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Visual Sentiment Prediction with Attribute Augmentation and Multi-attention Mechanism
Springer Science and Business Media LLC - Tập 51 - Trang 2403-2416 - 2020
Zhuanghui Wu, Min Meng, Jigang Wu
Recently, many methods that exploit attention mechanism to discover the relevant local regions via visual attributes, have demonstrated promising performance in visual sentiment prediction. In these methods, accurate detection of visual attributes is of vital importance to identify the sentiment relevant regions, which is crucial for successful assessment of visual sentiment. However, existing wor... hiện toàn bộ
Supervised Training Using an Unsupervised Approach to Active Learning
Springer Science and Business Media LLC - - 2002
A. P. Engelbrecht, R. Brits
Active learning algorithms allow neural networks to dynamically take part in the selection of the most informative training patterns. This paper introduces a new approach to active learning, which combines an unsupervised clustering of training data with a pattern selection approach based on sensitivity analysis. Training data is clustered into groups of similar patterns based on Euclidean distanc... hiện toàn bộ
Tuning Parameter Selection Based on Blocked $$3\times 2$$ Cross-Validation for High-Dimensional Linear Regression Model
Springer Science and Business Media LLC - Tập 51 - Trang 1007-1029 - 2019
Xingli Yang, Yu Wang, Ruibo Wang, Mengmeng Chen, Jihong Li
In high-dimensional linear regression, selecting an appropriate tuning parameter is essential for the penalized linear models. From the perspective of the expected prediction error of the model, cross-validation methods are commonly used to select the tuning parameter in machine learning. In this paper, blocked $$3\times 2$$ cross-validation ($$3\times 2$$ BCV) is proposed as the tuning parameter ... hiện toàn bộ
Applied Human Action Recognition Network Based on SNSP Features
Springer Science and Business Media LLC - - 2022
M. Shujah Islam, Khush Bakhat, Rahmatullah Khan, Nuzhat Naqvi, Muhammad Islam, Zhongfu Ye
Application of Hyperspectral Image Classification Based on Overlap Pooling
Springer Science and Business Media LLC - Tập 49 - Trang 1335-1354 - 2018
Hongmin Gao, Shuo Lin, Chenming Li, Yao Yang
Convolutional neural networks (CNN) are increasingly being used in hyperspectral image (HSI) classification. However, most pooling methods are non-overlap pooling and ignore the influence of neighboring pixels on image characteristics, thereby limiting network classification accuracy. This work presents a deep CNN that is based on overlap pooling; in this model, non-overlap pooling is replaced wit... hiện toàn bộ
Factor Augmented Artificial Neural Network Model
Springer Science and Business Media LLC - Tập 45 - Trang 507-521 - 2016
Ali Babikir, Henry Mwambi
This paper brings together two important developments in forecasting literature; the artificial neural networks and factor models. The paper introduces the factor augmented artificial neural network (FAANN) hybrid model in order to produce a more accurate forecasting. Theoretical and empirical findings have indicated that integration of various models can be an effective way of improving on their ... hiện toàn bộ
Data-Driven Deep Learning for The Multi-Hump Solitons and Parameters Discovery in NLS Equations with Generalized $${\mathcal{PT}\mathcal{}}$$ -Scarf-II Potentials
Springer Science and Business Media LLC - Tập 55 - Trang 2687-2705 - 2022
Ming Zhong, Jian-Guo Zhang, Zijian Zhou, Shou-Fu Tian, Zhenya Yan
In this paper, we investigate the data-driven forward and inverse problems of both the focusing and defocusing nonlinear Schrödinger equations (NLSEs) with generalized parity-time ( $${\mathcal{PT}\mathcal{}}$$ )-Scarf-II potential via the physics-informed neural networks (PINNs) deep learning. The NLSE with four different initial conditions and periodic boundary condition are analyzed via the PIN... hiện toàn bộ
A Transfer Learning Algorithm Based on Support Vector Machine
Springer Science and Business Media LLC - - 2022
Weifei Wu, Shidian Chen, LiYing Bao
In many scenarios of classification, test and training data must come from same feature space and have same distribution. However, this assumption may not be satisfactory in many practical applications. In recent years, the emergence of transfer learning has provided a new technology to solve this problem. Aiming at problems that the existing transfer learning algorithms only utilize the data in s... hiện toàn bộ
Sơ Đồ Mã Hóa Tần Số Tham Số Cao Cho Các Đối Tượng Âm Thanh Không Gian Sử Dụng Mạng Nơ-ron Tích Hợp Rải Rác Dịch bởi AI
Springer Science and Business Media LLC - Tập 54 - Trang 817-833 - 2021
Yulin Wu, Ruimin Hu, Xiaochen Wang, Chenhao Hu, Shanfa Ke
Các hệ thống âm thanh dựa trên đối tượng đã trở nên phổ biến trong những năm gần đây vì chúng cung cấp sự linh hoạt cho nhiều kịch bản thính giác, chẳng hạn như trò chơi thực tế ảo, rạp hát tương tác và giao tiếp âm thanh không gian. Để tiết kiệm băng thông, nhiều đối tượng âm thanh được nén thành tín hiệu trộn đơn âm và các tham số thông tin bên. Tuy nhiên, độ phân giải tần số của các tham số thô... hiện toàn bộ
#Âm thanh dựa trên đối tượng; mã hóa đối tượng âm thanh không gian; mạng nơ-ron tích hợp rải rác; tần số tham số cao.
A Dynamic Adaptive and Resource-Allocated Selection Method Based on TOPSIS and VIKOR in Federated Learning
Springer Science and Business Media LLC - - 2024
Lin Li, Wei Shi, Shuyu Chen, Jun Liu, Jiangping Huang, Pengcheng Liu
Federated learning (FL) is a decentralized and privacy-preserving machine learning technique that protects data privacy by learning models locally and not sharing datasets. However, due to limited computing resources on devices and highly heterogeneous data in practical situations, the training efficiency and resource utilization of federated learning is low. In order to resolve these challenges, ... hiện toàn bộ
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