Beijing Baidu Netcom Science Technology Co., Ltd

Beijing Baidu Netcom Science and Technology Co., Ltd.

International Centre for Bamboo and Rattan

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

Beijing Dajia Internet Information Technology Co., Ltd.

Yanjun Ma

Dianhai Yu

Xiaoguang Hu

Haifeng Wang

Yuning Du

Qiwen Liu

Tian Wu

Ruoyu Guo

Yi Liu

Daxiang Dong

Shengyu Wei

Xiang Lan

Ran Bi

Qiao Zhao

Ying Zhou

Hongyu Liu

Baohua Lai

Cheng Cui

Shuilong Dong

Liujie Zhang

Huihuang Zheng

Xueying Lyu

Tingquan Gao

Hua Wu

Wenbo Zhang

Tao Hu

Chenxia Li

Zeyu Chen

Zehui Jiang

Tianjian He

Bin Lu

Yanting Chang

Qingqing Dang

Yayun Deng

Yamei Li

Yan Li

Jie Yang

Wei Zhou

Yong Yuan

Jiachen Liu

Weimeng Zhang

Xingjian Li

Fei Guo

Wenhui Zhang

Weiwei Liu

Chunyu Zhang

Sheng Guo

An Liu

Meng Liao

Dong Ding

Zhihua Wu

Xingwu Sun

Haojie Wei

Weibao Gong

Guohua Li

Xiaoting Yin

Xueying Lv

Zhiliang Yu

Mengsi Xia

Kaipeng Deng

Lielin Jiang

Yufei Meng

Ming Zong

Zewu Wu

Yuying Hao

Yehua Yang

Shao Zeng

Sensen He

Bingqing Ke

Cunyi Zhang

Can Wen

Guanghua Yu

Haoshuang Wang

Guanzhong Wang

Xijuan Zhang

A session recommendation method, a device and an electronic device are provided, related to the field of graph neural network technology. The session recommendation method includes: acquiring a session control sequence, and acquiring a first embedding vector matrix based on an embedding vector of each of items in the session control sequence; generating a position information sequence based on an arrangement sequence of the items in the session control sequence, and acquiring a second embedding vector matrix based on an embedding vector of each piece of position information in the position information sequence; determining a target embedding vector matrix based on the first embedding vector matrix and the second embedding vector matrix; and determining a recommended item, based on the target embedding vector matrix and through a Session-based Recommendation Graph Neural Network.

Session recommendation method, device and electronic equipment

A scheduling method for a deep learning framework, a scheduling apparatus, an electronic device, a storage medium, and a program product is provided, and can be used in the field of artificial intelligence, especially in the fields of machine learning, deep learning, etc. The method includes: receiving a processing request for processing a plurality of tasks by using a dedicated processing unit, the processing request including scheduling requirements for the plurality of tasks, and each of the plurality of tasks being associated with execution of multi-batch data processing; and scheduling, based on the scheduling requirements for the plurality of tasks in batches of data, the dedicated processing unit to process the plurality of tasks.

Deep learning framework scheduling

Embodiments of the present disclosure provide a method and apparatus of training a model, an electronic device, a storage medium and a development system, which relate to a field of deep learning. The method may include calling a training preparation component to set at least a loss function and an optimization function for training the model, in response to determining that a training preparation instruction is received. The method further includes calling a training component to set a first data reading component, in response to determining that a training instruction is received. The first data reading component is configured to load a training data set for training the model. In addition, the method may further include training the model based on the training data set from the first data reading component, by using the loss function and the optimization function through the training component.

Model development tool to train, evaluate and predict with deep learning by selecting functions

The present application discloses a distributed training method and system, a device and a storage medium, and relates to technical fields of deep learning and cloud computing. The method includes: sending, by a task information server, a first training request and information of an available first computing server to at least a first data server; sending, by the first data server, a first batch of training data to the first computing server, according to the first training request; performing, by the first computing server, model training according to the first batch of training data, sending model parameters to the first data server so as to be stored after the training is completed, and sending identification information of the first batch of training data to the task information server so as to be recorded; wherein the model parameters are not stored at any one of the computing servers.

Distributed training method and system, device and storage medium

The present disclosure provides an image processing method and apparatus, and relates to the field of image processing, and in particular to the field of image annotation. An implementation is: obtaining an image to be processed including a target region to be annotated; in response to a first click on the target region, performing a first operation to expand a predicted region for the target region based on a click position of the first click; in response to a second click in a position where the predicted region exceeds the target region, performing a second operation to reduce the predicted region based on a click position of the second click; and in response to determining that a difference between the predicted region and the target region meets a preset condition, obtaining an outline of the predicted region to annotate the target region.

Image processing

Provided is a lightweight model training method, an image processing method, a device and a medium. The lightweight model training method includes: acquiring first and second augmentation probabilities and a target weight adopted in an e-th iteration; performing data augmentation on a data set based on the first and second augmentation probabilities respectively, to obtain first and second data sets; obtaining a first output value of a student model and a second output value of a teacher model based on the first data set; obtaining a third output value and a fourth output value based on the second data set; determining a distillation loss function, a truth-value loss function and a target loss function; training the student model based on the target loss function; and determining a first augmentation probability or target weight to be adopted in an (e+1)-th iteration in a case of e is less than E.

Lightweight model training method, image processing method, electronic device, and storage medium

The present disclosure provides a method for training a feature extraction model, a method for classifying an image and related apparatuses, and relates to the field of artificial intelligence technology such as deep learning and image recognition. The scheme comprises: extracting an image feature of each sample image in a sample image set using a basic feature extraction module of an initial feature extraction model, to obtain an initial feature vector set; performing normalization processing on each initial feature vector in the initial feature vector set using a normalization processing module of the initial feature extraction model, to obtain each normalized feature vector; and guiding training for the initial feature extraction model through a preset high discriminative loss function, to obtain a target feature extraction model as a training result.

Method for training feature extraction model, method for classifying image, and related apparatuses

A method and apparatus of constructing a network model for deep learning, a device, and a storage medium, which relate to artificial intelligence, and in particular to a field of deep learning. The method of constructing the network model for deep learning includes: determining an execution mode for executing codes, based on a mode parameter; executing the codes by using a first component, which is executable in a first execution mode, through a syntax element in the codes, in response to determining that the execution mode is the first execution mode; and executing the codes by using a second component, which is executable in a second execution mode, through the syntax element, in response to determining that the execution mode is the second execution mode; wherein the first component and the second component have the same component interface, and the syntax element corresponds to the component interface.

Method of constructing network model for deep learning, device, and storage medium

The present disclosure provides a vision processing and model training method, device, storage medium and program product. A specific implementation solution is as follows: establishing an image classification network with the same backbone network as the vision model, performing a self-monitoring training on the image classification network by using an unlabeled first data set; initializing a weight of a backbone network of the vision model according to a weight of a backbone network of the trained image classification network to obtain a pre-training model, the structure of the pre-training model being consistent with that of the vision model, and optimize the weight of the backbone network by using real data set in a current computer vision task scenario, so as to be more suitable for the current computer vision task; then, training the pre-training model by using a labeled second data set to obtain a trained vision model.

Vision processing and model training method, device, storage medium and program product

Provided are an image processing method and apparatus, a device, a medium and a program product. The image processing method includes: performing image augmentation on an original image to obtain at least one augmented image; performing subject detection on the original image and the at least one augmented image to obtain an original detection frame in the original image and an augmented detection frame in the at least one augmented image; determining whether the original detection frame and the augmented detection frame belong to the same subject; and in response to the original detection frame and the augmented detection frame belonging to the same subject, determining a target subject frame in the original image according to the augmented detection frame.

Image processing method and apparatus, device, medium and program product

An imaging exposure control method and apparatus, a device and a storage medium, which relate to the field of artificial intelligence technologies, such as machine learning technologies and intelligent imaging technologies, are disclosed. An implementation includes performing semantic segmentation on a preformed image to obtain semantic segmentation images of at least two semantic regions; estimating an exposure duration of each semantic region based on the semantic segmentation image and the preformed image; and controlling exposure of each semantic region during imaging based on the exposure duration of each semantic region.

Imaging exposure control method and apparatus, device and storage medium

There is provided a method and apparatus of training a model, a device, and a medium, which relate to artificial intelligence, and in particular to a deep learning and image processing technology. The method may include: determining a plurality of augmented sample sets associated with a plurality of original samples; determining a first constraint according to a first model based on the plurality of augmented sample sets; determining a second constraint according to the first model and a second model based on the plurality of augmented sample sets, wherein the second constraint is associated with a difference between outputs of the first model and the second model for one augmented sample, and the first model has a complexity lower than that of the second model; training the first model based on at least the first constraint and the second constraint, so as to obtain a trained first model.

Method and apparatus of training model, device, medium, and program product

The disclosure discloses a conversation-based recommending method. A directed graph corresponding to a current conversation is obtained. The current conversation includes clicked items, the directed graph includes nodes and directed edges between the nodes, each node corresponds to a clicked item, and each directed edge indicates relationship data between the nodes. For each node of the directed graph, an attention weight is determined for each directed edge corresponding to the node based on a feature vector of the node and the relationship data for each node of the directed graph. A new feature vector of the node is determined based on the relationship data and the attention weight of each directed edge. A feature vector of the current conversation is determined based on the new feature vector of each node. An item is recommended based on the feature vector of the current conversation.

Conversation-based recommending method, conversation-based recommending apparatus, and device

A peony PoWOX11 gene and an application of a coded protein thereof are provided in the present disclosure, belonging to the field of biotechnology. The peony PoWOX11 gene is introduced intoto obtain a transgenicplant stably expressing peony PoWOX11 gene, with bolting and flowering of the transgenicplant delayed; where the peony PoWOX11 gene has a nucleotide sequence as shown in SEQ ID NO. 7, and a protein coded by the peony PoWOX11 gene has an amino acid sequence as shown in SEQ ID NO. 8.

Peony POWOX11 gene and applications of coded protein thereof

Disclosed are a peony PoWOX4 gene and applications of a coded protein thereof, belonging to the field of biotechnology. According to the present disclosure,is taken as a model plant, and the peony PoWOX4 gene is transformed intoto promote the early bolting, flowering and vegetative growth of

Peony PoWOX4 gene and applications of coded protein thereof

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Beijing, China

Yanjun Ma