A method for categorizing biological structure of interest (BSOI) in digitized images of biological tissues comprises a stage of identifying BSOIs in digitized images and further comprises presenting an image from the plurality of images that comprises at least one BSOI with high level of entropy to a user, receiving from the user input indicative of a category to be associated with the BSOI that had the high level of entropy and updating the cell categories classifier according to the category of the BSOI provided by the user.

A system and method for iteratively training a neural network are provided. The system and method may include extracting a subset of labeled data points from a pool set of labeled data points; populating, an anchor set of labeled data points with the extracted subset of labeled data points; using the anchor set of labeled data points as labeled inputs to partially train the neural network; selectively swapping, at least some of the labeled data points in the anchor set with at least some of the remaining labeled data points in the pool set; and, retraining the neural network using the anchor set of labeled data points as labeled inputs to the neural network.

Systems, devices, computer-implemented methods, and/or computer program products that facilitate event schema induction from unstructured or semi-structured data. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a schema component and a retrieval component. The schema component can derive an event schema for a document corpus using parsing results obtained from the document corpus. The retrieval component can populate a response to a query with a document of the document corpus using events extracted from the query and the document using the event schema.

Embodiments of the present invention provide an approach for dynamically enhancing supervised learning using factor modification based on parsing user input. A user selects an object being displayed incorrectly and provides input as to the reason. The user input is parsed to derive a factor that is contributing to the false outcome. The factor is dynamically altered resulting in a decision path that produces a positive outcome. The change is sent to a model or application owner for final validation and refined training of the machine learning model.

A method for training a language model for multimodal dialog includes generating a first token sequence by tokenizing one or more text messages included in multimodal dialog data, generating a second token sequence by inserting, based on an appearance position of an image included in the multimodal dialog data, one or more image presence tokens and one or more image non-presence tokens into the first token sequence, and replacing one or more tokens among a plurality of tokens generated by the tokenization with a mask token, generating one or more feature vectors for the image, and training an artificial neural network-based language model to predict, based on the second token sequence and the one or more feature vectors, an appearance position of the image in the multimodal dialog data.

A method for training a language model for multimodal dialog includes generating a first token sequence by tokenizing one or more text messages included in multimodal dialog data, generating a second token sequence by inserting, based on an appearance position of an image included in the multimodal dialog data, one or more image presence tokens and one or more image non-presence tokens into the first token sequence, and replacing one or more tokens among a plurality of tokens generated by the tokenization with a mask token, generating one or more feature vectors for the image, and training an artificial neural network-based language model to predict, based on the second token sequence and the one or more feature vectors, an appearance position of the image in the multimodal dialog data.

A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

The present application relates to artificial intelligence, and discloses a text classification method, including: preprocessing original text data to obtain a text vector; matching a tag to the text vector to obtain a tagged text vector and an untagged text vector; inputting the tagged text vector into a BERT model to obtain a word vector feature; training the untagged text vector with a convolution neural network model according to the word vector feature to obtain a virtually tagged text vector; and using a random forest model to perform multi-tag classification on the tagged text vector and the virtually tagged text vector to obtain a text classification result. The present application also provides a text classification apparatus and a computer-readable storage medium. The present application can realize accurate and efficient text classification.

The present application relates to artificial intelligence, and discloses a text classification method, including: preprocessing original text data to obtain a text vector; matching a tag to the text vector to obtain a tagged text vector and an untagged text vector; inputting the tagged text vector into a BERT model to obtain a word vector feature; training the untagged text vector with a convolution neural network model according to the word vector feature to obtain a virtually tagged text vector; and using a random forest model to perform multi-tag classification on the tagged text vector and the virtually tagged text vector to obtain a text classification result. The present application also provides a text classification apparatus and a computer-readable storage medium. The present application can realize accurate and efficient text classification.

A method of improving robustness of a deep neural network (DNN), the method including: applying a coverage metric to a trained DNN based on a test set to determine test set adequacy; monitoring a performance of the trained DNN; based on the performance, applying new data to the trained DNN; applying a novelty metric to an output of the trained DNN based on the applied new data to identify a subset of the applied new data in response to determining whether new features are generated; and identifying the subset of the applied new data.