Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images; identifying a foreground of the one or more digital images; analyzing the foreground of the one or more digital images to identify a skin region in the foreground of the one or more digital images; when the skin region is identified, clustering a non-skin remainder of the foreground of the one or more digital images into one or more clusters; extracting one or more patches of the one or more digital images from the one or more clusters of the foreground of the one or more digital images; determining one or more scores for the one or more patches of the one or more digital images; and coordinating displaying a patch of the one or more patches on an electronic display based on the one or more scores for the one or more patches. Other embodiments are disclosed herein.

Abstract: Disclosed embodiments provide glitch prediction based on machine learning algorithms in mixed analog and digital systems, particularly directed to digital microelectromechanical (MEMS) multipath acoustic sensors or microphones, which allow seamless, low latency gain changes without audible artifacts or interruptions in the audio output signal.

Abstract: A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations comprising: training a visual similarity model to output one or more vector representations in a vector space; determining one or more nearest neighbors based on embeddings nearest to a respective embedding in the vector space for the item; ranking the one or more nearest neighbors for the item in an order; re-ranking the order of the one or more nearest neighbors of the item into a re-ranked order; inserting, into a display of the item, the one or more nearest neighbors of the item; and facilitating altering a graphical user interface (GUI) to convey the display of the item. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: training a visual similarity model; determining one or more nearest neighbors for an item using the visual similarity model, as trained; ranking the one or more nearest neighbors using results of the visual similarity model, as trained; re-ranking the one or more nearest neighbors using a learning-to-rank algorithm; and facilitating altering a graphical user interface (GUI) displaying the item based upon the one or more nearest neighbors, as re-ranked. Other embodiments are disclosed herein.

Abstract: A system for retrieving products in response to a customer query includes a computing device configured to obtain query information characterizing a query initiated by the customer on an ecommerce marketplace and to determine embedding-based search results comprising a first list of items. The computing device is also configured to obtain legacy search results comprising a second list of items and to blend the embedding-based search results with the legacy search results to obtain blended search results. The computing device is also configured to send the blended search results to the customer.

Abstract: A system including one or more processors and one or more non-transitory computer-readable storage media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: training a neural network detection model with a training dataset comprising synthetic training images by: using a transformation algorithm to create the synthetic training images by appending edge case training images to one or more compliant images; receiving, at the neural network detection model, as trained, at least one image; and determining, using the neural network detection model, as trained, whether the at least one image comprises non-compliant content. Other embodiments are disclosed herein.

Abstract: Disclosed embodiments provide glitch prediction based on machine learning algorithms in mixed analog and digital systems, particularly directed to digital microelectromechanical (MEMS) multipath acoustic sensors or microphones, which allow seamless, low latency gain changes without audible artifacts or interruptions in the audio output signal.

Abstract: A system can include one or more processors and one or more non-transitory computer-readable storage media storing computing instructions configured to run on the one or more processors and perform: generating a training dataset for training a neural network detection model; identifying, using the neural network detection model, as trained, the non-compliant content in the synthetic training images; receiving, at the neural network detection model, at least one image; and utilizing the neural network detection model to determine whether the at least one image comprises the non-compliant content. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving one or more digital images; identifying a foreground of the one or more digital images; analyzing the foreground of the one or more digital images to identify a skin region in the foreground of the one or more digital images; when the skin region is identified, clustering a non-skin remainder of the foreground of the one or more digital images into one or more clusters; extracting one or more patches of the one or more digital images from the one or more clusters of the foreground of the one or more digital images; determining one or more scores for the one or more patches of the one or more digital images; and coordinating displaying a patch of the one or more patches on an electronic display based on the one or more scores for the one or more patches. Other embodiments are disclosed herein.

Abstract: Some embodiments include systems and methods for recall estimation. An exemplary method comprises determining reservoir sampling and size of labeling from each strata; determining recall and variance for each strata; determining sum of sample size for a time period; and determining reservoir sampling for each strata and label the sample items. Other embodiments are described.

Abstract: Disclosed embodiments provide flexible performance, high dynamic range, microelectromechanical (MEMS) multipath digital microphones, which allow seamless, low latency transitions between audio signal paths without audible artifacts over interruptions in the audio output signal. Disclosed embodiments facilitate performance and power saving mode transitions maintaining high dynamic range capability.

Abstract: This application relates to apparatus and methods for ranking search results. In some examples, a computing device receives item data identifying items for a search query provided by a user. The computing determines, for each item, a first value based on a relevance of the item to the search query. The computing device also determines user engagement data for the user, where the user engagement data identifies engagements by the user on a webpage. The computing device determines, for each item, a second value based on the user engagement data. In some examples, the computing device determines the first values and second values by executing one or more machine learning models. The computing device further determines a ranking of the plurality of items based on the first values and the second values. The computing may transmit the ranking of the plurality of items, such as to a web server.

Abstract: Disclosed embodiments provide flexible performance, high dynamic range, microelectromechanical (MEMS) multipath digital microphones, which allow seamless, low latency transitions between audio signal paths without audible artifacts over interruptions in the audio output signal. Disclosed embodiments facilitate performance and power saving mode transitions maintaining high dynamic range capability.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of providing a semi-supervised learning abstraction model that includes an API; receiving, via the API, pre-training parameters at least identifying (a) a first set of unlabeled images and (b) an encoder model selected from the plurality of encoder models; executing a pre-training procedure that trains the encoder model using the first set of unlabeled images; receiving, via the API, supervised training parameters at least identifying (a) a second set of labeled images and (b) the encoder model that is pre-trained using the pre-training procedure; executing a supervised training procedure that further trains the encoder model using the second set of labeled images; and storing a encoder model checkpoint for the encoder model. Other embodiments are disclosed herein.

Abstract: A system for retrieving products in response to a customer query includes a computing device configured to obtain query information characterizing a query initiated by the customer on an ecommerce marketplace and to determine embedding-based search results comprising a first list of items. The computing device is also configured to obtain legacy search results comprising a second list of items and to blend the embedding-based search results with the legacy search results to obtain blended search results. The computing device is also configured to send the blended search results to the customer.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of receiving a digital image comprising multiple items; determining an embedding for the digital image using a machine learning algorithm trained on one or more combined digital images, the combined digital image comprising one or more annotated digital images; identifying an item of the multiple items in the digital image; and facilitating an alteration of a GUI on an electronic device in response to identifying the item in the digital image.

Abstract: A system for use in monitoring an operation of a classification model in generating estimated labels for item records is described herein. The system receives a current labeling budget value including a number of trusted labels available for use in a labeling operation, determines a current selection probability for each item record included in an item list as a function of the current labeling budget value, selects a plurality of item records from the item list as a function of each corresponding current selection probability, and generates a sampling list including the selected item records. The system determines a risk measurement value associated with the classification model indicating an accuracy of the estimated labels as compared to trusted labels associated with the item records.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: training a visual similarity model; determining one or more nearest neighbors for an item using the visual similarity model, as trained; ranking the one or more nearest neighbors using results of the visual similarity model, as trained; re-ranking the one or more nearest neighbors using a learning-to-rank algorithm; and facilitating altering a graphical user interface (GUI) displaying the item based upon the one or more nearest neighbors, as re-ranked. Other embodiments are disclosed herein.

Abstract: This application relates to apparatus and methods for ranking search results. In some examples, a computing device receives item data identifying items for a search query provided by a user. The computing determines, for each item, a first value based on a relevance of the item to the search query. The computing device also determines user engagement data for the user, where the user engagement data identifies engagements by the user on a webpage. The computing device determines, for each item, a second value based on the user engagement data. In some examples, the computing device determines the first values and second values by executing one or more machine learning models. The computing device further determines a ranking of the plurality of items based on the first values and the second values. The computing may transmit the ranking of the plurality of items, such as to a web server.

Abstract: A product image evaluation system. In embodiments, the system comprises or interacts with a product database comprising a product information record that comprises a product identifier and a product category for a product, and an image database comprising a plurality of candidate images for the product. In embodiments the image database can comprise images received from a plurality of different sources. The system can comprise a parameterized grouping engine configured to separate images into groups of similar images, an image selector configured to select one or more images from each group, and an image sorter configured to determine an order of the selected images. Embodiments can distill the superset of all available images to provide a set of images that are “sufficiently different” from each other and satisfy quality requirements. As a result, no images containing unique information are left behind, and images containing duplicate or irrelevant information are discarded.