公开(公告)号：US20240054348A1

公开(公告)日：2024-02-15

申请号：US18327619

申请日：2023-06-01

申请人： General Electric Company

发明人： Yiwei Fu ,  Nurali Virani ,  Honggang Wang ,  Benoit Christophe

IPC分类号： G06N3/0895

CPC分类号： G06N3/0895

摘要： A system includes a computing device including at least one processor in communication with at least one memory. The at least one processor is programmed to (a) store a plurality of historical time series data; (b) randomly select a sequence; (c) randomly select a mask length for a mask for the selected sequence; (d) apply the mask to the selected sequence, wherein the mask is applied to the plurality of forecast variables in the selected sequence; (e) execute a model with the masked selected sequence to generate predictions for the masked forecast variables; (f) compare the predictions for the masked forecast variables to the actual forecast variables in the selected sequence; (g) determine if convergence occurs based upon the comparison; and (h) if convergence has not occurred, update one or more parameters of the model and return to step b.

公开(公告)号：US20230419120A1

公开(公告)日：2023-12-28

申请号：US18247348

申请日：2020-10-05

申请人： NIPPON TELEGRAPH AND TELEPHONE CORPORATION

发明人： Tomoharu IWATA

IPC分类号： G06N3/0895

CPC分类号： G06N3/0895

摘要： A learning method according to an embodiment causes a computer to execute: an input step of inputting a plurality of data sets; and a learning step of learning, based on the plurality of input data sets, an estimation model for estimating a parameter of a topic model from a smaller amount of data than an amount ot data included in the plurality of data sets.

公开(公告)号：US20230290234A1

公开(公告)日：2023-09-14

申请号：US18247845

申请日：2022-07-01

申请人： NANJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS

发明人： Xin WEI ,  Liang ZHOU ,  Yingying SHI ,  Zhe ZHANG ,  Siqi ZHANG

IPC分类号： G08B6/00 ,  G06V20/40 ,  G06V10/80 ,  G06N3/0455 ,  G06N3/0895 ,  G06N3/084

CPC分类号： G08B6/00 ,  G06V20/46 ,  G06V10/806 ,  G06N3/0455 ,  G06N3/0895 ,  G06N3/084

摘要： An audio visual haptic signal reconstruction method includes first utilizing a large-scale audio-visual database stored in a central cloud to learn knowledge, and transferring same to an edge node; then combining, by means of the edge node, a received audio-visual signal with knowledge in the central cloud, and fully mining semantic correlation and consistency between modals; and finally fusing the semantic features of the obtained audio and video signals and inputting the semantic features to a haptic generation network, thereby realizing the reconstruction of the haptic signal. The method effectively solves the problems that the number of audio and video signals of a multi-modal dataset is insufficient, and semantic tags cannot be added to all the audio-visual signals in a training dataset by means of manual annotation. Also, the semantic association between heterogeneous data of different modals are better mined, and the heterogeneity gap between modals are eliminated.

公开(公告)号：US20230237341A1

公开(公告)日：2023-07-27

申请号：US18156882

申请日：2023-01-19

申请人： JPMORGAN CHASE BANK, N.A.

发明人： Georgios PAPADOPOULOS ,  Fanny SILAVONG ,  Sean MORAN ,  Rob OTTER ,  Brett SANFORD

IPC分类号： G06N3/0895 ,  G06N3/0475

CPC分类号： G06N3/0895 ,  G06N3/0475

摘要： Systems and methods for weak supervision classification with probabilistic generative latent variable models are disclosed. A method for weak supervision classification with probabilistic generative latent variable models may include: (1) receiving, by a generative model computer program, a plurality of records from a database; (2) receiving, by the generative model computer program, a plurality of user-defined label functions; (3) labeling, by the generative model computer program, each of the plurality of records with each of the plurality of user-defined label functions; (4) representing, by the generative model computer program, the plurality of records that are labeled with the user-defined label functions in a matrix; (5) performing, by the generative model computer program, probabilistic latent variable model analysis on the matrix using a probabilistic generative latent variable model; and (6) outputting, by the generative model computer program, a labeled dataset for the plurality of records.

公开(公告)号：US20230214423A1

公开(公告)日：2023-07-06

申请号：US18174481

申请日：2023-02-24

申请人： BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

发明人： Haifeng WANG ,  Hao TIAN ,  Xinyan XIAO ,  Xing LI ,  Tian WU

IPC分类号： G06F16/783 ,  G06F16/73 ,  G06N3/0895 ,  G06F40/30 ,  G06F40/40 ,  G06F40/295 ,  G10L15/22 ,  G10L15/18 ,  G10L15/16 ,  G10L25/57

CPC分类号： G06F16/7844 ,  G06F16/73 ,  G06F40/30 ,  G06F40/40 ,  G06F40/295 ,  G06N3/0895 ,  G10L15/16 ,  G10L15/22 ,  G10L15/1815 ,  G10L25/57

摘要： A video generation method is provided. The video generation method includes: obtaining global semantic information and local semantic information of a text, where the local semantic information corresponds to a text fragment in the text, searching, based on the global semantic information, a database to obtain at least one first data corresponding to the global semantic information; searching, based on the local semantic information, the database to obtain at least one second data corresponding to the local semantic information; obtaining, based on the at least one first data and the at least one second data, a candidate data set; matching, based on a relevancy between each of at least one text fragment and corresponding candidate data in the candidate data set, target data for the at least one text fragment; and generating, based on the target data matched with each of the at least one text fragment, a video.

公开(公告)号：US20240362459A1

公开(公告)日：2024-10-31

申请号：US18309438

申请日：2023-04-28

申请人： International Business Machines Corporation

发明人： Michael KAZDA ,  Michael Daniel MONKOWSKI

IPC分类号： G06N3/0455 ,  G06F30/327 ,  G06N3/0895

CPC分类号： G06N3/0455 ,  G06F30/327 ,  G06N3/0895

摘要： Embodiments of the present disclosure provide enhanced systems and methods for predicting optimal design flow parameters for optimized output targets for physical design synthesis of a given IC design. A Variational Autoencoder (VAE) along with a regression network are trained using a dataset comprising synthesis design construction flows from historical IC designs to provide a training data representation of the dataset constrained to a latent space of the VAE. The system generates feature vectors based on the training data representation of the dataset and updates the feature vectors with initial design characteristics of the given IC design. The system iteratively performs an input gradient search of the updated feature vectors to optimize an objective function of the design targets to identify locally optimal design parameters. The system identifies globally optimal design flow parameters for optimized design targets based on locally optimal design parameters.

公开(公告)号：US20240347200A1

公开(公告)日：2024-10-17

申请号：US18636128

申请日：2024-04-15

申请人： Exai Bio, Inc.

发明人： Babak Alipanahi ,  Hani Goodarzi ,  Fereydoun Hormozdiari ,  Mehran Karimzadeh

IPC分类号： G16H50/20 ,  G06N3/0455 ,  G06N3/0895 ,  G16B40/20

CPC分类号： G16H50/20 ,  G06N3/0455 ,  G06N3/0895 ,  G16B40/20

摘要： Embodiments described herein provide a neural network based cancer detection and subtyping tool for predicting the presence of a tumor, its tissue of origin, and its subtype using small RNA sequencing (smRNA-seq) data, for example, the oncRNA count data. Specifically, the AI-based cancer detection and subtyping tool uses variational Bayes inference and semi-supervised training to adjust for batch effects and learn a low dimensional distribution explaining biological variability of the data. A method is also provided for determining the likely subtype(s) in a cancer sample.

公开(公告)号：US20240345551A1

公开(公告)日：2024-10-17

申请号：US18419464

申请日：2024-01-22

申请人： Johnson Controls Tyco IP Holdings LLP

发明人： Rajiv Ramanasankaran ,  Krishnamurthy Selvaraj

IPC分类号： G05B13/02 ,  G06F16/248 ,  G06N3/0895

CPC分类号： G05B13/027 ,  G06F16/248 ,  G06N3/0895

摘要： Systems and methods are disclosed relating to building management systems with language model-based data structure generation. For example, a method can include receiving a query to select, from a plurality of data sources of a building management system, a selected one or more data sources according to a characteristic indicated by the query in at least one of a natural language representation or a semantic representation. The method can further include applying the query as input to a machine learning model to cause the machine learning model to generate an output indicating the selected one or more data sources, the machine learning model configured using training data comprising sample data and metadata from the plurality of data sources. The method can further include presenting, using at least one of a display device or an audio output device, the output.

公开(公告)号：US20240320500A1

公开(公告)日：2024-09-26

申请号：US18034418

申请日：2022-06-22

申请人： JINA AI GMBH ,  JINA AI (BEIJING) CO., LTD.

发明人： Han XIAO ,  Nan WANG ,  Bo WANG ,  Werk MAXIMILIAN ,  Mastrapas GEORGIOS

IPC分类号： G06N3/0895

CPC分类号： G06N3/0895

摘要： A method and an apparatus for generating training data are provided. The training data is used for training a target deep learning model. In the method, original data for generating the target deep learning model is obtained from a user. Then, a type of the original data is determined. The type of the original data includes categorical data with label, session data with label, and data without label. A label of the categorical data indicates a category of the categorical data. A label of the session data indicates a question-answer relevance of the session data. Next, the training data is generated according to the type of the original data.

公开(公告)号：US12086721B1

公开(公告)日：2024-09-10

申请号：US18600487

申请日：2024-03-08

申请人： The Strategic Coach Inc.

发明人： Barbara Sue Smith ,  Daniel J. Sullivan

IPC分类号： G06N3/092 ,  G06N3/088 ,  G06N3/0895 ,  G06N3/09

CPC分类号： G06N3/092 ,  G06N3/088 ,  G06N3/0895 ,  G06N3/09

摘要： The apparatus employs adaptive machine learning for model selection based on data complexity and user goals. It consists of a processor and memory. Initially, it creates a first model from a dataset and analytic goals. Then, it determines a complexity metric for another dataset, compares this metric to a set threshold, and identifies a complexity gap. Using a feature learning algorithm, it extracts candidate features from the second dataset. From these features, it generates a second model. The device assesses this model's performance using a third dataset and selects it based on its relation to the complexity gap.