相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article
Computer Science, Artificial Intelligence
Hao (Frank) Yang et al.
Summary: Managing and estimating parking lot availability information is crucial for travelers and managers, but it is challenging due to various factors. A comprehensive framework for real-time Smart Parking Data Management and Prediction (SPDMP) system has been proposed, with a customized neural network integrating historical and real-time data for better prediction accuracy. Through representation learning and feature embedding, the system achieves superior results for both urban and truck parking prediction tasks.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Mingrui Zhu et al.
Summary: This article investigates the impact of knowledge distillation (KD) on training neural networks for face photo-sketch synthesis and proposes an effective KD model to improve the performance of synthetic images. By applying the knowledge from a teacher network to separately learn face photo and face sketch knowledge, and transferring this knowledge to student networks, the learning ability is enhanced. A KD+ model combining GANs and KD is proposed to improve the perceptual quality of synthetic images. Extensive experiments demonstrate the superiority of the proposed models.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Ting-Bing Xu et al.
Summary: This article proposes an elegant self-distillation mechanism to directly obtain high-accuracy models without the need for an assistive model. It learns data representation invariance and effectively reduces generalization errors for various network architectures, surpassing existing model distillation methods with little extra training efforts.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Weizhi Liao et al.
Summary: A new encoder-decoder model based on dynamic residual network is proposed in this work to improve LSTM's long sequence dependencies by dynamically selecting an optimal state and simulating word dependence using reinforcement learning. Experimental results demonstrate significant improvements in capturing long-term dependencies compared to traditional LSTM-based Seq2Seq abstractive summarization model.
Article
Computer Science, Artificial Intelligence
Bing Bai et al.
Summary: The study introduces a novel ensemble method called TBOPE, which is based on multi-feature dictionary representation and ensemble learning. By extracting multiple dimensions of features and constructing multiple classifiers, the method aims to improve the classification performance of time series data.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jianping Gou et al.
Summary: This paper provides a comprehensive survey of knowledge distillation, covering knowledge categories, training schemes, teacher-student architecture, distillation algorithms, performance comparison, and applications. It also briefly reviews challenges in knowledge distillation and discusses future research directions.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2021)
Article
Computer Science, Artificial Intelligence
Wenbo Zheng et al.
Summary: This study proposes a novel framework called Unsupervised Meta-Learning to differentiate COVID-19 from pneumonia cases, achieving significantly higher diagnostic accuracy through self-learning during model training.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Zhiwen Xiao et al.
Summary: This paper introduces a novel multi-process collaborative architecture for time series classification, which combines multi-head convolutional neural networks and capsule mechanism to achieve better feature extraction and representation learning without the need for reconstruction to improve model accuracy.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Nikolaos Passalis et al.
Summary: The novel probabilistic knowledge transfer (PKT) method proposed in this article allows for transferring knowledge from a large deep learning model to a smaller, faster model by retaining as much information as possible, expressed through the teacher model. PKT outperforms existing state-of-the-art KT techniques and enables novel applications, demonstrated through extensive experiments on challenging data sets.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Filippo Maria Bianchi et al.
Summary: This article introduces an unsupervised approach based on reservoir computing to learn vector representations of multivariate time series, and proposes a modular RC framework for MTS classification. Experimental results show that RC classifiers are faster and achieve higher accuracy when using the proposed representation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Computer Science, Information Systems
Zhiwen Xiao et al.
Summary: Time series data contains both local and global patterns, but existing feature networks focus on local features and neglect the relationships among them. Therefore, a novel RTFN method is proposed for feature extraction in time series, consisting of TFN and LSTMaN. Experimental results show that the RTFN-based structures achieve excellent performance on multiple datasets.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Zhen Cheng et al.
Summary: Deep autoencoder-based methods are the majority of deep anomaly detection, but they may have poor performance when distinguishing anomalies from normal data. To address this issue, an Improved AutoEncoder for unsupervised Anomaly Detection (IAEAD) is proposed, which optimizes for anomaly detection tasks and learns representations that preserve the local data structure.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Chenghao Diao et al.
Summary: AI plays a crucial role in driving the carbon-neutral energy revolution, with a focus on neural networks. The study introduces the DL-CNN architecture for performance prediction in power cycles, showing that DL-CNN outperforms FC-NN in terms of prediction accuracy.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Jann Goschenhofer et al.
Summary: This study investigates the feasibility of transferring state-of-the-art deep semi-supervised models from image to time series classification, emphasizing necessary model adaptations and tailored data augmentation strategies. Through evaluations on large public time series classification problems, the transferred semi-supervised models show significant performance gains, especially in scenarios with very few labeled samples.
20TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhiwen Xiao et al.
Summary: In this paper, a robust neural temporal search (RNTS) framework is proposed for analyzing TSC data, which combines temporal search network and attentional LSTM network to extract basic features and explore complex relationships. Experimental results show that the framework outperforms several state-of-the-art approaches on 24 standard datasets from the UCR 2018 archive in terms of top-1 accuracy-based measures.
2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2021)
Article
Engineering, Electrical & Electronic
Wanli Peng et al.
Summary: This paper proposes an effective and real-time text steganalysis method based on multi-stage transfer learning to enhance inference efficiency and detection performance simultaneously. The experimental results show that the proposed method outperforms previously reported methods in terms of detection accuracy and inference efficiency.
IEEE SIGNAL PROCESSING LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Huagang Tong et al.
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Hassan Ismail Fawaz et al.
DATA MINING AND KNOWLEDGE DISCOVERY
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Shayan Jawed et al.
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2020, PT I
(2020)
Review
Computer Science, Artificial Intelligence
Hassan Ismail Fawaz et al.
DATA MINING AND KNOWLEDGE DISCOVERY
(2019)
Article
Computer Science, Artificial Intelligence
Haishuai Wang et al.
PATTERN RECOGNITION
(2019)
Article
Computer Science, Artificial Intelligence
Fazle Karim et al.
Proceedings Paper
Computer Science, Artificial Intelligence
Yuenan Hou et al.
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019)
(2019)
Article
Computer Science, Artificial Intelligence
Mabel Gonzalez et al.
KNOWLEDGE AND INFORMATION SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Sangdi Lin et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2018)
Article
Computer Science, Artificial Intelligence
Mustafa Gokce Baydogan et al.
DATA MINING AND KNOWLEDGE DISCOVERY
(2016)
Article
Computer Science, Artificial Intelligence
Jason Lines et al.
DATA MINING AND KNOWLEDGE DISCOVERY
(2015)
Article
Computer Science, Artificial Intelligence
Jon Hills et al.
DATA MINING AND KNOWLEDGE DISCOVERY
(2014)
Article
Engineering, Biomedical
Jin Wang et al.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2013)
Article
Computer Science, Artificial Intelligence
Mustafa Gokce Baydogan et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2013)
Article
Computer Science, Information Systems
Houtao Deng et al.
INFORMATION SCIENCES
(2013)