Capital One Services, LLC

Taylor Turner

Michael Davis

Jeremy Edward Goodsitt

Galen Rafferty

Samuel Sharpe

Brian Chandler Barr

Kenny Bean

Justin Au-yeung

Owen Reinert

Tyler Farnan

Systems and methods are disclosed herein for generating updated descriptions of items based on analyzing candidate embeddings of semantic representations of item descriptions. The system may obtain a text file describing an item. The system may provide the text file to a generative language model to generate semantic representations of the text file. The system may generate, based on the text file, candidate embeddings in an embedding space. The system may obtain embeddings associated with existing items. The system may determine subsets of the embeddings within a threshold distance. The system may compare the subsets. The system may determine attributes associated with a candidate embedding based on the comparison. The system may generate an updated text file based on the attributes.

Systems and methods for updating textual item descriptions using an embedding space

Methods and systems for scalable dataset content embedding for improved searchability. For example, the system may retrieve a first dataset from a first data source. The system may generate a first data profile of the first dataset. The system may generate a latent index of the first data profile based on processing the first data profile using a first embedding algorithm. The system may receive, via a user interface, a first request for a first text string. The system may generate an embedded request corresponding to the first request based on processing the first text string using the first embedding algorithm. The system may process the embedded request using the latent index. The system may generate for display, in the user interface, a result based on processing the embedded request using the latent index.

Systems and methods for scalable dataset content embedding for improved database searchability

Systems and methods for novel uses and/or improvements to data labeling applications, particularly data labeling applications involving sensitive data. As one example, systems and methods are described herein for preventing sensitive data leakage, using weak learner libraries, during label propagation.

Systems and methods for preventing sensitive data leakage during label propagation

In some aspects, a computing system obtain a first dataset including a set of original data samples. The computing system may generate a key that indicates a location within the first dataset where spurious data should be stored. The computing system may determine a modified value associated with a first data sample of the set of original data samples, where the modified value causes a machine learning model to generate output that does not match a label associated with the first data sample. Based on the first data sample, the computer system may generate a spurious data sample comprising the modified value. Based on the key, the computer system may add the spurious data sample to the first dataset. In some aspects, based on a request for the first dataset, the computing system may remove the spurious data sample from the first dataset.

Generation of effective spurious data for model degradation

In some aspects, a computing system may iterate between adding spurious data to the dataset and training a model on the dataset. If the model's performance has not dropped by more than a threshold amount, then additional spurious data may be added to the dataset until the desired amount of performance decrease has been achieved. the computing system may determine the amount of impact each feature has on a model's output. The computing system may generate a spurious data sample by modifying values of features that are more impactful than other features. The computing system may repeatedly modify the spurious data that is stored in a dataset. If a cybersecurity incident occurs (e.g., the dataset is stolen or leaked), the system may identify when the cybersecurity incident took place based on the spurious data that is stored in the dataset.

Layered cybersecurity using spurious data samples

Disclosed embodiments may include a system for executing programmable macros based on external device interactions. The system may include one or more processors, and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to execute programmable macros based on external device interactions. The system may receive a device identification of an external device. The system may receive a rule associated with the external device, wherein the rule comprises a triggering event and transfer instructions. The system may receive, from the external device, device data. Responsive to determining, from the device data, that the triggering event has occurred, the system may execute the transfer instructions.

Systems and methods for executing programmable macros based on external device interactions

In some aspects, a computing system obtain a first dataset including a set of original data samples and a first set of spurious data samples. Based on a time period expiring, the computing system may replace the first set of spurious data samples in the first dataset with a second set of spurious data samples. The computing system may obtain an indication that a second dataset is available via a third-party computing device. Based on a determination that a subset of samples of the second dataset correspond to the first set of spurious data samples, the computing system may determine a time window in which an incident occurred. As an example, the time window may be determined to correspond to a time before the first set of spurious data samples were replaced with the second set of spurious data samples.

Spurious-data-based detection related to malicious activity

In some implementations, a device may obtain a first document set associated with a first code repository. The device may generate a first embedding set of one or more embeddings for respective documents included in the first document set. The device may obtain a second embedding set of one or more embeddings for respective documents included in a second document set associated with a second code repository. The device may compare the first embedding set to the second embedding set. The device may generate a code repository similarity score that indicates a similarity between the first code repository and the second code repository. The device may perform, based on the code repository similarity score satisfying a threshold, an action associated with the first code repository and/or the second code repository.

Comparing code repositories

Methods and systems are described for novel uses and/or improvements to federated learning. As one example, methods and systems are described for improving the applicability of federated learning across various applications and increasing the efficiency of training a global model through federated learning. As another example, methods and systems are described for ensuring comprehensive training data is available to models assigned by the federated learning server. Additionally, methods and systems are described for improving the rate of training a global model through federated learning.

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Richmond, VA, United States of America

Taylor Turner