Abstract: Methods, systems, and storage media for classifying content across media formats based on weak supervision and cross-modal training are disclosed. The system can maintain a first feature classifier and a second feature classifier that classifies features of content having a first and second media format, respectively. The system can extract a feature space from a content item using the first feature classifier and the second feature classifier. The system can apply a set of content rules to the feature space to determine content metrics. The system can correlate a set of known labelled data to the feature space to construct determinative training data. The system can train a discrimination model using the content item and the determinative training data. The system can classify content using the discrimination model to assign a content policy to the second content item.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for retrieving and using contextual data from previous conversation sessions in conversational searches. In one aspect, a method includes receiving a first query for a first user session, determining that the first query refers to one or more tags in a first repository, the first repository associating respective identifiers to respective tags, each identifier representing a corresponding user session, determining one or more particular identifiers associated with the one or more tags in the first repository, retrieving particular contextual data associated with the determined particular identifiers in a second repository, the second repository associating respective identifiers to respective contextual data associated with corresponding user sessions represented by the respective identifiers, and performing an action responsive to the first query based on the retrieved particular contextual data.

Abstract: A first computing device captures audio data via an audio capture device. The audio data comprises probe signals from second computing devices located within the same area. The first computing device and the second computing devices are connected to a teleconference session. Based on the audio data, the first computing device generates a first encoding of the probe signals received. The first computing device receives a second encoding of probe signals received at a second computing device. The first computing device makes a determination that a degree of similarity between the first encoding and the second encoding is greater than or equal to a threshold degree of similarity. The first computing device generates co-location information identifying the second computing device as a co-located device that is located within the same area as the first computing device.

Abstract: Techniques are described herein for translating source code using sparse-self attention. In various implementations, a source code snippet in a first programming language may be processed to obtain graph(s) representing snippet tokens, and relationships therebetween. Based on the graph(s), a subset of snippet token pairs may be identified from a superset of all possible token pairs in the source code snippet. Each token pair of the subset may include snippet tokens that are represented by nodes connected by one or more edges of the one or more graphs. A self-attention network of a translation machine learning model may be adapted to sparsely attend across the identified subset of token pairs. The source code snippet may then be processed based on the adapted translation machine learning model to generate a translation of the source code snippet in the second programming language.

Abstract: The present disclosure generally relates to a method and a hardware and software system for collecting data from retail point of sale. The hardware connects to a wide variety of barcode scanners and receipt printers. Collected data is transmitted to a cloud-based infrastructure backend system using a wireless internet connection built into the hardware. The collected data can be processed to extract item-level transaction information, and estimate the price and in-stock status of items in particular shops. A search engine can be provided using this data, which allows a customer to discover which nearby shop has an item of interest in stock. A targeted advertising system that complements this search service is also presented. The data can be used by retailers and manufacturers for business analysis and optimization.

Abstract: Implementations set forth herein relate to employing dynamic regulations for governing responsiveness of multiple automated assistant devices, and specifically the responsiveness an automated assistant to a given spoken utterance that has been acknowledged by two or more of the assistant devices. The dynamic regulations can be context-dependent and adapted over time in order that the automated assistant can accommodate assistant interaction preferences that may vary from user to user. For instance, a spoken utterance such as “stop,” may be intended to affect different assistant actions based on a context in which the user provided the spoken utterance. The context can refer to a location of the user relative to other rooms in a home, a time of day, a user providing the spoken utterance, an arrangement of the assistant devices within a home, and/or a state of each device in the home.

Abstract: Techniques are disclosed that enable training a meta-learning model, for use in causing a robot to perform a task, using imitation learning as well as reinforcement learning. Some implementations relate to training the meta-learning model using imitation learning based on one or more human guided demonstrations of the task. Additional or alternative implementations relate to training the meta-learning model using reinforcement learning based on trials of the robot attempting to perform the task. Further implementations relate to using the trained meta-learning model to few shot (or one shot) learn a new task based on a human guided demonstration of the new task.

Abstract: Methods, systems, and apparatus for receiving user input that invokes digital assistant functionality; obtaining screen data indicating content displayed on a screen of the computing device; determining a classification for an entity referenced in the content indicated by the screen data; determining a suggested request that refers to the entity based on the determined classification; and providing the suggested request in response to receiving the user input that invokes the digital assistant functionality.

Abstract: Techniques enable an automatic adjustment of a muted response setting of an automated assistant based on a determination of an expectation by a user to hear an audible response to their query, despite the muted setting. Determination of the expectation may be based on historical, empirical data uploaded from multiple users over time for a given response scenario. For example, the system may determine from the historical data that a certain type of query has been associated with a user both repeating their query and increasing a response volume setting within a given timeframe. Metrics may be generated, stored, and invoked in response to attributes associated with identifiable types of queries and query scenarios. Automated response characteristics meant to reduce inefficiencies may be associated with certain queries that can otherwise collectively burden network bandwidth and processing resources.

Abstract: Systems and methods for text-to-speech with novel speakers can obtain text data and output audio data. The input text data may be input along with one or more speaker preferences. The speaker preferences can include speaker characteristics. The speaker preferences can be processed by a machine-learned model conditioned on a learned prior distribution to determine a speaker embedding. The speaker embedding can then be processed with the text data to generate an output that includes audio data descriptive of the text data spoken by a novel speaker.

Abstract: User attribute data associated with a user may be used to resolve unresolved entities in connection with the performance of computer-based actions, e.g., actions resulting from action queries generated based at least in part on content being accessed on a computing device.

Abstract: Methods and apparatus related to determining reply content for a reply to an electronic communication. Some implementations are directed generally toward analyzing a corpus of electronic communications to determine relationships between one or more original message features of “original” messages of electronic communications and reply content that is included in “reply” messages of those electronic communications. Some implementations are directed generally toward providing reply text to include in a reply to a communication based on determined relationships between one or more message features of the communication and the reply text.

Abstract: A user device (UE) capable of operating in dual-mode connectivity (DC) with a master node (MN) and a secondary node (SN) detects a low-power condition of a battery of the UE. In response, the user device prevents the UE from operating in DC with the SN, so that the UE and the MN are configured to operate in single connectivity.

Abstract: Methods, systems, and apparatus including computer programs encoded on a computer storage medium, for retrieving image search results using embedding neural network models. In one aspect, an image search query is received. A respective pair numeric embedding for each of a plurality of image-landing page pairs is determined. Each pair numeric embedding is a numeric representation in an embedding space. An image search query embedding neural network processes features of the image search query and generates a query numeric embedding. The query numeric embedding is a numeric representation of the image search query in the same embedding space. A subset of the image-landing page pairs having pair numeric embeddings that are closest to the query numeric embedding of the image search query in the embedding space are identified as first candidate image search results.

Abstract: Techniques are disclosed that enable the generation of a content agent based on content parameter(s) determined from an initial user request for content as well as a dialog session to further refine the request for content. Various implementations include using the content agent to render additional content responsive to an additional user request. Additional or alternatively implementations include using the content agent to proactively render content to the user.

Abstract: Methods, systems, and apparatus for recognizing objects and providing content related to the recognized objects are described. In one aspect, a method includes detecting presence of one or more objects depicted in a viewfinder of a camera of the mobile device. In response to detecting the presence of the one or more objects, image data representing the one or more objects is sent to a content distribution system that selects content related to objects depicted in images. A location of each of the one or more objects in the viewfinder of the camera is tracked while waiting to receive content from the content distribution system. Content related to the one or more objects is received from the content distribution system. A current location of each object in the viewfinder is determined and the content related to the object is presented within the viewfinder at the current location of the object.

Abstract: Systems, methods, and computer readable media related to information retrieval. Some implementations are related to training and/or using a relevance model for information retrieval. The relevance model includes an input neural network model and a subsequent content neural network model. The input neural network model and the subsequent content neural network model can be separate, but trained and/or used cooperatively. The input neural network model and the subsequent content neural network model can be “separate” in that separate inputs are applied to the neural network models, and each of the neural network models is used to generate its own feature vector based on its applied input. A comparison of the feature vectors generated based on the separate network models can then be performed, where the comparison indicates relevance of the input applied to the input neural network model to the separate input applied to the subsequent content neural network model.

Abstract: Methods and devices are presented for transforming a layout of a densely packed grid of micro-LED light emitters to a layout of a square rectilinear pixel grid to achieve compatibility with hardware and software used in imaging and display technologies. In particular, a pattern of regular hexagonal emitter cells for fabrication on a III-nitride substrate can be transformed to a square pixel array of irregular hexagonal trichrome pixels that are readily addressable. Separation between adjacent trichrome pixels, and between their constituent emitters, can be established for overlay tolerance, while maintaining a cell packing density of about 70% and a pixel pitch of about 4.0 ?m. Wavelength and quantum efficiency properties are shown to depend on optical current density, which can be determined by the emitter area specified in the grid layout.

Abstract: Methods, systems, and apparatus for efficient image analysis. In some aspects, a system includes a camera configured to capture images, one or more environment sensors configured to detect movement of the camera, a data processing apparatus, and a memory storage apparatus in data communication with the data processing apparatus. The data processing apparatus can access, for each of a multitude of images captured by a mobile device camera, data indicative of movement of the camera at a time at which the camera captured the image. The data processing apparatus can also select, from the images, a particular image for analysis based on the data indicative of the movement of the camera for each image, analyze the particular image to recognize one or more objects depicted in the particular image, and present content related to the one or more recognized objects.