公开(公告)号：US20230131935A1

公开(公告)日：2023-04-27

申请号：US17969505

申请日：2022-10-19

Applicant: THE TORONTO-DOMINION BANK

Inventor： Maksims Volkovs ,  Cheng Chang ,  Guangwei Yu ,  Himanshu Rai ,  Yichao Lu

IPC: G06V10/764 ,  G06V10/77 ,  G06V10/774

Abstract: An object detection model and relationship prediction model are jointly trained with parameters that may be updated through a joint backbone. The offset detection model predicts object locations based on keypoint detection, such as a heatmap local peak, enabling disambiguation of objects. The relationship prediction model may predict a relationship between detected objects and be trained with a joint loss with the object detection model. The loss may include terms for object connectedness and model confidence, enabling training to focus first on highly-connected objects and later on lower-confidence items.

公开(公告)号：US20200074324A1

公开(公告)日：2020-03-05

申请号：US16546134

申请日：2019-08-20

Applicant: THE TORONTO-DOMINION BANK

Inventor： Ga Wu ,  Maksims Volkovs ,  Himanshu Rai

IPC: G06N5/04 ,  G06F17/16

Abstract: A recommendation system models unknown preferences as samples from a noise distribution to generate recommendations for an online system. Specifically, the recommendation system obtains latent user and item representations from preference information that are representations of users and items in a lower-dimensional latent space. A recommendation for a user and item with an unknown preference can be generated by combining the latent representation for the user with the latent representation for the item. The latent user and item representations are learned to discriminate between observed interactions and unobserved noise samples in the preference information by increasing estimated predictions for known preferences in the ratings matrix, and decreasing estimated predictions for unobserved preferences sampled from the noise distribution.