公开(公告)号：US20240152799A1

公开(公告)日：2024-05-09

申请号：US18051364

申请日：2022-10-31

申请人： ADOBE INC.

发明人： Sudhanshu Chanpuriya ,  Ryan A. Rossi ,  Nedim Lipka ,  Anup Bandigadi Rao ,  Tung Mai ,  Zhao Song

IPC分类号： G06N20/00 ,  G06F7/78

CPC分类号： G06N20/00 ,  G06F7/78

摘要： Systems and methods for data augmentation are described. Embodiments of the present disclosure receive a dataset that includes a plurality of nodes and a plurality of edges, wherein each of the plurality of edges connects two of the plurality of nodes; compute a first nonnegative matrix representing a homophilous cluster affinity; compute a second nonnegative matrix representing a heterophilous cluster affinity; compute a probability of an additional edge based on the dataset using a machine learning model that represents a homophilous cluster and a heterophilous cluster based on the first nonnegative matrix and the second nonnegative matrix; and generate an augmented dataset including the plurality of nodes, the plurality of edges, and the additional edge.

公开(公告)号：US12047273B2

公开(公告)日：2024-07-23

申请号：US17671075

申请日：2022-02-14

申请人： ADOBE INC.

发明人： Georgios Theocharous ,  Kai Wang ,  Zhao Song ,  Sridhar Mahadevan

IPC分类号： H04L45/02 ,  H04L41/147

CPC分类号： H04L45/08 ,  H04L41/147

摘要： A control system facilitates active management of a streaming data system. Given historical data traffic for each data stream processed by a streaming data system, the control system uses a machine learning model to predict future data traffic for each data stream. The control system selects a matching between data streams and servers for a future time that minimizes a cost comprising a switching cost and a server imbalance cost based on the predicted data traffic for the future time. In some configurations, the matching is selected using a planning window comprising a number of future time steps dynamically selected based on uncertainty associated with the predicted data traffic. Given the selected matching, the control system may manage the streaming data system by causing data streams to be moved between servers based on the matching.

公开(公告)号：US20230368265A1

公开(公告)日：2023-11-16

申请号：US17743360

申请日：2022-05-12

申请人： Adobe Inc.

发明人： Ryan A. Rossi ,  Aravind Reddy Talla ,  Zhao Song ,  Anup Rao ,  Tung Mai ,  Nedim Lipka ,  Gang Wu ,  Anup Rao

IPC分类号： G06Q30/06

CPC分类号： G06Q30/0631 ,  G06Q30/0629 ,  G06Q30/0643

摘要： Embodiments provide systems, methods, and computer storage media for a Nonsymmetric Determinantal Point Process (NDPPs) for compatible set recommendations in a setting where data representing entities (e.g., items) arrives in a stream. A stream representing compatible sets of entities is received and used to update a latent representation of the entities and a compatibility distribution indicating likelihood of compatibility of subsets of the entities. The probability distribution is accessed in a single sequential pass to predict a compatible complete set of entities that completes an incomplete set of entities. The predicted complete compatible set is provided a recommendation for entities that complete the incomplete set of entities.

公开(公告)号：US20230281680A1

公开(公告)日：2023-09-07

申请号：US17652939

申请日：2022-03-01

申请人： ADOBE INC.

发明人： Michail Mamakos ,  Sridhar Mahadevan ,  Viswanathan Swaminathan ,  Mariette Philippe Souppe ,  Ritwik Sinha ,  Saayan Mitra ,  Zhao Song

IPC分类号： G06Q30/02 ,  G06Q10/06 ,  G06F9/50

CPC分类号： G06Q30/0283 ,  G06Q10/06313 ,  G06F9/5033

摘要： Systems and methods for resource allocation are described. The systems and methods include receiving utilization data for computing resources shared by a plurality of users, updating a pricing agent using a reinforcement learning model based on the utilization data, identifying resource pricing information using the pricing agent, and allocating the computing resources to the plurality of users based on the resource pricing information.

公开(公告)号：US20240273378A1

公开(公告)日：2024-08-15

申请号：US18163624

申请日：2023-02-02

申请人： ADOBE INC.

发明人： Saayan Mitra ,  Arash Givchi ,  Xiang Chen ,  Somdeb Sarkhel ,  Ryan A. Rossi ,  Zhao Song

IPC分类号： G06N3/098 ,  G06N3/092

CPC分类号： G06N3/098 ,  G06N3/092

摘要： Systems and methods for distributed machine learning are provided. According to one aspect, a method for distributed machine learning includes obtaining, by an edge device, a static machine learning model from a hub device, computing, by the edge device, an objective function for a dynamic machine learning model based on a relationship between the dynamic machine learning model and the static machine learning model, and updating, by the edge device, the dynamic machine learning model based on the objective function.

公开(公告)号：US20230261966A1

公开(公告)日：2023-08-17

申请号：US17671075

申请日：2022-02-14

申请人： ADOBE INC.

发明人： Georgios Theocharous ,  Kai Wang ,  Zhao Song ,  Sridhar Mahadevan

IPC分类号： H04L45/00 ,  H04L41/147

CPC分类号： H04L45/08 ,  H04L41/147

摘要： A control system facilitates active management of a streaming data system. Given historical data traffic for each data stream processed by a streaming data system, the control system uses a machine learning model to predict future data traffic for each data stream. The control system selects a matching between data streams and servers for a future time that minimizes a cost comprising a switching cost and a server imbalance cost based on the predicted data traffic for the future time. In some configurations, the matching is selected using a planning window comprising a number of future time steps dynamically selected based on uncertainty associated with the predicted data traffic. Given the selected matching, the control system may manage the streaming data system by causing data streams to be moved between servers based on the matching.

公开(公告)号：US20240144307A1

公开(公告)日：2024-05-02

申请号：US18047421

申请日：2022-10-18

申请人： ADOBE INC.

发明人： Tung Mai ,  Ritwik Sinha ,  Trevor Hyrum Paulsen ,  Xiang Chen ,  William Brandon George ,  Nate Purser ,  Zhao Song

IPC分类号： G06Q30/02

CPC分类号： G06Q30/0204

摘要： One aspect of systems and methods for segment size estimation includes identifying a segment of users for a first time period based on time series data, wherein the time series data includes a series of interactions between users and a content channel and wherein the segment includes a portion of the users interacting with the content channel during the first time period; computing a segment return value for a second time period based on the time series data by computing a first subset and a second subset of the segment, wherein the first subset includes users that interact with the content channel greater than a threshold number of times during a range of the time series data and the second subset comprises a complement of the first subset with respect to the segment; and providing customized content to a user in the segment based on the segment return value.

公开(公告)号：US20230379507A1

公开(公告)日：2023-11-23

申请号：US17749846

申请日：2022-05-20

申请人： Adobe Inc.

发明人： Gang Wu ,  Yang Li ,  Stefano Petrangeli ,  Viswanathan Swaminathan ,  Haoliang Wang ,  Ryan A. Rossi ,  Zhao Song

IPC分类号： H04N19/96 ,  H04N19/91 ,  H04N19/50 ,  H04N19/184 ,  H04N19/182 ,  G06N20/00

CPC分类号： H04N19/96 ,  H04N19/91 ,  H04N19/50 ,  H04N19/184 ,  H04N19/182 ,  G06N20/00

摘要： Embodiments described herein provide methods and systems for facilitating actively-learned context modeling. In one embodiment, a subset of data is selected from a training dataset corresponding with an image to be compressed, the subset of data corresponding with a subset of data of pixels of the image. A context model is generated using the selected subset of data. The context model is generally in the form of a decision tree having a set of leaf nodes. Entropy values corresponding with each leaf node of the set of leaf nodes are determined. Each entropy value indicates an extent of diversity of context associated with the corresponding leaf node. Additional data from the training dataset is selected based on the entropy values corresponding with the leaf nodes. The updated subset of data is used to generate an updated context model for use in performing compression of the image.