Method of training model and method of determining asset valuation

Patent metadata

Field	Value
Publication number	US-2023127699-A1
Application number	US-202218088872-A
Country	US
Kind code	A1
Filing date	Dec 27, 2022
Priority date	Dec 29, 2021
Publication date	Apr 27, 2023
Grant date	—

Abstract

A method of training a model, a method of determining an asset valuation, a device, a storage medium, and a program product, which relate to a field of artificial intelligence, in particular to fields of deep learning and natural language understanding. A specific implementation can include: determining an event-level representation according to a first set of feature data; performing a multi-task learning for a first model according to the event-level representation, to obtain first price distribution data, and transmitting the first price distribution data to a central server; determining a first intra-region representation according to a second set of feature data; adding a noise signal to the first intra-region representation, and transmitting the noised intra-region representation to a client; and adjusting a parameter of the first model according to a noised parameter gradient in response to the noised parameter gradient being received from the central server.

First claim

What is claimed is:

1 . A method of training a model, the method comprising:
determining an event-level representation according to a first set of feature data; performing a multi-task learning for a first model according to the event-level representation, so as to obtain first price distribution data, and transmitting the first price distribution data to a central server; determining a first intra-region representation according to a second set of feature data; adding a noise signal to the first intra-region representation to obtain a noised intra-region representation, and transmitting the noised intra-region representation to a client; and adjusting a parameter of the first model according to a noised parameter gradient in response to the noised parameter gradient being received from the central server.

2 . The method according to claim 1 , further comprising:
acquiring a sensitivity and a differential privacy parameter; calculating a first parameter according to the sensitivity and the differential privacy parameter; sampling from a uniformly distributed sample space to obtain a second parameter; and calculating a noise value of the noise signal according to the first parameter and the second parameter.

3 . The method according to claim 2 , wherein the calculating a first parameter according to the sensitivity and the differential privacy parameter comprises:
calculating the first parameter according to:

b
=

Δ
⁢
f

ε

where b represents the first parameter, Δf represents the sensitivity, and ϵ represents the differential privacy parameter.

4 . The method according to claim 2 , wherein the calculating a noise value of the noise signal according to the first parameter and the second parameter comprises:
calculating the noise value according to:
f −1 =−b· sign(α)·ln(1−2√|α|)

where f −1 represents the noise value, b represents the first parameter, and α represents the second parameter.

5 . The method according to claim 1 , wherein the determining an event-level representation according to a first set of feature data comprises:
determining a transaction event graph according to the first set of feature data; and performing a representation learning by using the transaction event graph, so as to obtain the event-level representation.

6 . The method according to claim 5 , wherein the first set of feature data comprises:
asset profile features and temporal features of a plurality of transaction events; and
wherein the determining a transaction event graph according to the first set of feature data comprises:
determining a first transaction event related to a prediction target from the plurality of transaction events according to the asset profile features and the temporal features of the plurality of transaction events; and
determining the transaction event graph according to the asset profile feature and the temporal feature of the first transaction event.

7 . The method according to claim 1 , wherein the determining a first intra-region representation according to a second set of feature data comprises:
determining a first region graph according to the second set of feature data; and performing a representation learning by using the first region graph, so as to obtain the first intra-region representation.

8 . The method according to claim 7 , wherein the second set of feature data comprises: asset profile features, temporal features and regional features of a plurality of transaction events; and
wherein the determining a first region graph according to the second set of feature data comprises: dividing the plurality of transaction events into a plurality of sets of transaction events according to the regional features of the plurality of transaction events; for each set of transaction events in the plurality of sets of transaction events,
determining a second transaction event related to a prediction target from the set of transaction events; and
determining the first region graph according to the asset profile feature, the temporal feature and the regional feature of the second transaction event,
wherein the performing a multi-task learning for a first model according to the event-level representation, so as to obtain first price distribution data comprises: dividing the event-level representation into a plurality of sets of representations according to an area corresponding to the event-level representation; and executing a learning task for the first model according to each set of representations in the plurality of sets of representations respectively, so as to obtain the first price distribution data, wherein at least part of model parameters is shared between the learning tasks corresponding to the plurality of sets of representations.

9 . A method of training a model, the method comprising:
receiving a noised intra-region representation from a client; determining a region-level representation according to a third set of feature data and the noised intra-region representation; performing a multi-task learning for a second model according to the noised intra-region representation and the region-level representation, so as to obtain second price distribution data; transmitting the second price distribution data to a central server; and adjusting a parameter of the second model according to a noised parameter gradient in response to the noised parameter gradient being received from the central server.

10 . The method according to claim 9 , wherein the determining a region-level representation according to a third set of feature data and the noised intra-region representation comprises:
determining a second region graph according to the third set of feature data and the noised intra-region representation, performing a representation learning by using the second region graph, so as to obtain a second intra-region representation, and determining the region-level representation according to the second intra-region representation and the noised intra-region representation; and wherein the determining a second region graph according to the third set of feature data and the noised intra-region representation comprises: dividing the third set of feature data and the noised intra-region representation into a plurality of sets of regional features according to a region corresponding to the third set of feature data and the noised intra-region representation, for each set of regional features in the plurality of sets of regional features,
determining a target feature related to a prediction target from the set of regional features; and
determining the second region graph according to the target feature; and
wherein the performing a multi-task learning for a second model according to the noised intra-region representation and the region-level representation, so as to obtain second price distribution data comprises: dividing the noised intra-region representation and the region-level representation into a plurality of sets of representations according to a region corresponding to the noised intra-region representation and the region-level representation, and executing, for each set of representations in the plurality of sets of representations, a learning task for the second model to obtain the second price distribution data, wherein at least part of model parameters is shared between the learning tasks corresponding to the plurality of sets of representatio

Assignees

• Beijing Baidu Netcom Sci & Tech Co Ltd

Inventors

• Liu Ji
• Yu Sunjie
• Zhang Weijia
• Liu Hao
• Zhu Hengshu
• Dou Dejing
• Xiong Hui

Classifications

• G06Q50/16 Primary

  Real estate · CPC title

• G06Q30/0206

  Price or cost determination based on market factors · CPC title

• G06N20/00 Primary

  Machine learning · CPC title

• G06N3/084

  Backpropagation, e.g. using gradient descent · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title