An online concierge system trains a user interaction model to predict a probability of a user performing an interaction after one or more content items are displayed to the user. This provides a measure of an effect of displaying content items to the user on the user performing one or more interactions. The user interaction model is trained from displaying content items to certain users of the online concierge system and withholding display of the content items to other users of the online concierge system. To train the user interaction model, the user interaction model is applied to labeled examples identifying a user and value based on interactions the user performed after one or more content items were displayed to the user and interactions the user performed when one or more content items were not used.

A cloud network is a complex environment in which hundreds and thousands of users or entities can each host, create, modify, and develop multiple virtual machines. Each virtual machine can have complex behavior unknown to the provider or maintainer of the cloud. Technologies disclosed include methods, systems, and apparatuses to monitor the complex environment to detect network anomalies using machine learning techniques. In addition, techniques to modify and adapt to user feedback are provided allowing the developed models to be tuned for specific use cases, virtual machine types, and users.

An apparatus to facilitate machine learning matrix processing is disclosed. The apparatus comprises a memory to store matrix data one or more processors to execute an instruction to examine a message descriptor included in the instruction to determine a type of matrix layout manipulation operation that is to be executed, examine a message header included in the instruction having a plurality of parameters that define a two-dimensional (2D) memory surface that is to be retrieved, retrieve one or more blocks of the matrix data from the memory based on the plurality of parameters and a register file including a plurality of registers, wherein the one or more blocks of the matrix data is stored within a first set of the plurality of registers.

System and method for relation extraction using adaptive thresholding and localized context pooling (ATLOP). The system includes a computing device, the computing device has a processer and a storage device storing computer executable code. The computer executable code is configured to provide a document; embed entities in the document into embedding vectors; and predict relations between a pair of entities in the document using their embedding vectors. The relation prediction is performed based on an improved language model. Each relation has an adaptive threshold, and the relation between the pair of entities is determined to exist when a logit of the relation between the pair of entities is greater than a logit function of the corresponding adaptive threshold.

A system for use in a vehicle for determining an indication of the type of terrain in the vicinity of the vehicle, the system comprising; means configured to receive sensor output data from at least one sensor on the vehicle; means configured to determine a plurality of parameters in dependence on the sensor output data; a neural network algorithm configured to receive the plurality of parameters; and means configured to execute the neural network algorithm to provide a plurality of outputs corresponding to a plurality of different terrain types, the neural network being further configured to associate the plurality of parameters with one of the plurality of outputs, so as to determine an indication of the terrain type.

A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.

Systems and method for lifelong tag learning. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code is configured to: provide product descriptions and seed tags characterizing products; train a named-entity recognition (NER) model using the product descriptions and the seed tags; predict pseudo tags from the product descriptions using the NER model; calculate confidence scores of the pseudo tags; compare the confidence scores with a threshold, and define the pseudo tags as true tags when the confidence scores are greater than the threshold; add the true tags to the seed tags to obtain updated tags; and repeat the steps of training, predicting, calculating, comparing and adding using the product descriptions and the updated tags, so as to keep updating the updated tags.

A cognitive computing system for enhancing workflow performance in the oil and gas industry, in some embodiments, comprises: neurosynaptic processing logic including multiple electronic neurons operating in parallel; input and output interfaces coupled to the neurosynaptic processing logic; and one or more information repositories accessible to the neurosynaptic processing logic, wherein the neurosynaptic processing logic receives a workflow enhancement request via the input interface, accesses the one or more information repositories to obtain information pertaining to the request, uses said information to perform a probability analysis, produces an option relating to the workflow enhancement request based on said probability analysis, and presents said option via the output interface.

A computer-implemented method comprising: outputting questions to a user via one or more user devices, and receiving back responses to some of the questions from the user via one or more user devices; over time, controlling the outputting of the questions so as to output the questions under circumstances of different values for each of one or more items of metadata, wherein the one or more items of metadata comprise at least a time and/or a location at which a question was output to the user via the one or more user devices; monitoring whether or not the user responds when the question is output with the different metadata values; training the machine learning algorithm to learn a value of each of the items of metadata which optimizes a reward function, and based thereon selecting a time and/or location at which to output subsequent questions.

A system and method for classifying products. A processor generates first and second instances of a first classifier, and trains the instances based on an input dataset. A second classifier is trained based on the input, where the second classifier is configured to learn a representation of a latent space associated with the input. A first supplemental dataset is generated in the latent space, where the first supplemental dataset is an unlabeled dataset. A first prediction is generated for labeling the first supplemental dataset based on the first instance of the first classifier, and a second prediction is generated for labeling the first supplemental dataset based on the second instance of the first classifier. Labeling annotations are generated for the first supplemental dataset based on the first prediction and the second prediction. A third classifier is trained based on at least the input dataset and the annotated first supplemental dataset.