公开(公告)号：US20240419978A1

公开(公告)日：2024-12-19

申请号：US18738557

申请日：2024-06-10

Applicant: THE TORONTO-DOMINION BANK

Inventor： George Frazer Stein ,  Jesse Cole Cresswell ,  Rasa Hosseinzadeh ,  Yi Sui ,  Brendan Leigh Ross ,  Valentin Victor Villecroze ,  Zhaoyan Liu ,  Anthony Lawrence Caterini ,  Joseph Eric Timothy Taylor ,  Gabriel Loaiza Ganem

IPC: G06N3/09 ,  G06V10/774

Abstract: A variety of generative models are trained that are trained on a reference data set. The generative models are evaluated by candidate metrics to determine the relative rankings of the models as evaluated by the different candidate metrics. Rankings as generated by the models is compared with human evaluation of the generated results as simulated and the candidate metrics that most align with the human evaluation may then be used to automatically evaluate subsequent generative models. The candidate metrics may include various types of encoding models trained for non-generative purposes, such that the selected candidate metric may represent selecting an encoding model that performs well on the generative data.

公开(公告)号：US20230385694A1

公开(公告)日：2023-11-30

申请号：US18202459

申请日：2023-05-26

Applicant: THE TORONTO-DOMINION BANK

Inventor： Jesse Cole Cresswell ,  Brendan Leigh Ross ,  Ka Ho Yenson Lau ,  Junfeng Wen ,  Yi Sui

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Model training systems collaborate on model training without revealing respective private data sets. Each private data set learns a set of client weights for a set of computer models that are also learned during training. Inference for a particular private data set is determined as a mixture of the computer model parameters according to the client weights. During training, at each iteration, the client weights are updated in one step based on how well sampled models represent the private data set. In another step, gradients are determined for each sampled model and may be weighed according to the client weight for that model, relatively increasing the gradient contribution of a private data set for model parameters that correspond more highly to that private data set.

公开(公告)号：US20240330772A1

公开(公告)日：2024-10-03

申请号：US18618757

申请日：2024-03-27

Applicant: THE TORONTO-DOMINION BANK

Inventor： Jesse Cole Cresswell ,  Noël Vouitsis ,  Yi Sui

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A classification model is calibrated with a conformal threshold to determine a known error rate for classifications. Rather than directly use the model outputs, the classification model outputs are processed to a conformal score that is compared with a conformal threshold for determining whether a data sample is a member of a class. When a number of classes for the data sample that pass the conformal threshold for inclusion is a single class, an action associated with the class can confidently be applied with a known error rate. When the number of classes is zero or multiple classes, it may indicate sufficient uncertainty in the model prediction and the data sample may be escalated to another decision mechanism, such as manual review or a more complex classification model.