Abstract: Example embodiments of aspects of the present disclosure provide an example computer-implemented method for improved prompting of a machine-learned model. The example method can include obtaining an instructive sequence descriptive of an instructive query, an instructive response, and an instructive trace of intermediate states from the instructive query to the instructive response. The example method can include inputting, to a machine-learned model, the instructive sequence and an operative query, wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence. The example method can include generating, using the machine-learned model and responsive to the operative query, an operative response.

Abstract: An example method for pretraining a machine-learned model is provided. The example method includes obtaining a plurality of different combinations of configuration parameters of a pretraining objective framework. The example method includes generating, using the pretraining objective framework, a plurality of corrupted training examples from one or more training examples, wherein the plurality of corrupted training examples are respectively generated according to the plurality of different combinations. The example method includes inputting the plurality of corrupted training examples into the machine-learned model, wherein the machine-learned model is configured to generate uncorrupted subportions corresponding to corrupted subportions of the corrupted training examples. The example method includes obtaining, from the machine-learned model, a plurality of outputs respectively generated by the machine-learned model based on the plurality of corrupted training examples.

Abstract: Methods and apparatus for associating an entity with at least one search query. Some implementations are directed to methods and apparatus for identifying multiple queries associated with an entity and identifying one or more of the queries as an entity search query that provides desired search results for the entity. Some implementations are directed to methods and apparatus for identifying a particular entity and, in response to identifying the particular entity, identifying an entity search query corresponding to the particular entity.

Abstract: Provided are systems and methods for predicting machine learning model performance from the model parameter values, including for use in making improved decisions with regard to early stopping of training procedures. As one example, the present disclosure discusses the prediction of the accuracy (e.g., relative to a defined task and testing dataset such as a computer vision task) of trained neural networks (e.g., convolutional neural networks (CNNs)), using only the parameter values (e.g., the values of the network's weights) as inputs. As such, one example aspect of the present disclosure is directed to computing systems that include and use a machine-learned performance prediction model that has been trained to predict performance values of machine-learned models based on their parameter values (e.g., weight values and/or hyperparameter values).

Abstract: Methods and apparatus for associating an entity with at least one search query. Some implementations are directed to methods and apparatus for identifying multiple queries associated with an entity and identifying one or more of the queries as an entity search query that provides desired search results for the entity. Some implementations are directed to methods and apparatus for identifying a particular entity and, in response to identifying the particular entity, identifying an entity search query corresponding to the particular entity.

Abstract: Methods and apparatus for associating an entity with at least one search query. Some implementations are directed to methods and apparatus for identifying multiple queries associated with an entity and identifying one or more of the queries as an entity search query that provides desired search results for the entity. Some implementations are directed to methods and apparatus for identifying a particular entity and, in response to identifying the particular entity, identifying an entity search query corresponding to the particular entity.

Abstract: Methods and apparatus for associating an entity with at least one search query. Some implementations are directed to methods and apparatus for identifying multiple queries associated with an entity and identifying one or more of the queries as an entity search query that provides desired search results for the entity. Some implementations are directed to methods and apparatus for identifying a particular entity and, in response to identifying the particular entity, identifying an entity search query corresponding to the particular entity.

Abstract: Methods and apparatus for associating an entity with at least one search query. Some implementations are directed to methods and apparatus for identifying multiple queries associated with an entity and identifying one or more of the queries as an entity search query that provides desired search results for the entity. Some implementations are directed to methods and apparatus for identifying a particular entity and, in response to identifying the particular entity, identifying an entity search query corresponding to the particular entity.