Beijing, China
Beijing, China

Baidu百度, Inc. , incorporated on January 18, 2000, is a Chinese web services company headquartered in the Baidu Campus in Haidian District in Beijing.Baidu offers many services, including a Chinese language-search engine for websites, audio files, and images. Baidu offers 57 search and community services including Baidu Baike and a searchable, keyword-based discussion forum. Baidu was established in 2000 by Robin Li and Eric Xu. Both of the co-founders are Chinese nationals who studied and worked overseas before returning to China. In May 2014, Baidu ranked 5th overall in the Alexa Internet rankings. During Q4 of 2010, it is estimated that there were 4.02 billion search queries in China of which Baidu had a market share of 56.6%. China's Internet-search revenue share in second quarter 2011 by Baidu is 76%. In December 2007, Baidu became the first Chinese company to be included in the NASDAQ-100 index. In December 2014, Baidu was expected to invest in the company Uber.Baidu provides an index of over 740 million web pages, 80 million images, and 10 million multimedia files. Baidu offers multimedia content including MP3 music, and movies, and is the first in China to offer Wireless Application Protocol and personal digital assistant -based mobile search.Baidu Baike is similar to Wikipedia as an encyclopedia; however, unlike Wikipedia, only registered users can edit the articles. While access to Wikipedia has been intermittently blocked or certain articles filtered in China since June 2004, there is some controversy about the degree to which Baidu cooperates with Chinese government censorship. Wikipedia.


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Disclosed are systems and methods that implement efficient engines for computation-intensive tasks such as neural network deployment. Various embodiments of the invention provide for high-throughput batching that increases throughput of streaming data in high-traffic applications, such as real-time speech transcription. In embodiments, throughput is increased by dynamically assembling into batches and processing together user requests that randomly arrive at unknown timing such that not all the data is present at once at the time of batching. Some embodiments allow for performing steaming classification using pre-processing. The gains in performance allow for more efficient use of a compute engine and drastically reduce the cost of deploying large neural networks at scale, while meeting strict application requirements and adding relatively little computational latency so as to maintain a satisfactory application experience.


Described herein are systems and methods for determining how to automatically answer questions like Where did Harry Potter go to school? Carefully built knowledge graphs provide rich sources of facts. However, it still remains a challenge to answer factual questions in natural language due to the tremendous variety of ways a question can be raised. Presented herein are embodiments of systems and methods for human inspired simple question answering (HISQA), a deep-neural-network-based methodology for automatic question answering using a knowledge graph. Inspired by humans natural actions in this task, embodiments first find the correct entity via entity linking, and then seek a proper relation to answer the question-both achieved by deep gated recurrent networks and neural embedding mechanism.


Described herein are systems and methods for generating and using attention-based deep learning architectures for visual question answering task (VQA) to automatically generate answers for image-related (still or video images) questions. To generate the correct answers, it is important for a models attention to focus on the relevant regions of an image according to the question because different questions may ask about the attributes of different image regions. In embodiments, such question-guided attention is learned with a configurable convolutional neural network (ABC-CNN). Embodiments of the ABC-CNN models determine the attention maps by convolving image feature map with the configurable convolutional kernels determined by the questions semantics. In embodiments, the question-guided attention maps focus on the question-related regions and filters out noise in the unrelated regions.


The present application discloses a virtual router cluster, and a data forwarding method and apparatus. A specific implementation of the virtual router cluster includes: a gateway and at least one virtual router interconnected with the gateway; the gateway receiving an externally transmitted data packet; the gateway selecting a first virtual router corresponding to the data packet from the at least one virtual router according to an Open Shortest Path First protocol, and forwarding the data packet to the first virtual router corresponding to the data packet; and the first virtual router receiving the data packet and forwarding the data packet to a destination. This implementation implements network load balancing, thereby avoiding network congestion.


Patent
Baidu | Date: 2016-08-05

Presented are systems and methods that provide a unified end-to-end detection pipeline for object detection that achieves impressive performance in detecting very small and highly overlapped objects in face and car images. Various embodiments of the present disclosure provide for an accurate and efficient one-stage FCN-based object detector that may be optimized end-to-end during training. Certain embodiments train the object detector on a single scale using jitter-augmentation integrated landmark localization information through joint multi-task learning to improve the performance and accuracy of end-to-end object detection. Various embodiments apply hard negative mining techniques during training to bootstrap detection performance. The presented are systems and methods are highly suitable for situations where region proposal generation methods may fail, and they outperform many existing sliding window fashion FCN detection frameworks when detecting objects at small scales and under heavy occlusion conditions.


Described herein are systems and methods that address the task of learning novel visual concepts, and their interactions with other concepts, from a few images with sentence descriptions. Using linguistic context and visual features, embodiments are able to efficiently hypothesize the semantic meaning of new words and add them to model word dictionaries so that they can be used to describe images which contain these novel concepts. In the experiments, it was shown that the tested embodiments effectively learned novel visual concepts from a few examples without disturbing the previously learned concepts.


Patent
Baidu | Date: 2016-11-21

Embodiments of end-to-end deep learning systems and methods are disclosed to recognize speech of vastly different languages, such as English or Mandarin Chinese. In embodiments, the entire pipelines of hand-engineered components are replaced with neural networks, and the end-to-end learning allows handling a diverse variety of speech including noisy environments, accents, and different languages. Using a trained embodiment and an embodiment of a batch dispatch technique with GPUs in a data center, an end-to-end deep learning system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.


Patent
Baidu | Date: 2016-11-21

Embodiments of end-to-end deep learning systems and methods are disclosed to recognize speech of vastly different languages, such as English or Mandarin Chinese. In embodiments, the entire pipelines of hand-engineered components are replaced with neural networks, and the end-to-end learning allows handling a diverse variety of speech including noisy environments, accents, and different languages. Using a trained embodiment and an embodiment of a batch dispatch technique with GPUs in a data center, an end-to-end deep learning system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.


The embodiments of the present disclosure disclose a vehicular lane line data processing method, apparatus, storage medium, and device. The method includes: acquiring at least two consecutive original images of a vehicular lane line and positioning data of the original images; calculating, using a deep neural network model, a pixel confidence for a conformity between a pixel characteristic in the original images and a vehicular lane line characteristic; determining an outline of the vehicular lane line from the original images and using the outline of the vehicular lane line as a candidate vehicular lane line; calculating a vehicular lane line confidence of the candidate vehicular lane line based on the pixel confidences of pixels in the candidate vehicular lane line; filtering the candidate vehicular lane line based on the vehicular lane line confidence of the candidate vehicular lane line; recognizing, for the filtered vehicular lane line, attribute information of the vehicular lane line; and determining map data of the vehicular lane line based on the attribute information of the vehicular lane line and the positioning data during shooting of the original images. By means of the vehicular lane line data processing method, apparatus, storage medium, and device provided by the embodiments of the present disclosure, the vehicular lane line data can be efficiently and precisely determined, the labor costs in high-precision map production is greatly reduced, and the mass production of high-precision maps can be achieved.


Disclosed is an artificial intelligence based voiceprint login method. The method comprises: S1: receiving a login request from a user and acquiring user information of the user; S2: generating a login string and replacing at least one character in the login string according to character replacement control information corresponding to the user information; S3: providing the user the replaced login string and receiving voice information of a login string read by the user; and S4: performing login authentication on the user according to the voice information of the login string read by the user. The method on one hand increases voiceprint password security by combining a voiceprint and user-defined characters to replace a voiceprint authentication corresponding to normal information, on the other hand, the method hides characters that a user wishes to hide to satisfy a psychological need of the user in which the user may not wish to directly show all the passwords, improving user experiences and increasing password security. Also disclosed is an artificial intelligence based voiceprint login device.

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