公开(公告)号：US20210133540A1

公开(公告)日：2021-05-06

申请号：US17058428

申请日：2019-03-14

Applicant: The Trustees of Princeton University

Inventor： Xiaoliang DAI ,  Hongxu YIN ,  Niraj K. JHA

IPC: G06N3/04 ,  G06N3/063

Abstract: According to various embodiments, a method for generating an optimal hidden-layer long short-term memory (H-LSTM) architecture is disclosed. The H-LSTM architecture includes a memory cell and a plurality of deep neural network (DNN) control gates enhanced with hidden layers. The method includes providing an initial seed H-LSTM architecture, training the initial seed H-LSTM architecture by growing one or more connections based on gradient information and iteratively pruning one or more connections based on magnitude information, and terminating the iterative pruning when training cannot achieve a predefined accuracy threshold.

公开(公告)号：US20210182683A1

公开(公告)日：2021-06-17

申请号：US16760209

申请日：2018-10-25

Applicant: The Trustees of Princeton University

Inventor： Xiaoliang DAI ,  Hongxu YIN ,  Niraj K. JHA

IPC: G06N3/08 ,  G06N3/04

Abstract: According to various embodiments, a method for generating one or more optimal neural network architectures is disclosed. The method includes providing an initial seed neural network architecture and utilizing sequential phases to synthesize the neural network until a desired neural network architecture is reached. The phases include a gradient-based growth phase and a magnitude-based pruning phase.

公开(公告)号：US20250078998A1

公开(公告)日：2025-03-06

申请号：US18235422

申请日：2022-02-01

Applicant: The Trustees of Princeton University

Inventor： Shayan HASSANTABAR ,  Zhao ZHANG ,  Hongxu YIN ,  Niraj K. JHA

IPC: G16H50/20 ,  G16H40/67

Abstract: According to various embodiments, a machine-learning based system for mental health disorder identification and monitoring is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs. The processors are further configured to train at least one neural network based on raw physiological data augmented with synthetic data and subjected to a grow-and-prune paradigm to generate at least one mental health disorder inference model. The processors are also configured to output a mental health disorder-based decision by inputting the received physiological data into the generated mental health disorder inference model.

公开(公告)号：US20220222534A1

公开(公告)日：2022-07-14

申请号：US17613284

申请日：2020-03-20

Applicant: The Trustees of Princeton University

Inventor： Xiaoliang DAI ,  Hongxu YIN ,  Niraj K. JHA

IPC: G06N3/08 ,  G06N3/04

Abstract: According to various embodiments, a method for generating a compact and accurate neural network for a dataset that has initial data and is updated with new data is disclosed. The method includes performing a first training on the initial neural network architecture to create a first trained neural network architecture. The method additionally includes performing a second training on the first trained neural network architecture when the dataset is updated with new data to create a second trained neural network architecture. The second training includes growing one or more connections for the new data based on a gradient of each connection, growing one or more connections for the new data and the initial data based on a gradient of each connection, and iteratively pruning one or more connections based on a magnitude of each connection until a desired neural network architecture is achieved.