Abstract: Methods and systems for mining minority-class data samples are described. A minority-class mining service receives activations generated by an inner-layer of a client neural network that has been trained to perform a prediction task that involves classification. The minority-class mining service generates a recalibrated activation using a recalibration neural network, and generates an anomaly detector output using an anomaly detector. From the anomaly detector output, a minority-class score is computed for the data sample represented by a received activation. The computed minority-class score is compared against a minority-class threshold to identify a candidate minority-class data sample. The candidate minority-class data sample can then be labeled and added to the training dataset for the client neural network.

Abstract: Systems, methods and computer-readable media for generating, by a first entity, a set of synthetic data samples that represent a corresponding set of original data samples; sending, by the first entity, the set of synthetic data samples for use by a second entity to generate a set of second entity predictions for the set of synthetic data samples using a machine learning (ML) model that has been trained using a second entity dataset; sending, by the first entity, for a third entity, a set of trusted labels corresponding to the set of original data samples; and receiving, by the first entity, from the third entity, valuation information for the second entity dataset that is based on a comparison by the third entity of the set of trusted labels and the set of second entity predictions.

Abstract: The present disclosure provides a method and system for evaluating a machine learning model using an evaluation dataset for the machine learning model. The evaluation dataset includes for each entity in a group of entities: (i) an ordered set of attribute values for the entity, each attribute value corresponding to a respective attribute in a set of attributes that is common for all of the entities in the group of entities, and (ii) an outcome prediction generated for the entity by the machine learning model based on the ordered set of attribute values for the entity, wherein the outcome prediction generated for each entity is either a first outcome or a second outcome. Based on the evaluation dataset, using an optimization process, respective importance values are computed for the attributes, the respective importance values indicating attributes that are most responsible for the machine learning model predicting a first outcome.

Abstract: A multi-functional luggage includes a first frame positioned such that an outer face of the first frame includes elements for engaging with the ground. The luggage further includes a second frame operably connected to the first frame and a third frame operably connected to the second frame using a hinge structure such that the third frame pivots from a first position wherein the second frame and the third frame are contiguous to a second position wherein the third frame and the second frame are partially open in order to expose interior of the third frame and the second frame. The third frame and the second frame form a seat in the second position that is sized to support a human such that a torso of the human rests in the second frame and feet of the human rests in the third frame.

Abstract: Methods and systems for mining minority-class data samples are described. A minority-class mining service receives activations generated by an inner-layer of a client neural network that has been trained to perform a prediction task that involves classification. The minority-class mining service generates a recalibrated activation using a recalibration neural network, and generates an anomaly detector output using an anomaly detector. From the anomaly detector output, a minority-class score is computed for the data sample represented by a received activation. The computed minority-class score is compared against a minority-class threshold to identify a candidate minority-class data sample. The candidate minority-class data sample can then be labeled and added to the training dataset for the client neural network.

Abstract: A multi-functional luggage includes a first frame positioned such that an outer face of the first frame includes elements for engaging with the ground. The luggage further includes a second frame operably connected to the first frame and a third frame operably connected to the second frame using a hinge structure such that the third frame pivots from a first position wherein the second frame and the third frame are contiguous to a second position wherein the third frame and the second frame are partially open in order to expose interior of the third frame and the second frame. The third frame and the second frame form a seat in the second position that is sized to support a human such that a torso of the human rests in the second frame and feet of the human rests in the third frame.

Abstract: Examples provided herein are directed to deploying personalized educational content to student devices. In example implementations, a computing system may communicate with student devices corresponding to students in a classroom setting. The computing system may cause the student devices to obtain educational content from content providers. As a student progresses through a lesson plan by interacting with a student device, the computing system may intelligently deploy educational content that is personalized to the student's academic abilities and needs.