Abstract: The present disclosure relates to methods and systems for sharing with a plurality of users a chat session that uses large language models to provide responses for input messages received for the chat session. The methods and systems provide access to the chat session to the users and update the chat session in response to any changes made to the chat session by any of the users. The methods and systems allow the users to resume the chat session at a future time using the chat session history.

Abstract: Systems and methods are provided for building and training machine learning models configured to generate in-domain embeddings and perform multimodal analysis inside the same domain. The models include a first encoder trained to receive input from one or more entities represented in a first modality and to encode the one or more entities in the first modality, such that the first encoder is configured to output a first set of embeddings. The models also include a second encoder trained to receive input from one or more entities represented in the second modality and to encode the one or more entities in the second modality, such that the second encoder is configured to output a second set of embeddings. The models also include a projection layer configured to project the first set of embeddings and the second set of embeddings to a shared contrastive space.

Abstract: A computer implemented method includes receiving an image that includes a type of object, segmenting the object into multiple segments via a trained segmentation machine learning model, and inputting the segments into multiple different attribute extraction models to extract different types of attributes from each of the multiple segments.

Abstract: The present disclosure relates to methods and systems for using large language models to support research activities. The methods and systems include a copilot engine that creates input prompts to provide to the large language model to use in generating responses to input messages. The copilot engine infers an intent of the input messages and sends the intent with the input message in the input prompt to the large language model. The large language model generates different types of responses for different intents.

Abstract: The present disclosure relates to methods and systems for using large language models to support research activities. The methods and systems include a copilot engine that creates input prompts to provide to the large language model to use in generating responses to input messages. The copilot engine infers an intent of the input messages and sends the intent with the input message in the input prompt to the large language model. The large language model generates different types of responses for different intents.

Abstract: The present disclosure relates to extracting entities from a collection of digital content items based on text from within the digital content items. For example, the present disclosure describes a customizable entity extraction system that utilizes a number of models to extract entities, rank entities, and classify certain entities using a combination of rule-based and machine learning approaches. In one or more embodiments, a customizable entity extraction system applies a set of rules to unstructured text of a collection of digital content items to extract and classify a set of entities in connection with a specific domain of interest.

Abstract: This document relates to predicting the impact of documents using a trained machine learning model. For instance, the disclosed implementations can train a gradient-boosted decision tree or neural network to predict impact scores of previously-published documents using features such as author features, journal features, document metadata features, and/or text embeddings representing text from the previously-published documents. Once trained, the machine learning model can be employed to predict impact scores of newly-published documents. The impact scores can be employed for operations such as ranking the newly-published documents in response to a received query.

Abstract: The present disclosure relates to systems and methods for automated phenotypic analysis of microscopic images using a cascade machine learning architecture with iterative active learning. The systems and methods include a cascading flow of data through a plurality of machine learning models from images to specific phenotypic detection. The systems and methods use a feedback loop for continually improving the predictions of the plurality of machine learning models.

Abstract: Methods and apparatuses for providing a machine learning development platform that leverages a collection of reusable machine learning components and a natural language processing (NLP) assistant to reduce development time and reduce compute and data storage resources during the development and testing of machine learning programs are described. The NLP assistant may automatically configure, generate and test a pipeline of selected components from a collection of reusable machine learning components based on user instructions to the NLP assistant, component metadata that includes a natural language description for each component, and component interface information that includes input and output interface schemas for each component. The user instructions may comprise natural language instructions from text and/or audio transcriptions that specify a set of tasks to be performed by the pipeline of selected components.

Abstract: Systems and methods are provided for generating a training dataset for training a molecule embedding module using contrastive learning, wherein the definition of similarity is based on molecular scaffold similarity. For example, systems access a molecular dataset and separate the molecular dataset into positive samples and negative samples. Systems then generate a training dataset comprising the positive samples and negative samples. Systems and methods are also provided for using the trained molecule embedding module to generate molecule embeddings and for building an end-to-end machine learning model configured to perform molecular embedding analysis and molecular property prediction, the model comprising the trained molecule embedding module and a property prediction module.

Abstract: A molecule representation is extracted from a document and associated with the document in a metadata database. For example, an image of a molecular structure may be extracted from a document and stored in the metadata database in a text-based representation such as SMILES. The metadata database may be searched to identify documents that mention a particular molecule. Continuing the example, the metadata database may be searched with a SMILES representation to identify the document and other documents that refer to the same molecule. The metadata database may index documents based on different types of molecule representations, including text-based, image-based, graph-based, name, abbreviation, etc. This allows search over multiple representations of a molecule, improving accuracy and thoroughness. These improvements reduce the time and computational resources needed to search for documents that refer to a particular molecule.

Abstract: A computer implemented method includes rendering a document page as an image; detecting tables, columns, and other associated table objects within the image via one or more table recognition models that model objects in the image as overlapping bounding boxes; transforming the set of objects into a structured representation of the table; extracting data from the objects into the structured representation; and exporting the table into the desired output format.

Abstract: This disclosure describes a machine learning system that includes a contrastive learning based two-tower model for retrieval of relevant chemical reaction procedures given a query chemical reaction. The two-tower model uses attention-based transformers and neural networks to convert tokenized representations of chemical reactions and chemical reaction procedures to embeddings in a shared embedding space. Each tower can include a transformer network, a pooling layer, a normalization layer, and a neural network. The model is trained with labeled data pairs that include a chemical reaction and the text of a chemical reaction procedure for that chemical reaction. New queries can locate chemical reaction procedures for performing a given chemical reaction as well as procedures for similar chemical reactions. The architecture and training of the model make it possible to perform semantic matching based on chemical structures. The model is highly accurate providing an average recall at K=5 of 95.9%.

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 methods and systems for sharing with a plurality of users a chat session that uses large language models to provide responses for input messages received for the chat session. The methods and systems provide access to the chat session to the users and update the chat session in response to any changes made to the chat session by any of the users. The methods and systems allow the users to resume the chat session at a future time using the chat session history.

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: This disclosure describes a machine learning system that includes a contrastive learning based two-tower model for retrieval of relevant chemical reaction procedures given a query chemical reaction. The two-tower model uses attention-based transformers and neural networks to convert tokenized representations of chemical reactions and chemical reaction procedures to embeddings in a shared embedding space. Each tower can include a transformer network, a pooling layer, a normalization layer, and a neural network. The model is trained with labeled data pairs that include a chemical reaction and the text of a chemical reaction procedure for that chemical reaction. New queries can locate chemical reaction procedures for performing a given chemical reaction as well as procedures for similar chemical reactions. The architecture and training of the model make it possible to perform semantic matching based on chemical structures. The model is highly accurate providing an average recall at K=5 of 95.9%.

Abstract: An ensemble machine learning model is used to visualize complex data relationships. Complex data is provided to multiple generator models that each independently create a graph representing relationships in the data. At least one of the generator models is a machine learning model that can be trained with a generator model loss function. The graphs produced by the separate generator models are combined by an ensemble model to create a consensus graph. The ensemble model may be implemented as an edge-selector neural network. The models are trained jointly with a ensemble model loss function that includes the loss functions of the generator models added as regularization terms. A visualization of the ensemble graph is created to aid a user in understanding the complex data relationships.