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公开(公告)号：US20240054373A1
公开(公告)日：2024-02-15
申请号：US18471570
申请日：2023-09-21
发明人： Wenchao Yu , Wei Cheng , Haifeng Chen , Yuncong Chen , Xuchao Zhang , Tianxiang Zhao
摘要： A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.
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公开(公告)号：US20240046091A1
公开(公告)日：2024-02-08
申请号：US18484793
申请日：2023-10-11
发明人： Wenchao Yu , Wei Cheng , Haifeng Chen , Yiwei Sun
摘要： A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.
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公开(公告)号：US20220058240A9
公开(公告)日：2022-02-24
申请号：US16987734
申请日：2020-08-07
发明人： Wei Cheng , Haifeng Chen , Jingchao Ni , Dongkuan Xu , Wenchao Yu
摘要： A method for unsupervised multivariate time series trend detection for group behavior analysis is presented. The method includes collecting multi-variate time series data from a plurality of sensors, learning piecewise linear trends jointly for all of the multi-variate time series data, dividing the multi-variate time series data into a plurality of time segments, counting a number of up/down trends in each of the plurality of time segments, for a training phase, employing a cumulative sum (CUSUM), and, for a testing phase, monitoring the CUSUM for trend changes.
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公开(公告)号：US20210319847A1
公开(公告)日：2021-10-14
申请号：US17197166
申请日：2021-03-10
发明人： Renqiang Min , Wenchao Yu , Hans Peter Graf , Igor Durdanovic
摘要： A method is provided for peptide-based vaccine generation. The method receives a dataset of positive and negative binding peptide sequences. The method pre-trains a set of peptide binding property predictors on the dataset to generate training data. The method trains a Wasserstein Generative Adversarial Network (WGAN) only on the positive binding peptide sequences, in which a discriminator of the WGAN is updated to distinguish generated peptide sequences from sampled positive peptide sequences from the training data, and a generator of the WGAN is updated to fool the discriminator. The method trains the WGAN only on the positive binding peptide sequences while simultaneously updating the generator to minimize a kernel Maximum Mean Discrepancy (MMD) loss between the generated peptide sequences and the sampled peptide sequences and maximize prediction accuracies of a set of pre-trained peptide binding property predictors with parameters of the set of pre-trained peptide binding property predictors being fixed.
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公开(公告)号：US20210103706A1
公开(公告)日：2021-04-08
申请号：US17060850
申请日：2020-10-01
发明人： Wenchao Yu , Bo Zong , Wei Cheng , Haifeng Chen , Xiusi Chen
摘要： Methods and systems for performing a knowledge graph task include aligning multiple knowledge graphs and performing a knowledge graph task using the aligned multiple knowledge graphs. Aligning the multiple knowledge graphs includes updating entity representations based on representations of neighboring entities within each knowledge graph, updating entity representations based on representations of entities from different knowledge graphs, and learning machine learning model parameters to align the multiple knowledge graphs, based on the updated entity representations.
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公开(公告)号：US20190130212A1
公开(公告)日：2019-05-02
申请号：US16169184
申请日：2018-10-24
发明人： Wei Cheng , Haifeng Chen , Kenji Yoshihira , Wenchao Yu
摘要： Methods and systems for embedding a network in a latent space include generating a representation of an input network graph in the latent space using an autoencoder model and generating a representation of a set of noise samples in the latent space using a generator model. A discriminator model discriminates between the representation of the input network graph and the representation of the set of noise samples. The autoencoder model, the generator model, and the discriminator model are jointly trained by minimizing a joint loss function that includes parameters for each model. A final representation of the input network graph is generated using the trained autoencoder model.
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公开(公告)号：US20240104393A1
公开(公告)日：2024-03-28
申请号：US18466333
申请日：2023-09-13
发明人： Wei Cheng , Wenchao Yu , Haifeng Chen , Yue Wu
IPC分类号： G06N3/098
CPC分类号： G06N3/098
摘要： Systems and methods for personalized federated learning. The method may include receiving at a central server local models from a plurality of clients, and aggregating a heterogeneous data distribution extracted from the local models. The method can further include processing the data distribution as a linear mixture of joint distributions to provide a global learning model, and transmitting the global learning model to the clients. The global learning model is used to update the local model.
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公开(公告)号：US20240037400A1
公开(公告)日：2024-02-01
申请号：US18484805
申请日：2023-10-11
发明人： Wenchao Yu , Wei Cheng , Haifeng Chen , Yiwei Sun
摘要： A method for acquiring skills through imitation learning by employing a meta imitation learning framework with structured skill discovery (MILD) is presented. The method includes learning behaviors or tasks, by an agent, from demonstrations: by learning to decompose the demonstrations into segments, via a segmentation component, the segments corresponding to skills that are transferrable across different tasks, learning relationships between the skills that are transferrable across the different tasks, employing, via a graph generator, a graph neural network for learning implicit structures of the skills from the demonstrations to define structured skills, and generating policies from the structured skills to allow the agent to acquire the structured skills for application to one or more target tasks.
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公开(公告)号：US20230080424A1
公开(公告)日：2023-03-16
申请号：US17877081
申请日：2022-07-29
发明人： Wenchao Yu , Wei Cheng , Haifeng Chen , Yuncong Chen , Xuchao Zhang , Tianxiang Zhao
IPC分类号： G06N7/00
摘要： A method for learning a self-explainable imitator by discovering causal relationships between states and actions is presented. The method includes obtaining, via an acquisition component, demonstrations of a target task from experts for training a model to generate a learned policy, training the model, via a learning component, the learning component computing actions to be taken with respect to states, generating, via a dynamic causal discovery component, dynamic causal graphs for each environment state, encoding, via a causal encoding component, discovered causal relationships by updating state variable embeddings, and outputting, via an output component, the learned policy including trajectories similar to the demonstrations from the experts.
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公开(公告)号：US11544530B2
公开(公告)日：2023-01-03
申请号：US16662754
申请日：2019-10-24
发明人： Wei Cheng , Wenchao Yu , Haifeng Chen
摘要： Methods and systems for determining a network embedding include training a network embedding model using training data that includes topology information for networks and attribute information relating to vertices of the networks. An embedded representation is generated using the trained network embedding model to represent an input network, with associated attribute information, in a network topology space. A machine learning task is performed using the embedded representation as input to a machine learning model.
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