set of object type classifications and (ii) a set of image information based on the image set as input to one or more object state description modules. The system defines an object state distribution based on collections of the object state vectors.

Abstract: A computer system is configured to provide an intelligent machine-learning (ML) model catalog containing data associated with multiple ML models. The multiple ML models are trained over multiple training datasets respectively, and the intelligent ML model catalog contains at least multiple training data spaces of embeddings generated based on the multiple ML models and the multiple training datasets. In response to receiving a user dataset, for at least one ML model in the plurality of ML models, the computer system is configured to extract a user data space of embeddings based on the at least one ML model and the user dataset, and evaluate the user data space against the training data space to determine whether the at least one ML model is a good fit for the user dataset.

Abstract: The monitoring of performance of a machine-learned model for use in generating an embedding space. The system uses two embedding spaces: a reference embedding space generated by applying an embedding model to reference data, and an evaluation embedding space generated by applying the embedding model to evaluation data. The system obtains multiple views of the reference embedding space, and uses those multiple views to determine a distance threshold. The system determines a distance between the evaluation and reference embedding spaces, and compares that distance with the fitness threshold. Based on the comparison, the system determines a level of acceptability of the model for use with the evaluation dataset.

Abstract: A system for implementing object state distributions obtains a first object state distribution associated with a first image set. The first object state distribution includes a first plurality of object state vectors generated based upon (i) a first set of object type classifications associated with the first image set and (ii) a first set of image information based on the first image set. The system obtains a second object state distribution associated with a second image set. The second object state distribution comprises a second plurality of object state vectors generated based upon (i) a second set of object type classifications associated with the second image set and (ii) a second set of image information based on the second image set. The system determines a distance measure between the first object state distribution and the second object state distribution and assigns a label based upon the distance measure.

Abstract: The interpretation of a graph data structure represented on a computing system in which the connection between a pair of nodes in the graph may be interpreted by which intermediary entity (node or edge) on a path (e.g., a shortest path) between the node pair is most dominant. That is, if the intermediary entity were not present, a detour path is determined. The greater the difference between the detour path and the original path, the more significant that intermediary entity is. The significance of multiple intermediary entities in the original path may be determined in this way.