Intel Corporation

Otis Elevator Company

Lenovo (beijing) Limited

Beijing Lenovo Software Ltd.

Beijing Naura Microelectronics Equipment Co., Ltd.

Mobvoi Information Technology Company Limited

Bdp Enviro Tech Limited

Beijing Supvan Electronic Technology Co. , Ltd.

Jianguo Li

Yurong Chen

Tao Wang

Yimin Zhang

Yangzhou Du

Qiang Li

Wenlong Li

Anbang Yao

Yi-Jen Chiu

Ya-Ti Peng

Peng Wang

Xiaofeng Tong

Zhou Su

Bin Wang

Lin Xu

Shoumeng Yan

Ke Chen

Libin Wang

Ying Zheng

Zhen Jia

Alan Matthew Finn

Zheng Wang

Chen Wang

Arthur Hsu

Hui Fang

Jianwei Zhao

Feng Xu

Dayong Ding

Alois Senger

Yanzhi Chen

Xiaobin Liang

LongWen Wang

ZhaoXia Hu

Anna Su

QingXi Cai

Gero Gschwendtner

Lifei Cheng

Wilfrid Fuchs

Ang Liu

Nigel Bruce Morris

Xin Wei

Wei Ge

Jie Hu

Xiangbo Lv

MingPing Jiang

Yongqing Fang

Zhiqiang Shen

Kai Cheng

Baolin Wang

Qiang Song

Zhuang Liu

Jianbo Ye

Jiangxing Huang

Fengshu Yue

Hongmin Zhu

Kaisheng Xu

Qian Li

Patricia P Wang

Jiuwei Li

Yuxi Li

Yinpeng Dong

Jun Ma

Renkun Ni

Chong Cao

Jinlong Li

Shimao Chen

Jianye Yang

Jiawei Wu

Bingyin Cheng

Jinqi Yang

Yunyun Zhao

Lianrui Fu

Jianwei Luo

Du Yangzhou

Zuan Tang

Nigel Morris

Jian Wan

Bin Robin Wang

A wafer boat temporary storage device for temporarily storing a wafer boat in a semiconductor processing device includes two carrying platforms arranged oppositely and configured to carry two ends of the wafer boat in a length direction of the wafer boat, respectively. The wafer boat includes two positioning parts arranged at two ends in the length direction and spaced apart in a width direction of the wafer boat. Each carrying platform includes a first positioning structure and a second positioning structure. The first positioning structure is configured to make rolling contact with two sides of the two positioning parts at one end of the wafer boat that face toward or away from the two positioning parts at the other end of the wafer boat in the length direction to perform alignment between the wafer boat and the wafer boat temporary storage device in the length direction.

Wafer boat temporary storage device and semiconductor processing device

A mechanism is described for facilitating slimming of neural networks in machine learning environments. A method includes learning a first neural network associated with machine learning processes to be performed by a processor of a computing device, where learning includes analyzing a plurality of channels associated with one or more layers of the first neural network. The method may further include computing a plurality of scaling factors to be associated with the plurality of channels such that each channel is assigned a scaling factor, wherein each scaling factor to indicate relevance of a corresponding channel within the first neural network. The method may further include pruning the first neural network into a second neural network by removing one or more channels of the plurality of channels having low relevance as indicated by one or more scaling factors of the plurality of scaling factors assigned to the one or more channels.

Efficient neural networks with elaborate matrix structures in machine learning environments

A label printer and a label printing method for precise positioning thereof are provided. The label printer includes an outer shell and a label cartridge arranged inside the outer shell, a power mechanism and a control mechanism configured to control paper output of the label cartridge and winding and unwinding of a carbon ribbon, and a print head configured to realize label printing. An inner space of the label cartridge is reasonably planed, and a linear label paper output channel and a label rollback release space are arranged, thereby ensuring smoothness of paper feeding and paper rollback, and preventing label paper from wrinkling in a rollback process. A gear transmission system is improved to realize forward and reverse rotation of gears relying on the same electric motor. A photoelectric sensor with its own reflecting prism is arranged to reduce an occupied space of the label cartridge.

Label printer and label printing method for precise positioning thereof

Embodiments are directed to a composite binary decomposition network. An embodiment of a computer-readable storage medium includes executable computer program instructions for transforming a pre-trained first neural network into a binary neural network by processing layers of the first neural network in a composite binary decomposition process, where the first neural network having floating point values representing weights of various layers of the first neural network. The composite binary decomposition process includes a composite operation to expand real matrices or tensors into a plurality of binary matrices or tensors, and a decompose operation to decompose one or more binary matrices or tensors of the plurality of binary matrices or tensors into multiple lower rank binary matrices or tensors.

Composite binary decomposition network

Techniques related to implementing convolutional neural networks for face or other object recognition are discussed. Such techniques may include applying, in turn, a depth-wise separable convolution, a condense point-wise convolution, and an expansion point-wise convolution to input feature maps to generate output feature maps such that the output from the expansion point-wise convolution has more channels than the output from the condense point-wise convolution.

Condense-expansion-depth-wise convolutional neural network for face recognition

Techniques and apparatus for generating dense natural language descriptions for video content are described. In one embodiment, for example, an apparatus may include at least one memory and logic, at least a portion of the logic comprised in hardware coupled to the at least one memory, the logic to receive a source video comprising a plurality of frames, determine a plurality of regions for each of the plurality of frames, generate at least one region-sequence connecting the determined plurality of regions, apply a language model to the at least one region-sequence to generate description information comprising a description of at least a portion of content of the source video. Other embodiments are described and claimed.

Techniques for dense video descriptions

An example apparatus for semantic image segmentation includes a receiver to receive an image to be segmented. The apparatus also includes a gated dense pyramid network including a plurality of gated dense pyramid (GDP) blocks to be trained to generate semantic labels for respective pixels in the received image. The apparatus further includes a generator to generate a segmented image based on the generated semantic labels.

Semantic image segmentation using gated dense pyramid blocks

An example apparatus for visual question answering includes a receiver to receive an input image and a question. The apparatus also includes an encoder to encode the input image and the question into a query representation including visual attention features. The apparatus includes a knowledge spotter to retrieve a knowledge entry from a visual knowledge base pre-built on a set of question-answer pairs. The apparatus further includes a joint embedder to jointly embed the visual attention features and the knowledge entry to generate visual-knowledge features. The apparatus also further includes an answer generator to generate an answer based on the query representation and the visual-knowledge features.

Visual question answering using visual knowledge bases

A mechanism is described for facilitating slimming of neural networks in machine learning environments. A method of embodiments, as described herein, includes learning a first neural network associated with machine learning processes to be performed by a processor of a computing device, where learning includes analyzing a plurality of channels associated with one or more layers of the first neural network. The method may further include computing a plurality of scaling factors to be associated with the plurality of channels such that each channel is assigned a scaling factor, wherein each scaling factor to indicate relevance of a corresponding channel within the first neural network. The method may further include pruning the first neural network into a second neural network by removing one or more channels of the plurality of channels having low relevance as indicated by one or more scaling factors of the plurality of scaling factors assigned to the one or more channels.

Slimming of neural networks in machine learning environments

One embodiment provides for a compute apparatus to perform machine learning operations, the compute apparatus comprising instruction decode logic to decode a single instruction including multiple operands into a single decoded instruction, the multiple operands including a first operand and a second operand, the first operand including vector of one-hot coded weights and the second operand including a vector of input data; and a general-purpose graphics compute unit including a first logic unit, the general-purpose graphics compute unit to execute the single decoded instruction, wherein to execute the single decoded instruction includes to perform multiple operations on the first set of operands and the second set of operands.

System and method enabling one-hot neural networks on a machine learning compute platform

A system, article, and method of deep supervision object detection for reducing resource usage is provided for image processing and that uses depth-wise dense blocks.

Method and system of deep supervision object detection for reducing resource usage

Autonomous robots and methods of operating the same are disclosed. An autonomous robot includes a sensor and memory including machine readable instructions. The autonomous robot further includes at least one processor to execute the instructions to generate a velocity costmap associated with an environment in which the robot is located. The processor generates the velocity costmap based on a source image captured by the sensor. The velocity costmap includes velocity information indicative of movement of an obstacle detected in the environment.

Autonomous robots and methods of operating the same

A horn mounting structure, includes: a shell, a sound hole is formed at a side surface of the shell; a mounting groove formed inside the shell, and the sound hole is communicated with the mounting groove; a holder, the holder is bonded in the mounting groove through a sealant, and a front sound chamber is formed at one side of the holder facing to the sound hole, and a horn, and the horn is bonded to one side of the holder facing away from the shell through the sealant.

Horn mounting structure and electronic device having same

An example apparatus for semantic image segmentation includes a receiver to receive an image to be segmented. The apparatus also includes a gated dense pyramid network comprising a plurality of gated dense pyramid (GDP) blocks to be trained to generate semantic labels for each pixel in the received image. The apparatus further includes a generator to generate a segmented image based on the generated semantic labels.

Growing community of inventors

Beijing, China

Jianguo Li