Abstract: The present application generally relates to search engines, and more specifically to systems and methods for a search tool for code snippets. Embodiment described herein provide a customized code search system that generates code search results from customized data sources, extract code snippets from the code search results, and presents the code snippets via a user interface. In one embodiment, the search system adopts a machine learning module to generate and highlight search results from different data sources that include code examples, e.g., in a programming language. To improve search efficiency, in response to a code search query, the search system may extract code snippets from search results from relevant sources in a user interface element, such as user selectable panels.

Abstract: The present application generally relates to search engines, and more specifically to systems and methods for a customized search platform that generates and integrates search applications based on input from data sources or application providers.

Abstract: Embodiments described herein provide systems and methods for a customized search platform that provides users control and transparency in their searches. The system may use a ranker and parser to utilize input data and contextual information to identify search applications, sort the search applications, and present search results via user-engageable elements. The system may also use input from a user to personalize and update search results based on a user's interaction with user-engageable elements.

Abstract: Embodiments described herein provide systems and methods for a customized generative AI platform that provides users with a tool to generate various formats of responses to user inputs that incorporate results from searches performed by the generative AI platform. The system may use a neural network to utilize input data and contextual information to identify potential search queries, gather relevant data, sort information, generate text-based responses to user inputs, and present response and search results via user-engageable elements.

Abstract: The present application generally relates to search engines, and more specifically to systems and methods for a search tool for code snippets. Embodiment described herein provide a customized code search system that generates code search results from customized data sources, extract code snippets from the code search results, and presents the code snippets via a user interface. In one embodiment, the search system adopts a machine learning module to generate and highlight search results from different data sources that include code examples, e.g., in a programming language. To improve search efficiency, in response to a code search query, the search system may extract code snippets from search results from relevant sources in a user interface element, such as user selectable panels.

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

Abstract: The present application generally relates to search engines, and more specifically to systems and methods for a search tool for code snippets. Embodiment described herein provide a customized code search system that generates code search results from customized data sources, extract code snippets from the code search results, and presents the code snippets via a user interface. In one embodiment, the search system adopts a machine learning module to generate and highlight search results from different data sources that include code examples, e.g., in a programming language. To improve search efficiency, in response to a code search query, the search system may extract code snippets from search results from relevant sources in a user interface element, such as user selectable panels.

Abstract: Embodiments described herein provide systems and methods for a customized search platform that provides users control and transparency in their searches. The system may use a ranker and parser to utilize input data and contextual information to identify search applications, sort the search applications, and present search results via user-engageable elements. The system may also use input from a user to personalize and update search results based on a user's interaction with user-engageable elements.

Abstract: The present application generally relates to search engines, and more specifically to systems and methods for a customized search platform that generates and integrates search applications based on input from data sources or application providers.

Abstract: The present disclosure provides systems and methods for controllable protein generation. According to some embodiments, the systems and methods leverage neural network models and techniques that have been developed for other fields, in particular, natural language processing (NLP). In some embodiments, the systems and methods use or employ models implemented with transformer architectures developed for language modeling and apply the same to generative modeling for protein engineering.

Abstract: Embodiments described herein provide systems and methods for a customized search platform that provides users control and transparency in their searches. The system may use a ranker and parser to utilize input data and contextual information to identify search applications, sort the search applications, and present search results via user-engageable elements. The system may also use input from a user to personalize and update search results based on a user's interaction with user-engageable elements.

Abstract: Embodiments described herein provide systems and methods for a customized search platform that provides users control and transparency in their searches. The system may use a ranker and parser to utilize input data and contextual information to identify search applications, sort the search applications, and present search results via user-engageable elements. The system may also use input from a user to personalize and update search results based on a user's interaction with user-engageable elements.

Abstract: The present application generally relates to search engines, and more specifically to systems and methods for a search tool for code snippets. Embodiment described herein provide a customized code search system that generates code search results from customized data sources, extract code snippets from the code search results, and presents the code snippets via a user interface. In one embodiment, the search system adopts a machine learning module to generate and highlight search results from different data sources that include code examples, e.g., in a programming language. To improve search efficiency, in response to a code search query, the search system may extract code snippets from search results from relevant sources in a user interface element, such as user selectable panels.

Abstract: The present disclosure provides systems and methods for controllable protein generation. According to some embodiments, the systems and methods leverage neural network models and techniques that have been developed for other fields, in particular, natural language processing (NLP). In some embodiments, the systems and methods use or employ models implemented with transformer architectures developed for language modeling and apply the same to generative modeling for protein engineering.

Abstract: Embodiments described herein provide a flexible controllable summarization system that allows users to control the generation of summaries without manually editing or writing the summary, e.g., without the user actually adding or deleting certain information under various granularity. Specifically, the summarization system performs controllable summarization through keywords manipulation. A neural network model is learned to generate summaries conditioned on both the keywords and source document so that at test time a user can interact with the neural network model through a keyword interface, potentially enabling multi-factor control.

Abstract: Embodiments described herein provide natural language processing (NLP) systems and methods that utilize energy-based models (EBMs) to compute an exponentially-weighted energy-like term in the loss function to train an NLP classifier. Specifically, noise contrastive estimation (NCE) procedures are applied together with the EBM-based loss objectives for training the NLPs.

Abstract: Embodiments described herein provides a training mechanism that transfers the knowledge from a trained BERT model into a much smaller model to approximate the behavior of BERT. Specifically, the BERT model may be treated as a teacher model, and a much smaller student model may be trained using the same inputs to the teacher model and the output from the teacher model. In this way, the student model can be trained within a much shorter time than the BERT teacher model, but with comparable performance with BERT.

Abstract: Embodiments described herein provide systems and methods for a customized generative AI platform that provides users with a tool to generate various formats of responses to user inputs that incorporate results from searches performed by the generative AI platform. The system may use a neural network to utilize input data and contextual information to identify potential search queries, gather relevant data, sort information, generate text-based responses to user inputs, and present response and search results via user-engageable elements.

Abstract: The present application generally relates to search engines, and more specifically to systems and methods for a search tool for code snippets. Embodiment described herein provide a customized code search system that generates code search results from customized data sources, extract code snippets from the code search results, and presents the code snippets via a user interface. In one embodiment, the search system adopts a machine learning module to generate and highlight search results from different data sources that include code examples, e.g., in a programming language. To improve search efficiency, in response to a code search query, the search system may extract code snippets from search results from relevant sources in a user interface element, such as user selectable panels.

Abstract: Approaches for cross-lingual regularization for multilingual generalization include a method for training a natural language processing (NLP) deep learning module. The method includes accessing a first dataset having a first training data entry, the first training data entry including one or more natural language input text strings in a first language; translating at least one of the one or more natural language input text strings of the first training data entry from the first language to a second language; creating a second training data entry by starting with the first training data entry and substituting the at least one of the natural language input text strings in the first language with the translation of the at least one of the natural language input text strings in the second language; adding the second training data entry to a second dataset; and training the deep learning module using the second dataset.