Abstract: A linguistic system includes a controller. The controller may be configured to receive a query and document, tokenize the query into a sequence of query tokens and tokenize the document into a sequence of document tokens, generate a matrix of token pairs for each of the query and the document tokens, retrieve for each entry in the matrix of token pairs, a precomputed similarity score produced by a neural conditional translation probability network, wherein the neural network has been trained in a ranking task using a corpus of paired queries and respective relevant documents, produce a ranking score for each document with respect to each query via a product-of-sum aggregation of each of the similarity scores for the respective query; and output the document and associated ranking score of the document.

Abstract: A computer-implemented method for training a machine-learning network. The method includes receiving an input data from a sensor, wherein the input data is indicative of image, radar, sonar, or sound information, generating an input data set utilizing the input data, wherein the input data set includes perturbed data, sending the input data set to a robustifier, wherein the robustifier is configured to clean the input data set by removing perturbations associated with the input data set to create a modified input data set, sending the modified input data set to a pretrained machine learning task, training the robustifier to obtain a trained robustifier utilizing the modified input data set, and in response to convergence of the trained robustifier to a first threshold, output the trained robustifier.

Abstract: A linguistic system includes a controller. The controller may be configured to receive a query and document, tokenize the query into a sequence of query tokens and tokenize the document into a sequence of document tokens, generate a matrix of token pairs for each of the query and the document tokens, retrieve for each entry in the matrix of token pairs, a precomputed similarity score produced by a neural conditional translation probability network, wherein the neural network has been trained in a ranking task using a corpus of paired queries and respective relevant documents, produce a ranking score for each document with respect to each query via a product-of-sum aggregation of each of the similarity scores for the respective query; and output the document and associated ranking score of the document.