公开(公告)号：US20190079999A1

公开(公告)日：2019-03-14

申请号：US16038858

申请日：2018-07-18

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Dinghan Shen ,  Yitong Li

IPC: G06F17/30 ,  H04L12/58 ,  G06N5/04 ,  G06N3/04 ,  G06F17/27 ,  H03M7/30

Abstract: A system for electronic message classification and delivery using a neural network architecture includes one or more computing devices associated with one or more users, and at least one computer processing system in communication with one or more computing devices over at least one network. The at least one computer processing system includes at least one processor operatively coupled to a memory device and configured to execute program code stored on the memory device to receive one or more inputs associated with one or more e-mails corresponding to the one or more users across the at least one network, classify the one or more e-mails by performing natural language processing based on one or more sets of filters conditioned on respective ones of the one or more inputs, and permit the one or more users access to the one or more classified e-mails via the one or more computing devices.

公开(公告)号：US20170293543A1

公开(公告)日：2017-10-12

申请号：US15478753

申请日：2017-04-04

Applicant: NEC Laboratories America, Inc.

Inventor： Jianwu Xu ,  Ke Zhang ,  Hui Zhang ,  Renqiang Min ,  Guofei Jiang

IPC: G06F11/22 ,  G06N99/00 ,  G06N5/04 ,  G06N3/04

CPC classification number: G06F11/2263 ,  G06N3/0445 ,  G06N5/04 ,  G06N99/005

Abstract: Mobile phones and methods for mobile phone failure prediction include receiving respective log files from one or more mobile phone components, including at least one user application. The log files have heterogeneous formats. A likelihood of failure of one or more mobile phone components is determined based on the received log files by clustering the plurality of log files according to structural log patterns and determining feature representations of the log files based on the log clusters. A user is alerted to a potential failure if the likelihood of component failure exceeds a first threshold. An automatic system control action is performed if the likelihood of component failure exceeds a second threshold.

公开(公告)号：US20160259887A1

公开(公告)日：2016-09-08

申请号：US15049983

申请日：2016-02-22

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Sanjay Purushotham

IPC: G06F19/24 ,  G06N99/00

CPC classification number: G16B40/00

Abstract: An optimization-driven sparse learning framework is disclosed to identify discriminative system components among system input features that are essential for system output prediction. In biomarker discovery, to handle the combinatorial interactions among gene or protein expression measurements for identifying interaction complexes and disease biomarkers, the system uses both single input features and high-order input feature interactions.

Abstract translation: 公开了优化驱动的稀疏学习框架，以识别对系统输出预测至关重要的系统输入特征之间的区分系统组件。 在生物标志物发现中，为了处理识别相互作用复合物和疾病生物标志物的基因或蛋白质表达测量之间的组合相互作用，系统使用单一输入特征和高阶输入特征相互作用。

公开(公告)号：US20160098633A1

公开(公告)日：2016-04-07

申请号：US14844520

申请日：2015-09-03

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Methods and systems for training a neural network include pre-training a bi-linear, tensor-based network, separately pre-training an auto-encoder, and training the bi-linear, tensor-based network and auto-encoder jointly. Pre-training the bi-linear, tensor-based network includes calculating high-order interactions between an input and a transformation to determine a preliminary network output and minimizing a loss function to pre-train network parameters. Pre-training the auto-encoder includes calculating high-order interactions of a corrupted real network output, determining an auto-encoder output using high-order interactions of the corrupted real network output, and minimizing a loss function to pre-train auto-encoder parameters.

Abstract translation: 用于训练神经网络的方法和系统包括预训练基于双线性张拉的网络，分别对自动编码器进行预训练，并且共同训练基于双线性，基于张力的网络和自动编码器。 预训练双线性，基于张量的网络包括计算输入和转换之间的高阶交互以确定初步网络输出并最小化损失函数以预先训练网络参数。 预编程自动编码器包括计算已损坏的实际网络输出的高阶交互，使用损坏的实际网络输出的高阶交互来确定自动编码器输出，以及最小化损失函数以预先列出自动编码器 参数。

公开(公告)号：US20140310218A1

公开(公告)日：2014-10-16

申请号：US14243311

申请日：2014-04-02

Applicant: NEC LABORATORIES AMERICA, INC.

Inventor： Renqiang Min ,  Bing Bai

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Systems and method are disclosed for determining complex interactions among system inputs by using semi-Restricted Boltzmann Machines (RBMs) with factorized gated interactions of different orders to model complex interactions among system inputs; applying semi-RBMs to train a deep neural network with high-order within-layer interactions for learning a distance metric and a feature mapping; and tuning the deep neural network by minimizing margin violations between positive query document pairs and corresponding negative pairs.

Abstract translation: 公开了系统和方法，用于通过使用半限制玻尔兹曼机器（RBM）与系统输入之间的复杂相互作用的分解门控交互来确定系统输入之间的复杂相互作用; 应用半RBM来训练具有高阶层内交互的深层神经网络，用于学习距离度量和特征映射; 并通过最小化正查询文档对和对应的负对之间的边缘违规来调整深层神经网络。

公开(公告)号：US20250157353A1

公开(公告)日：2025-05-15

申请号：US18944762

申请日：2024-11-12

Applicant: NEC Laboratories America, Inc.

Inventor： Honglu Zhou ,  Renqiang Min

IPC: G09B5/06 ,  G09B23/28

Abstract: Systems and methods include predicting a first action step and a last action step based on an initial visual observation and a goal visual state and retrieving multiple procedural plans from a procedural knowledge graph (PKG), trained using a set of training instructional videos, which start with the first action step and end with the last action step. A procedure plan is generated using the retrieved multiple procedural plans. An instructional video is generated based on the procedure plan.

公开(公告)号：US12288389B2

公开(公告)日：2025-04-29

申请号：US17585754

申请日：2022-01-27

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Hans Peter Graf ,  Ligong Han

IPC: G06V10/82 ,  G06N3/045 ,  G06V10/62 ,  G06V10/77 ,  G06V10/80

Abstract: A method for learning disentangled representations of videos is presented. The method includes feeding each frame of video data into an encoder to produce a sequence of visual features, passing the sequence of visual features through a deep convolutional network to obtain a posterior of a dynamic latent variable and a posterior of a static latent variable, sampling static and dynamic representations from the posterior of the static latent variable and the posterior of the dynamic latent variable, respectively, concatenating the static and dynamic representations to be fed into a decoder to generate reconstructed sequences, and applying three regularizers to the dynamic and static latent variables to trigger representation disentanglement. To facilitate the disentangled sequential representation learning, orthogonal factorization in generative adversarial network (GAN) latent space is leveraged to pre-train a generator as a decoder in the method.

公开(公告)号：US20240185948A1

公开(公告)日：2024-06-06

申请号：US18414645

申请日：2024-01-17

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Hans Peter Graf ,  Ziqi Chen

IPC: G16B15/30 ,  G06N20/00 ,  G16B20/50 ,  G16B40/20

CPC classification number: G16B15/30 ,  G06N20/00 ,  G16B20/50 ,  G16B40/20

Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy is presented. The method includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from target patients, predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients, developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions based on a reconstruction-based score and a density estimation-based score, randomly sampling batches of TCRs and following a policy network to mutate the TCRs, outputting mutated TCRs, and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.

公开(公告)号：US20240177798A1

公开(公告)日：2024-05-30

申请号：US18414670

申请日：2024-01-17

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Hans Peter Graf ,  Ziqi Chen

IPC: G16B15/30 ,  G06N20/00 ,  G16B20/50 ,  G16B40/20

CPC classification number: G16B15/30 ,  G06N20/00 ,  G16B20/50 ,  G16B40/20

Abstract: A method for implementing deep reinforcement learning with T-cell receptor (TCR) mutation policies to generate binding TCRs recognizing target peptides for immunotherapy is presented. The method includes extracting peptides to identify a virus or tumor cells, collecting a library of TCRs from target patients, predicting, by a deep neural network, interaction scores between the extracted peptides and the TCRs from the target patients, developing a deep reinforcement learning (DRL) framework with TCR mutation policies to generate TCRs with maximum binding scores, defining reward functions based on a reconstruction-based score and a density estimation-based score, randomly sampling batches of TCRs and following a policy network to mutate the TCRs, outputting mutated TCRs, and ranking the outputted TCRs to utilize top-ranked TCR candidates to target the virus or the tumor cells for immunotherapy.

公开(公告)号：US20240029823A1

公开(公告)日：2024-01-25

申请号：US18479423

申请日：2023-10-02

Applicant: NEC Laboratories America, Inc.

Inventor： Renqiang Min ,  Hans Peter Graf ,  Ligong Han

IPC: G16B15/30 ,  G06N3/08 ,  G16B40/20 ,  G06N3/045

CPC classification number: G16B15/30 ,  G06N3/08 ,  G16B40/20 ,  G06N3/045

Abstract: A computer-implemented method is provided for generating new binding peptides to Major Histocompatibility Complex (MHC) proteins. The method includes training, by a processor device, a Generative Adversarial Network GAN having a generator and a discriminator only on a set of binding peptide sequences given training data comprising the set of binding peptide sequences and a set of non-binding peptide sequences. A GAN training objective includes the discriminator being iteratively updated to distinguish generated peptide sequences from sampled binding peptide sequences as fake or real and the generator being iteratively updated to fool the discriminator. The training includes optimizing the GAN training objective while learning two projection vectors for a binding class with two cross-entropy losses. A first loss discriminating binding peptide sequences in the training data from non-binding peptide sequences in the training data. A second loss discriminating generated binding peptide sequences from non-binding peptide sequences in the training data.