International Business Machines Corporation

Intel Corporation

Palo Alto Networks, Inc.

Lenovo (beijing) Limited

China Petroleum & Chemical Corporation

Beijing Research Institute of Chemical Industry, China Petroleum & Chemical Corporation

Microsoft Technology Licensing, LLC

Other

Boe Technology Group Co., Ltd.

Beijing Boe Optoelectronics Technology Co., Ltd.

Beijing Lenovo Software Ltd.

Sinopec Beijing Research Institute of Chemical Industry

China Petroleum & Chemcial Corporation

China South Industry Academy

Lin Xu

Anbang Yao

Yurong Chen

Yiwen Guo

Shandong Wang

Ping Hu

Libin Wang

Dongqi Cai

Wenhua Cheng

Liu Yang

Matthew McClain

Zhao Cao

Qi Jin

Chuanqing Li

Yi Qin Yu

Yue Pan

Xing Zhi Sun

Aimin Liang

Lanjun Wang

Seokkyung Chung

Yuqing Hou

Hui Liao

Michael Haden Conner

Xingzhi Sun

Guozhu Yu

Jie Zhang

William Redington Hewlett, Ii

Sriram Srinivasan

Ximing Xie

Wei Sun

Kai Liu

Qiang Li

Tao Li

Qing Fa Wang

Wei Lu

Sriram Kolinjivadi Padmanabhan

Jianguo Li

Hui Liu

Yangzhou Du

Yao Liang Chen

Sheng Huang

Zhi Xu

Yuan Feng

Xiao Yan Chen

Christopher Shane Claussen

Jing Sun

Xiao Min Xu

Shadi Rostami-Hesarsorkh

Farshad Rostamabadi

Bai Chen Deng

Zhou Su

Shuo Wu

Lee Klarich

Benjamin Charles ZumBrunnen

Renzo Lazzarato

Jiangwei Zhao

Jing Wang

Wei Li

Yi-Jen Chiu

Yu Chen Zhou

Chen Wang

Peng Li

Wenlong Li

Chao Wang

Wei Hu

Xiaofeng Tong

Hui Li

Zhiqiang Wang

Bin Benjamin Zhu

Yimin Zhang

Wei Shen

Jia Zou

Jackson Michael Davis

Yan Wu

Gregory Alan Flurry

Hui-I Hsiao

Bing Jiang Sun

Lei Zhang

Ju Wei Shi

Ya-Ti Peng

Xinyu Wang

Xu Zhao

Ye Li

Jun Ma

Xin Ma

Jiang Wang

Song Lu

Brent Rector

Joshua Wai-Ho Hui

Yunfeng Zheng

Weiping Hu

Chuan Sun

Qiuli Wang

Li Wen

Zhixiang Wang

Saji Abraham

Yan Lin

Jianfeng Yao

Pengfei Lv

Ying Tao

Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps. The weighted feature maps are stored in the LSTM. A Q value is calculated for different actions based on the weighted feature maps stored in the LSTM.

Methods and systems for budgeted and simplified training of deep neural networks

The present disclosure provides a fiber laser light coherent combination system, comprising: a modulator module configured to perform a phase modulation on sub-beams according to pseudo-random sequences orthogonally independent from each other, and perform a frequency shift on a reference beam according to a set frequency; a fiber laser light amplifier module configured to perform a power amplification on the modulated sub-beams; a laser light collimation emission module configured to collimate and output the sub-beams and the reference beam; a combination sampling module configured to perform a combination of the sub-beams and the reference beam which are collimated and outputted, and convert them into an electrical signal; a digital phase modulation and demodulation module configured to perform a demodulation on the electrical signal according to the shifted frequency and each of the pseudo-random sequences, and obtain a phase difference between each of the sub-beams and the reference beam.

Fiber laser light coherent combination system

An apparatus and method are described for reducing the parameter density of a deep neural network (DNN). A layer-wise pruning module to prune a specified set of parameters from each layer of a reference dense neural network model to generate a second neural network model having a relatively higher sparsity rate than the reference neural network model; a retraining module to retrain the second neural network model in accordance with a set of training data to generate a retrained second neural network model; and the retraining module to output the retrained second neural network model as a final neural network model if a target sparsity rate has been reached or to provide the retrained second neural network model to the layer-wise pruning model for additional pruning if the target sparsity rate has not been reached.

Method and apparatus for reducing the parameter density of a deep neural network (DNN)

Methods and systems are disclosed for boosting deep neural networks for deep learning. In one example, in a deep neural network including a first shallow network and a second shallow network, a first training sample is processed by the first shallow network using equal weights. A loss for the first shallow network is determined based on the processed training sample using equal weights. Weights for the second shallow network are adjusted based on the determined loss for the first shallow network. A second training sample is processed by the second shallow network using the adjusted weights. In another example, in a deep neural network including a first weak network and a second weak network, a first subset of training samples is processed by the first weak network using initialized weights. A classification error for the first weak network on the first subset of training samples is determined. The second weak network is boosted using the determined classification error of the first weak network with adjusted weights. A second subset of training samples is processed by the second weak network using the adjusted weights.

Methods and systems for boosting deep neural networks for deep learning

Skin smoothing is applied to images using a bilateral filter and aided by a skin map. In one example a method includes receiving an image having pixels at an original resolution. The image is buffered. The image is downscaled from the original resolution to a lower resolution. A bilateral filter is applied to pixels of the downscaled image. The filtered pixels of the downscaled image are blended with pixels of the image having the original resolution, and the blended image is produced.

Skin map-aided skin smoothing of images using a bilateral filter

Described herein are hardware acceleration of random number generation for machine learning and deep learning applications. An apparatus () includes a uniform random number generator (URNG) circuit () to generate uniform random numbers and an adder circuit () that is coupled to the URNG circuit (). The adder circuit hardware () accelerates generation of Gaussian random numbers for machine learning.

Systems and methods for generating gaussian random numbers with hardware acceleration

A displaying device and a driving method thereof, wherein the displaying device includes a first display panel, a second display panel and a driving circuit; the first display panel includes a plurality of first pixel units that are able to display a color, wherein each of the first pixel units includes a plurality of sub-pixel units; and the second display panel is provided on a light exiting side of the first display panel, including a plurality of second pixel units; wherein a distribution density of the plurality of second pixel units is greater than a distribution density of the plurality of sub-pixel units.

Displaying device and driving method thereof

Methods and systems are disclosed using camera devices for deep channel and Convolutional Neural Network (CNN) images and formats. In one example, image values are captured by a color sensor array in an image capturing device or camera. The image values provide color channel data. The captured image values by the color sensor array are input to a CNN having at least one CNN layer. The CNN provides CNN channel data for each layer. The color channel data and CNN channel data is to form a deep channel image that stored in a memory. In another example, image values are captured by sensor array. The captured image values by the sensor array are input a CNN having a first CNN layer. An output is generated at the first CNN layer using the captured image values by the color sensor array. The output of the first CNN layer is stored as a feature map of the captured image.

Methods and systems using camera devices for deep channel and convolutional neural network images and formats

Methods and systems are disclosed using improved training and learning for deep neural networks. In one example, a deep neural network includes a plurality of layers, and each layer has a plurality of nodes. The nodes of each L layer in the plurality of layers are randomly connected to nodes of an L+layer. The nodes of each L+layer are connected to nodes in a subsequent L layer in a one-to-one manner. Parameters related to the nodes of each L layer are fixed. Parameters related to the nodes of each L+layers are updated. In another example, inputs for the input layer and labels for the output layer of a deep neural network are determined related to a first sample. A similarity between different pairs of inputs and labels is estimated using a Gaussian regression process.

Methods and systems using improved training and learning for deep neural networks

An identification of an item that was misclassified by a classification model constructed in accordance with a machine learning technique is received. One example of such a machine learning technique is a random forest. A subset of training data, previously used to construct the model, and that is associated with the misclassified item is identified. At least a portion of the identified subset is provided as output.

Data blaming

Methods and systems for advanced and augmented training of deep neural networks (DNNs) using synthetic data and innovative generative networks. A method includes training a DNN using synthetic data, training a plurality of DNNs using context data, associating features of the DNNs trained using context data with features of the DNN trained with synthetic data, and generating an augmented DNN using the associated features.

Methods and systems for advanced and augmented training of deep neural networks using synthetic data and innovative generative networks

A website misclassification report is received, indicating that a website has been misclassified. A determination is made that a current classification model correctly classifies the reported website. The current classification model is different from a model that was previously used to classify the website. In response to a determination that the reported website should be reclassified using the current classification model, a reclassification operation is performed, using the current classification model, on a set of websites determined to be similar to the reported website.

Focused URL recrawl

Described herein are advanced artificial intelligence agents for modeling physical interactions. An apparatus to provide an active artificial intelligence (AI) agent includes at least one database to store physical interaction data and compute cluster coupled to the at least one database. The compute cluster automatically obtains physical interaction data from a data collection module without manual interaction, stores the physical interaction data in the at least one database, and automatically trains diverse sets of machine learning program units to simulate physical interactions with each individual program unit having a different model based on the applied physical interaction data.

Advanced artificial intelligence agent for modeling physical interactions

Growing community of inventors

Beijing, China

Lin Xu