Abstract: Implementations are directed to receiving a plurality of data samples comprising a first set of data samples associated with respective labels and a second set of data samples to be labeled; generating a random forest structure comprising a set of decisions trees, each decision tree including nodes corresponding to the first set of data samples; adding the second set of data samples into each decision tree as additional nodes of each decision tree; merging the set of decision trees to obtain a universal graph, wherein each node corresponds to a data sample; extracting, using a graph embedding algorithm, an embedding feature for each data sample that corresponds to each node included in the universal graph; determining a distance between any pair of two data samples using respective embedding features of the two data samples; and determining a label for each of the second set of data samples using the distance.

Abstract: A device may receive training data that includes datasets associated with natural language processing, and may mask the training data to generate masked training data. The device may train a masked event C-BERT model, with the masked training data, to generate pretrained weights and a trained masked event C-BERT model, and may train an event aware C-BERT model, with the training data and the pretrained weights, to generate a trained event aware C-BERT model. The device may receive natural language text data identifying natural language events, and may process the natural language text data, with the trained masked event C-BERT model, to determine weights. The device may process the natural language text data and the weights, with the trained event aware C-BERT model, to predict causality relationships between the natural language events, and may perform actions, based on the causality relationships.

Abstract: Systems and methods for evaluating reliability of a model are disclosed, including a processor that may include a data augmentor and a model evaluator. The data augmentor may receive a task data pertaining to information related to a pre-defined task to be performed by the model. The data augmentor may augment the task data to obtain an augmented aspect data. The model evaluator may evaluate a trained model based on the augmented aspect data to obtain aspect evaluation metrics. The model may be an artificial intelligence (AI) model that may be trained using the task data. The evaluation may enable to assess performance of the trained model by computing a performance score based on the aspect evaluation metrics. The performance score may help evaluate the reliability of the model in a pre-defined domain.

Abstract: A device may receive training data that includes datasets associated with natural language processing, and may mask the training data to generate masked training data. The device may train a masked event C-BERT model, with the masked training data, to generate pretrained weights and a trained masked event C-BERT model, and may train an event aware C-BERT model, with the training data and the pretrained weights, to generate a trained event aware C-BERT model. The device may receive natural language text data identifying natural language events, and may process the natural language text data, with the trained masked event C-BERT model, to determine weights. The device may process the natural language text data and the weights, with the trained event aware C-BERT model, to predict causality relationships between the natural language events, and may perform actions, based on the causality relationships.

Abstract: A device may receive unlabeled data associated with a particular domain and may select sets of data from the unlabeled data. The device may calculate Gaussian kernel densities and minimum distances for data points in each of the sets of data and may calculate anomaly scores for the data points based on the Gaussian kernel densities and the minimum distances for the data points. The device may train a machine learning model, with the anomaly scores for the data points, to generate a trained machine learning model that determines a single anomaly score for the data points, wherein a plurality of single anomaly scores is determined for the sets of data. The device may calculate a final anomaly score for the unlabeled data based on a combination of the plurality of single anomaly scores and may perform one or more actions based on the final anomaly score.

Abstract: In some implementations, a prediction system may receive a gene regulatory network associated with genes. The prediction system may determine interactions between the genes associated with the gene regulatory network. The prediction system may generate a hyperbolic embedded space based on the gene regulatory network and the interactions between the genes. The prediction system may determine a hyperbolic distance measure based on the hyperbolic embedded space. The prediction system may process the hyperbolic embedded space and the hyperbolic distance measure, with a neural network model, to generate predictions of interactions between the genes. The prediction system may perform one or more actions based on the predictions of interactions between the genes.