Patents by Inventor Anbang Yao

Anbang Yao has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20200285879
    Abstract: A semiconductor package apparatus may include technology to apply a trained scene text detection network to an image to identify a core text region, a supportive text region, and a background region of the image, and detect text in the image based on the identified core text region and supportive text region. Other embodiments are disclosed and claimed.
    Type: Application
    Filed: November 8, 2017
    Publication date: September 10, 2020
    Applicant: INTEL CORPORATION
    Inventors: Wenhua Cheng, Anbang Yao, Libin Wang, Dongqi Cai, Jianguo Li, Yurong Chen
  • Patent number: 10769748
    Abstract: One embodiment provides for a compute apparatus to perform machine learning operations, the compute apparatus comprising a decode unit to decode a single instruction into a decoded instruction, the decoded instruction to cause the compute apparatus to perform a complex machine learning compute operation.
    Type: Grant
    Filed: November 21, 2018
    Date of Patent: September 8, 2020
    Assignee: Intel Corporation
    Inventors: Eriko Nurvitadhi, Balaji Vembu, Nicolas C. Galoppo Von Borries, Rajkishore Barik, Tsung-Han Lin, Kamal Sinha, Nadathur Rajagopalan Satish, Jeremy Bottleson, Farshad Akhbari, Altug Koker, Narayan Srinivasa, Dukhwan Kim, Sara S. Baghsorkhi, Justin E. Gottschlich, Feng Chen, Elmoustapha Ould-Ahmed-Vall, Kevin Nealis, Xiaoming Chen, Anbang Yao
  • Publication number: 20200279156
    Abstract: A system to perform multi-modal analysis has at least three distinct characteristics: an early abstraction layer for each data modality integrating homogeneous feature cues coming from different deep learning architectures for that data modality, a late abstraction layer for further integrating heterogeneous features extracted from different models or data modalities and output from the early abstraction layer, and a propagation-down strategy for joint network training in an end-to-end manner. The system is thus able to consider correlations among homogeneous features and correlations among heterogenous features at different levels of abstraction. The system further extracts and fuses discriminative information contained in these models and modalities for high performance emotion recognition.
    Type: Application
    Filed: October 9, 2017
    Publication date: September 3, 2020
    Inventors: Dongqi Cai, Anbang Yao, Ping Hu, Shandong Wang, Yurong Chen
  • Publication number: 20200242821
    Abstract: Techniques are disclosed for analyzing a graph image in a disconnected mode, e.g., when a graph is rendered as .jpeg, .gif, .png, and so on, and identifying a portion of the graph image associated with a plot/curve of interest. The identified portion of the graph image may then be utilized to generate an adjusted image. The adjusted image may therefore dynamically increase visibility of the plot/curve of interest relative to other plots/curves, and thus the present disclosures provides additional graph functionalities without access to the data originally used to generate the graph. The disconnected graph functionalities disclosed herein may be implemented within an Internet browser or other “app” that may present images depicting graphs to a user.
    Type: Application
    Filed: October 3, 2017
    Publication date: July 30, 2020
    Applicant: Intel Corporation
    Inventors: Wenlong YANG, Anbang YAO, Avi NAHMIAS
  • Publication number: 20200242734
    Abstract: Methods and systems are disclosed using improved Convolutional Neural Networks (CNN) for image processing. In one example, an input image is down-sampled into smaller images with a smaller resolution than the input image. The down-sampled smaller images are processed by a CNN having a last layer with a reduced number of nodes than a last layer of a full CNN used to process the input image at a full resolution. A result is outputted based on the processed down-sampled smaller images by the CNN having a last layer with a reduced number of nodes. In another example, shallow CNN networks are built randomly. The randomly built shallow CNN networks are combined to imitate a trained deep neural network (DNN).
    Type: Application
    Filed: April 7, 2017
    Publication date: July 30, 2020
    Inventors: Shandong WANG, Yiwen GUO, Anbang YAO, Dongqi CAI, Libin WANG, Lin XU, Ping HU, Wenhua CHENG, Yurong CHEN
  • Patent number: 10726514
    Abstract: One embodiment provides a general-purpose graphics processing unit comprising a dynamic precision floating-point unit including a control unit having precision tracking hardware logic to track an available number of bits of precision for computed data relative to a target precision, wherein the dynamic precision floating-point unit includes computational logic to output data at multiple precisions.
    Type: Grant
    Filed: April 28, 2017
    Date of Patent: July 28, 2020
    Assignee: Intel Corporation
    Inventors: Elmoustapha Ould-Ahmed-Vall, Sara S. Baghsorkhi, Anbang Yao, Kevin Nealis, Xiaoming Chen, Altug Koker, Abhishek R. Appu, John C. Weast, Mike B. Macpherson, Dukhwan Kim, Linda L. Hurd, Ben J. Ashbaugh, Barath Lakshmanan, Liwei Ma, Joydeep Ray, Ping T. Tang, Michael S. Strickland
  • Publication number: 20200234411
    Abstract: 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.
    Type: Application
    Filed: April 7, 2017
    Publication date: July 23, 2020
    Inventors: Lin XU, Liu YANG, Anbang YAO, dongqi CAI, Libin WANG, Ping HU, Shaodong WANG, Wenhua CHENG, Yiwen GUO, Yurong CHEN
  • Publication number: 20200226362
    Abstract: Techniques are provided for neural network based, human attribute recognition, guided by anatomical key-points and statistic correlation models. Attributes include characteristics that can be visibly identified or inferred from an image, such as gender, hairstyle, clothing style, etc. A methodology implementing the techniques according to an embodiment includes applying an attribute feature extraction (AFE) convolutional neural network (CNN) to an image of a human to generate attribute feature maps based on the image. The method further includes applying a key-point guided proposal (KPG) CNN to the image of the human to generate proposed hierarchical regions of the image based on associated anatomical key-points.
    Type: Application
    Filed: December 27, 2017
    Publication date: July 16, 2020
    Applicant: INTEL CORPORATION
    Inventors: Ping Hu, Anbang Yao, Jia Wei, Dongqi Cai, Yurong Chen
  • Patent number: 10685262
    Abstract: Techniques related to implementing convolutional neural networks for object recognition are discussed. Such techniques may include generating a set of binary neural features via convolutional neural network layers based on input image data and applying a strong classifier to the set of binary neural features to generate an object label for the input image data.
    Type: Grant
    Filed: March 20, 2015
    Date of Patent: June 16, 2020
    Assignee: Intel Corporation
    Inventors: Anbang Yao, Lin Xu, Jianguo Li, Yurong Chen
  • Publication number: 20200167654
    Abstract: Methods and apparatus are disclosed for enhancing a binary weight neural network using a dependency tree. A method of enhancing a convolutional neural network (CNN) having binary weights includes constructing a tree for obtained binary tensors, the tree having a plurality of nodes beginning with a root node in each layer of the CNN. A convolution is calculated of an input feature map with an input binary tensor at the root node of the tree. A next node is searched from the root node of the tree and a convolution is calculated at the next node using a previous convolution result calculated at the root node of the tree. The searching of a next node from root node is repeated for all nodes from the root node of the tree, and a convolution is calculated at each next node using a previous convolution result.
    Type: Application
    Filed: May 23, 2018
    Publication date: May 28, 2020
    Inventors: Yiwen Guo, Anbang Yao, Hao Zhao, Ming Lu, Yurong CHEN
  • Publication number: 20200143205
    Abstract: A convolutional neural network framework is described that uses reverse connection and obviousness priors for object detection. A method includes performing a plurality of layers of convolutions and reverse connections on a received image to generate a plurality of feature maps, determining an objectness confidence for candidate bounding boxes based on outputs of an objectness prior, determining a joint loss function for each candidate bounding box by combining an objectness loss, a bounding box regression loss and a classification loss, calculating network gradients over positive boxes and negative boxes, updating network parameters within candidate bounding boxes using the joint loss function, repeating performing the convolutions through to updating network parameters until the training converges, and outputting network parameters for object detection based on the training images.
    Type: Application
    Filed: August 10, 2017
    Publication date: May 7, 2020
    Inventors: Anbang Yao, Tao Kong, Ming Lu, Yiwen Guo, Yurong CHEN
  • Publication number: 20200117936
    Abstract: Combinatorial shape regression is described as a technique for face alignment and facial landmark detection in images. As described stages of regression may be built for multiple ferns for a facial landmark detection system. In one example a regression is performed on a training set of images using face shapes, using facial component groups, and using individual face point pairs to learn shape increments for each respective image in the set of images. A fern is built based on this regression. Additional regressions are performed for building additional ferns. The ferns are then combined to build the facial landmark detection system.
    Type: Application
    Filed: December 11, 2019
    Publication date: April 16, 2020
    Applicant: INTEL CORPORATION
    Inventors: ANBANG YAO, YURONG CHEN
  • Publication number: 20200082264
    Abstract: Methods and apparatus are disclosed for enhancing a neural network using binary tensor and scale factor pairs. For one example, a method of optimizing a trained convolutional neural network (CNN) includes initializing an approximation residue as a trained weight tensor for the trained CNN. A plurality of binary tensors and scale factor pairs are determined. The approximation residue is updated using the binary tensors and scale factor pairs.
    Type: Application
    Filed: May 22, 2018
    Publication date: March 12, 2020
    Inventors: Yiwen Guo, Anbang Yao, Hao Zhao, Ming Lu, Yurong CHEN
  • Publication number: 20200082198
    Abstract: Methods and apparatus for discriminative semantic transfer and physics-inspired optimization in deep learning are disclosed. A computation training method for a convolutional neural network (CNN) includes receiving a sequence of training images in the CNN of a first stage to describe objects of a cluttered scene as a semantic segmentation mask. The semantic segmentation mask is received in a semantic segmentation network of a second stage to produce semantic features. Using weights from the first stage as feature extractors and weights from the second stage as classifiers, edges of the cluttered scene are identified using the semantic features.
    Type: Application
    Filed: May 22, 2018
    Publication date: March 12, 2020
    Inventors: Anbang YAO, Hao ZHAO, Ming LU, Yiwen GUO, Yurong CHEN
  • Publication number: 20200026999
    Abstract: 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.
    Type: Application
    Filed: April 7, 2017
    Publication date: January 23, 2020
    Inventors: Libin Wang, Yiwen Guo, Anbang Yao, Dongqi Cai, Lin Xu, Ping Hu, Shangong Wang, Wenhua Cheng, Yurong Chen
  • Publication number: 20200026988
    Abstract: 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. For each L layer in the plurality of layers, the nodes of each L layer are randomly connected to nodes in a L+1 layer. For each L+1 layer in the plurality of layers, the nodes of each L+1 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+1 layers are updated, and L is an integer starting with 1. In another example, a deep neural network includes an input layer, output layer, and a plurality of hidden layers. Inputs for the input layer and labels for the output layer are determined related to a first sample. Similarity between different pairs of inputs and labels between a second sample with the first sample is estimated using Gaussian regression process.
    Type: Application
    Filed: April 7, 2017
    Publication date: January 23, 2020
    Inventors: Yiwen Guo, Anbang Yao, Dongqi Cai, Libin Wang, Lin Xu, Ping Hu, Shangong Wang, Wenhua Cheng, Wenhua Cheng, Yurong Chen
  • Publication number: 20200026499
    Abstract: Described herein are hardware acceleration of random number generation for machine learning and deep learning applications. An apparatus (700) includes a uniform random number generator (URNG) circuit (710) to generate uniform random numbers and an adder circuit (750) that is coupled to the URNG circuit (710). The adder circuit hardware (750) accelerates generation of Gaussian random numbers for machine learning.
    Type: Application
    Filed: April 7, 2017
    Publication date: January 23, 2020
    Inventors: Yiwen Guo, Anbang Yao, Dongqi Cai, Libin Wang, Lin Xu, Ping Hu, Shangong Wang, Wenhua Cheng
  • Publication number: 20200026965
    Abstract: 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.
    Type: Application
    Filed: April 7, 2017
    Publication date: January 23, 2020
    Inventors: Yiwen GUO, Yuqing Hou, Anbang Yao, Dongqi Cai, Lin Xu, Ping Hu, Shangong Wang, Wenhua Cheng, Yurong Chen, Libin Wag
  • Publication number: 20200020070
    Abstract: An apparatus to facilitate compute optimization is disclosed. The apparatus includes a mixed precision core to perform a mixed precision multi-dimensional matrix multiply and accumulate operation on 8-bit and/or 32 bit signed or unsigned integer elements.
    Type: Application
    Filed: September 26, 2019
    Publication date: January 16, 2020
    Applicant: Intel Corporation
    Inventors: Abhishek R. Appu, Altug Koker, Linda L. Hurd, Dukhwan Kim, Mike B. Macpherson, John C. Weast, Feng Chen, Farshad Akhbari, Narayan Srinivasa, Nadathur Rajagopalan Satish, Joydeep Ray, Ping T. Tang, Michael S. Strickland, Xiaoming Chen, Anbang Yao, Tatiana Shpeisman
  • Publication number: 20200013212
    Abstract: Techniques are provided for facial image replacement between a reference facial image and a target facial image, of varying pose and illumination, using 3-dimensional morphable face models (3DMMs). A methodology implementing the techniques according to an embodiment includes fitting the reference face and the target face to a first and second 3DMM, respectively. The method further includes generating a texture map based on the fitted 3D reference face and rendering the fitted 3D reference face to a pose of the fitted 3D target face. The rendering is based on parameters of the first 3DMM, parameters of the second 3DMM, and the generated texture map associated with the fitted 3D reference face. The method further includes, determining a region of interest of the target facial image; and blending the rendered 3D reference face onto the region of interest of the target facial image to generate a replaced facial image.
    Type: Application
    Filed: April 4, 2017
    Publication date: January 9, 2020
    Applicant: INTEL CORPORATION
    Inventors: SHANDONG WANG, MING LU, ANBANG YAO, YURONG CHEN