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Found 11 papers, 5 papers with code
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems.
A Comprehensive Review of Digital Twin -- Part 2: Roles of Uncertainty Quantification and Optimization, a Battery Digital Twin, and Perspectives
This second paper presents a literature review of key enabling technologies of digital twins, with an emphasis on uncertainty quantification, optimization methods, open source datasets and tools, major findings, challenges, and future directions.
A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies
In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared.
Deep Transfer Convolutional Neural Network and Extreme Learning Machine for Lung Nodule Diagnosis on CT images
Some content of the article needs to be kept secret
CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension
We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers.
CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain
In this paper, we introduce CAIL2019-SCM, Chinese AI and Law 2019 Similar Case Matching dataset.
Overview of CAIL2018: Legal Judgment Prediction Competition
In this paper, we give an overview of the Legal Judgment Prediction (LJP) competition at Chinese AI and Law challenge (CAIL2018).
CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction
In this paper, we introduce the \textbf{C}hinese \textbf{AI} and \textbf{L}aw challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for judgment prediction.
Multi-pretrained Deep Neural Network
In this paper, we pretrained deep neutral network by different pretraining models and hence investigated the difference between DBN and Stacked Denoising Autoencoder (SDA) when used as pretraining model.
On the Complexity of One-class SVM for Multiple Instance Learning
However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms.
Audio Classical Composer Identification by Deep Neural Network
We think our method is promising even though we test it in a different data set, since our data set is comparable to that in MIREX by size.
