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نتیجه جستجو - یادگیری چند وظیفه ای

تعداد مقالات یافته شده: 4
ردیف عنوان نوع
1 Emotional editing constraint conversation content generation based on reinforcement learning
ویرایش احساسی تولید محتوای مکالمه محدود بر اساس یادگیری تقویتی-2020
In recent years, the generation of conversation content based on deep neural networks has attracted many re- searchers. However, traditional neural language models tend to generate general replies, lacking logical and emotional factors. This paper proposes a conversation content generation model that combines reinforcement learning with emotional editing constraints to generate more meaningful and customizable emotional replies. The model divides the replies into three clauses based on pre-generated keywords and uses the emotional editor to further optimize the final reply. The model combines multi-task learning with multiple indicator rewards to comprehensively optimize the quality of replies. Experiments shows that our model can not only improve the fluency of the replies, but also significantly enhance the logical relevance and emotional relevance of the replies.
Keywords: Emotional conversation generation | Affective computing | Emotional editing | Reinforcement learning | Multitask learning
مقاله انگلیسی
2 Research on image steganography analysis based on deep learning
تحقیق در مورد تجزیه و تحلیل استگانوگرافی تصویر بر اساس یادگیری عمیق-2019
Although steganalysis has developed rapidly in recent years, it still faces many difficulties and challenges. Based on the theory of in-depth learning method and image-based general steganalysis, this paper makes a deep study of the hot and difficult problem of steganalysis feature expression, and tries to establish a new steganalysis paradigm from the idea of feature learning. The main contributions of this paper are as follows: 1. An innovative steganalysis paradigm based on in-depth learning is proposed. Based on the representative deep learning method CNN, the model is designed and adjusted according to the characteristics of steganalysis, which makes the proposed model more effective in capturing the statistical characteristics such as neighborhood correlation. 2. A steganalysis feature learning method based on global information constraints is proposed. Based on the previous research of steganalysis method based on CNN, this work focuses on the importance of global information in steganalysis feature expression. 3. A feature learning method for low embedding rate steganalysis is proposed. 4. A general steganalysis method for multi-class steganography is proposed. The ultimate goal of general steganalysis is to construct steganalysis detectors without distinguishing specific types of steganalysis algorithms
Keywords: Steganalysis | Steganography | Feature learning | Deep learning | Convolutional neural network | Transfer learning | Multitask learning
مقاله انگلیسی
3 Multi-task least squares twin support vector machine for classification
حداقل مربعات جزئی چند وظیفه ای ماشین بردار پشتیبانی برای طبقه بندی-2019
With the bloom of machine learning, pattern recognition plays an important role in many aspects. How- ever, traditional pattern recognition mainly focuses on single task learning (STL), and the multi-task learning (MTL) has largely been ignored. Compared to STL, MTL can improve the performance of learn- ing methods through the shared information among all tasks. Inspired by the recently proposed di- rected multi-task twin support vector machine (DMTSVM) and the least squares twin support vector ma- chine (LSTWSVM), we put forward a novel multi-task least squares twin support vector machine (MTLS- TWSVM). Instead of two dual quadratic programming problems (QPPs) solved in DMTSVM, our algorithm only needs to deal with two smaller linear equations. This leads to simple solutions, and the calculation can be effectively accelerated. Thus, our proposed model can be applied to the large scale datasets. In addition, it can deal with linear inseparable samples by using kernel trick. Experiments on three popular multi-task datasets show the effectiveness of our proposed methods. Finally, we apply it to two popular image datasets, and the experimental results also demonstrate the validity of our proposed algorithm.
Keywords: Pattern recognition | Multi-task learning | Relation learning | Least square twin support vector machine
مقاله انگلیسی
4 A deep learning based multitask model for network-wide traffic speed prediction
یک مدل چند کاره مبتنی بر یادگیری عمیق برای پیش بینی سرعت ترافیک شبکه-2019
This paper proposes a deep learning based multitask learning (MTL) model to predict network-wide traf- fic speed, and introduces two methods to improve the prediction performance. The nonlinear Granger causality analysis is used to detect the spatiotemporal causal relationship among various links so as to select the most informative features for the MTL model. Bayesian optimization is employed to tune the hyperparameters of the MTL model with limited computational costs. Numerical experiments are carried out with taxis’ GPS data in an urban road network of Changsha, China, and some conclusions are drawn as follows. The deep learning based MTL model outperforms four deep learning based single task learn- ing (STL) models (i.e., Gated Recurrent Units network, Long Short-term Memory network, Convolutional Gated Recurrent Units network and Temporal Convolutional Network) and three other classic models (i.e., Support Vector Machine, k -Nearest Neighbors and Evolving Fuzzy Neural Network). The nonlinear Granger causality test provides a reliable guide to select the informative features from network-wide links for the MTL model. Compared with two other optimization approaches (i.e., grid search and random search), Bayesian optimization yields a better tuning performance for the MTL model in the prediction accuracy under the budgeted computation cost. In summary, the deep learning based MTL model with nonlinear Granger causality analysis and Bayesian optimization promises the accurate and efficient traffic speed prediction for a large-scale network.
Keywords: Short-term traffic speed prediction | Deep learning | Multitask learning | Nonlinear Granger causality | Bayesian optimization
مقاله انگلیسی
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