Nec Laboratories America, Inc.

Nec Corporation

Shenghuo Zhu

Yihong Gong

Yun Chi

Kai Hung Yu

Ming M Yang

Yuanqing Lin

Xiaoyu Wang

Vahit Hakan Hacigumus

Jinjun Wang

Chang Huang

Junichi Tatemura

V Hakan Hacigumus

Pengcheng Xiong

Mei Han

Anelia Angelova

Hyun Jin Moon

Zhen Guo

Fengjun Lv

Koji Hino

Wentao Wu

Timothee Cour

Dingding Wang

Tianbao Yang

Qi Qian

Jenny Duggan

Methods and systems for distance metric learning include generating two random projection matrices of a dataset from a d-dimensional space into an m-dimensional sub-space, where m is smaller than d. An optimization problem is solved in the m-dimensional subspace to learn a distance metric based on the random projection matrices. The distance metric is recovered in the d-dimensional space.

Efficient distance metric learning for fine-grained visual categorization

Systems and methods are disclosed to perform performance prediction for cloud-based databases by building on a computer a cloud database performance model using a set of training workloads; and using a learned model on the computer to predict database performance in the cloud for a new workload, wherein for each reference workload r and hardware configuration h, system throughput t, average throughput of &#x3b1;and standard deviation &#x3c3;, comprising normalizing each throughput as: 

Portable workload performance prediction for the cloud

Systems and methods are disclosed for object detection by receiving an image and extracting features therefrom; applying a learning process to determine sub-regions and select predetermined pooling regions; and performing selective max-pooling to choose one or more feature regions without noises.

Selective max-pooling for object detection

Systems and methods are disclosed for object detection by receiving an image; segmenting the image; extracting features from the image; and performing a dimension-wise spatial layout selection to pick up dimensions inside a discriminative spatial region for classification.

Dimension-wise spatial layout importance selection: an alternative way to handle object deformation

Systems and methods for object detection by receiving an image; segmenting the image and identifying candidate bounding boxes which may contain an object; for each candidate bounding box, dividing the box into overlapped small patches, and extracting dense features from the patches; during a training phase, applying a learning process to learn one or more discriminative classification models to classify negative boxes and positive boxes; and during an operational phase, for a new box generated from the image, applying the learned classification model to classify whether the box contains an object.

Window dependent feature regions and strict spatial layout for object detection

Systems and methods are disclosed to search for a query image, by detecting local invariant features and local descriptors; retrieving best matching images by quantizing the local descriptors with a vocabulary tree; and reordering retrieved images with results from the vocabulary tree quantization.

Contextual weighting and efficient re-ranking for vocabulary tree based image retrieval

A method for fine-grained image classification on an image includes automatically segmenting one or more objects of interest prior to classification; and combining segmented and original image features before performing final classification.

Image segmentation for large-scale fine-grained recognition

A nearest-neighbor-based distance metric learning process includes applying an exponential-based loss function to provide a smooth objective; and determining an objective and a gradient of both hinge-based and exponential-based loss function in a quadratic time of the number of instances using a computer.

Fast methods of learning distance metric for classification and retrieval

Systems and methods for metric learning include iteratively determining feature groups of images based on its derivative norm. Corresponding metrics of the feature groups are learned by gradient descent based on an expected loss. The corresponding metrics are combined to provide an intermediate metric matrix as a sparse representation of the images. A loss function of all metric parameters corresponding to features of the intermediate metric matrix are optimized, using a processor, to learn a final metric matrix. Eigenvalues of the final metric matrix are projected onto a simplex.

Large-scale strongly supervised ensemble metric learning

There are provided a system and method for predicting query execution time in a database system. A cost model determination device determines a cost model of a database query optimizer for the database system. The cost model models costs of queries applied to the database system. A profiling device determines profiling queries for profiling input/output cost units and processor cost units relating to the database system, and profiles the cost units using the profiling queries to output profiled cost units. A calibrating device calibrates cost units in the cost model responsive to the profiled cost units to output calibrated cost units. A sampling re-estimator samples and re-estimates a cardinality estimation of a final query plan to output an updated cardinality estimation. A predictor applies the calibrated cost units and the updated cardinality estimation in the cost model to generate a prediction of an execution time of a given query.

Predicting query execution time

An admission control system for a cloud database includes a machine learning prediction module to estimate a predicted probability for a newly arrived query with a deadline, if admitted into the cloud database, to finish its execution before said deadline, wherein the prediction considers query characteristics and current system conditions. The system also includes a decision module applying the predicted probability to admit a query into the cloud database with a target of profit maximization with an expected profit determined using one or more service level agreements (SLAs).

Admission control in cloud databases under service level agreements

Systems and methods are disclosed for determining personal characteristics from images by generating a baseline gender model and an age estimation model using one or more convolutional neural networks (CNNs); capturing correspondences of faces by face tracking, and applying incremental learning to the CNNs and enforcing correspondence constraint such that CNN outputs are consistent and stable for one person.

Systems and methods for determining personal characteristics

Systems and methods are disclosed for generating super resolution images by building a set of multi-resolution bases from one or more training images; estimating a sparse resolution-invariant representation of an image, and reconstructing one or more missing patches at any resolution level.

Systems and methods for resolution-invariant image representation

Systems and methods are disclosed for summarizing multiple documents by generating a model of the documents as a mixture of document clusters, each document in turn having a mixture of sentences, wherein the model simultaneously representing summarization information and document cluster structure; and determining a loss function for evaluating the model and optimizing the model.

Multiple-document summarization using document clustering

Systems and methods are disclosed to manage resources in a cloud-based computing system by generating a model of a relationship between cloud database resources and an expected profit based on cloud-server system parameters and service level agreements (SLAs) that indicates profits for different system performances, wherein the model comprises a two level optimization/control problem, wherein model receives system metrics, number of replicas, and arrival rate as the multiple input; and dynamically adjusting resource allocation among different customers based on current customer workload and the expected profit to maximize the expected profit for a cloud computing service provider.

Growing community of inventors

Santa Clara, CA, United States of America

Shenghuo Zhu