Beijing Baidu Netcom Science and Technology Co., Ltd.

Baidu Online Network Technology (beijing) Co., Ltd.

Xiaomin Fang

Fan Wang

Jingzhou He

Lihang Liu

Lingke Zeng

Haifeng Wang

Jizhou Huang

Haijin Liang

Yelan Mo

Yaxue Chen

The present application discloses an optimizer learning method and apparatus, an electronic device and a readable storage medium, which relates to the field of deep learning technologies. An implementation solution adopted by the present application during optimizer learning is: acquiring training data, the training data including a plurality of data sets each including neural network attribute information, neural network optimizer information, and optimizer parameter information; and training a meta-learning model by taking the neural network attribute information and the neural network optimizer information in the data sets as input and taking the optimizer parameter information in the data sets as output, until the meta-learning model converges. The present application can implement self-adaptation of optimizers, so as to improve generalization capability of the optimizers.

Optimizer learning method and apparatus, electronic device and readable storage medium

A method and apparatus for optimizing a recommendation system, a device and a computer storage medium are described, which relates to the technical field of deep learning and intelligent search in artificial intelligence. A specific implementation solution is: taking the recommendation system as an agent, a user as an environment, each recommended content of the recommendation system as an action of the agent, and a long-term behavioral revenue of the user as a reward of the environment; and optimizing to-be-optimized parameters in the recommendation system by reinforcement learning to maximize the reward of the environment. The present disclosure can effectively optimize long-term behavioral revenues of users.

Method and apparatus for optimizing recommendation system, device and computer storage medium

A method and apparatus for building a route time consumption estimation model, and a method and apparatus for estimating a route time consumption are provided. The method comprises: obtaining training data from user trajectory data; obtaining the route time consumption estimation model by using the training data, wherein the route time consumption estimation model comprises: a road condition subnetwork estimating road condition feature representations of road segments included by the route by using the time information and road network feature representations of road segments included by the route; an integration subnetwork determining an estimated time consumption on the route according to a feature representation of the time information, vector representations of the road segments included by the route and road condition feature representations of the road segments included by the route; a training target of the route time consumption estimation model is to minimize a difference between the estimated time consumption and an actual time consumption on the route. The accuracy of the estimation of time consumption on the route can be improved.

Method and apparatus for building route time consumption estimation model, and method and apparatus for estimating route time consumption

Embodiment of the disclosure provide a method and apparatus for generating information. The method includes: acquiring vectors of a plurality of users, the vector being used to characterize behavior habits of the users; inputting the vectors of the plurality of users and push information pushed by a push system to the plurality of users into a feedback information generating model established in advance, to generate the feedback information of the plurality of users for the push information, wherein the feedback information generating model is used to characterize a corresponding relationship between the vectors, the push information and the feedback information; and generating an evaluation report of the push system based on the feedback information.

Method and apparatus for generating information

Growing community of inventors

Beijing, China

Xiaomin Fang