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公开(公告)号：US20240266049A1
公开(公告)日：2024-08-08
申请号：US18425715
申请日：2024-01-29
发明人： Wenchao Yu , Haifeng Chen , Wei Cheng
摘要： Methods and systems for training a healthcare treatment machine learning model include aggregating local weights from a set of clients to update a set of global weights for an imitation-based skill learning model. A set of local prototype vectors are clustered from the plurality of clients to generate clusters. Representative vectors are selected for the clusters as a set of global prototypes. Client-specific prototype vectors are determined for the clients based on the representative vectors. The updated set of global weights and the client-specific prototype vectors are distributed to the clients.
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2.发明公开公开(公告)号：US20240231994A9
公开(公告)日：2024-07-11
申请号：US18493374
申请日：2023-10-24
发明人： Yuncong Chen , LuAn Tang , Yanchi Liu , Zhengzhang Chen , Haifeng Chen
IPC分类号： G06F11/07
CPC分类号： G06F11/079 , G06F11/0709 , G06F11/0793 , G16H50/20
摘要： Methods and systems for anomaly detection include encoding a multivariate time series and a multi-type event sequence using respective transformers and an aggregation network to generate a feature vector. Anomaly detection is performed using the feature vector to identify an anomaly within a system. A corrective action is performed responsive to the anomaly to correct or mitigate an effect of the anomaly. The detected anomaly can be used in a healthcare context to support decision making by medical professionals with respect to the treatment of a patient. The encoding may include machine learning models to implement the transformers and the aggregation network using deep learning.
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公开(公告)号：US20240062070A1
公开(公告)日：2024-02-22
申请号：US18450799
申请日：2023-08-16
发明人： Wenchao Yu , Haifeng Chen , Tianxiang Zhao
摘要： Methods and systems for training a model include performing skill discovery, using a set of demonstrations that includes known-good demonstrations and noisy demonstrations, to generate a set of skills. A unidirectional skill embedding model is trained in a first training while parameters of a skill matching model and low-level policies that relate skills to actions are held constant. The unidirectional skill embedding model, the skill matching model, and the low-level policies are trained together in an end-to-end fashion in a second training.
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公开(公告)号：US20240046127A1
公开(公告)日：2024-02-08
申请号：US18471558
申请日：2023-09-21
发明人： Wenchao Yu , Wei Cheng , Haifeng Chen , Yuncong Chen , Xuchao Zhang , Tianxiang Zhao
IPC分类号： G06N7/01
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：US20240046092A1
公开(公告)日：2024-02-08
申请号：US18484816
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：US20240006070A1
公开(公告)日：2024-01-04
申请号：US18370062
申请日：2023-09-19
发明人： Jingchao Ni , Wei Cheng , Haifeng Chen , Takayoshi Asakura
摘要： Systems and methods for predicting an occurrence of a medical event for a patient using a trained neural network. Historical patient data is preprocessed to generate normalized training samples, and the normalized training samples are sent to a personalized deep convolutional neural network for model pretraining and updating of model parameters. The pretrained model is stored in a remote server for utilization by a local machine for personalization during a preparation time period for a medical treatment. A normalized finetuning set is generated as output, and the model parameters are iteratively finetuned. A personal prediction score for future medical events is generated, and an operation of a medical treatment device is controlled responsive to the prediction score.
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7.发明公开公开(公告)号：US20240005156A1
公开(公告)日：2024-01-04
申请号：US18370140
申请日：2023-09-19
发明人： Jingchao Ni , Wei Cheng , Haifeng Chen
摘要： A computer-implemented method for model building is provided. The method includes receiving a training set of medical records and model hyperparameters. The method further includes initializing an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters. The method also includes performing, by a hardware processor, a forward computation to (1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities. The method additionally includes checking by a convergence evaluator if the iterative optimization has converged. The method further includes performing model personalization responsive to model convergence by encoding the support data of a new patient and using the embeddings and event subtype labels to train a personalized classifier.
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8.发明公开公开(公告)号：US20240005155A1
公开(公告)日：2024-01-04
申请号：US18370129
申请日：2023-09-19
发明人： Jingchao Ni , Wei Cheng , Haifeng Chen
摘要： A computer-implemented method for model building is provided. The method includes receiving a training set of medical records and model hyperparameters. The method further includes initializing an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters. The method also includes performing, by a hardware processor, a forward computation to (1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities. The method additionally includes checking by a convergence evaluator if the iterative optimization has converged. The method further includes performing model personalization responsive to model convergence by encoding the support data of a new patient and using the embeddings and event subtype labels to train a personalized classifier.
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公开(公告)号：US20230401851A1
公开(公告)日：2023-12-14
申请号：US18332057
申请日：2023-06-09
发明人： Xuchao Zhang , Xujiang Zhao , Yuncong Chen , Wenchao Yu , Haifeng Chen , Wei Cheng
CPC分类号： G06V20/44 , G06V20/52 , G06V10/82 , G06T2207/20081
摘要： Methods and systems for event detection include training a joint neural network model with respective neural networks for audio data and video data relating to a same scene. The joint neural network model is configured to output a belief value, a disbelief value, and an uncertainty value. It is determined that an event has occurred based on the belief value, the disbelief value, and the uncertainty value.
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公开(公告)号：US20230376589A1
公开(公告)日：2023-11-23
申请号：US18302908
申请日：2023-04-19
发明人： Zhengzhang Chen , Yuncong Chen , LuAn Tang , Haifeng Chen
CPC分类号： G06F21/552 , G06F21/577 , G06F2221/034 , G06F2221/2101
摘要： A method for detecting an origin of a computer attack given a detection point based on multi-modality data is presented. The method includes monitoring a plurality of hosts in different enterprise system entities to audit log data and metrics data, generating causal dependency graphs to learn statistical causal relationships between the different enterprise system entities based on the log data and the metrics data, detecting a computer attack by pinpointing attack detection points, backtracking from the attack detection points by employing the causal dependency graphs to locate an origin of the computer attack, and analyzing computer attack data resulting from the backtracking to prevent present and future computer attacks.
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