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Journal articles on the topic 'Domain adaption'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Li, Linhao, Zhiqiang Zhou, Bo Wang, Lingjuan Miao, Zhe An, and Xiaowu Xiao. "Domain Adaptive Ship Detection in Optical Remote Sensing Images." Remote Sensing 13, no. 16 (2021): 3168. http://dx.doi.org/10.3390/rs13163168.

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With the successful application of the convolutional neural network (CNN), significant progress has been made by CNN-based ship detection methods. However, they often face considerable difficulties when applied to a new domain where the imaging condition changes significantly. Although training with the two domains together can solve this problem to some extent, the large domain shift will lead to sub-optimal feature representations, and thus weaken the generalization ability on both domains. In this paper, a domain adaptive ship detection method is proposed to better detect ships between diff

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Guo, Rui, Yong Zhou, Jiaqi Zhao, Rui Yao, Bing Liu, and Xunhui Zhang. "Unsupervised spatial-awareness attention-based and multi-scale domain adaption network for point cloud classification." International Journal of Wavelets, Multiresolution and Information Processing 19, no. 04 (2021): 2150007. http://dx.doi.org/10.1142/s0219691321500077.

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Domain adaption is a special transfer learning method, whose source domain and target domain generally have different data distribution, but need to complete the same task. There have been many significant types of research on domain adaptation in 2D images, but in 3D data processing, domain adaptation is still in its infancy. Therefore, we design a novel domain adaptive network to complete the unsupervised point cloud classification task. Specifically, we propose a multi-scale transform module to improve the feature extractor. Besides, a spatial-awareness attention module combined with channe

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Deng, Wan-Yu, Yu-Tao Qu, and Qian Zhang. "Domain Adaption Based on ELM Autoencoder." Mathematical Problems in Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/1239164.

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We propose a new ELM Autoencoder (ELM-AE) based domain adaption algorithm which describes the subspaces of source and target domain by ELM-AE and then carries out subspace alignment to project different domains into a common new space. By leveraging nonlinear approximation ability and efficient one-pass learning ability of ELM-AE, the proposed domain adaption algorithm can efficiently seek a better cross-domain feature representation than linear feature representation approaches such as PCA to improve domain adaption performance. The widely experimental results on Office/Caltech-256 datasets s

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Lu, Nannan, Fei Chu, Haoran Qi, and Shuang Xia. "A new domain adaption algorithm based on weights adaption from the source domain." IEEJ Transactions on Electrical and Electronic Engineering 13, no. 12 (2018): 1769–76. http://dx.doi.org/10.1002/tee.22739.

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M., Dhanashree, and R. N. Phursule. "Online Cost Sensitive Domain Knowledge Adaption." International Journal of Computer Applications 123, no. 18 (2015): 16–18. http://dx.doi.org/10.5120/ijca2015905639.

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6

Foxall, Gordon R., and Isabelle Szmigin. "Adaption-Innovation and Domain-Specific Innovativeness." Psychological Reports 84, no. 3 (1999): 1029–30. http://dx.doi.org/10.2466/pr0.1999.84.3.1029.

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Reported is a study of the relationship between a global measure of adaption-innovation (Kirton's Adaption-Innovation Inventory) and a domain-specific measure of consumers' innovativeness (Goldsmith and Hofacker's Domain Specific Innovativeness Scale). For a convenience sample of 26 adult consumers there was, as expected, no significant correlation between scores on the two scales.

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Ma, Chenhui, Dexuan Sha, and Xiaodong Mu. "Unsupervised Adversarial Domain Adaptation with Error-Correcting Boundaries and Feature Adaption Metric for Remote-Sensing Scene Classification." Remote Sensing 13, no. 7 (2021): 1270. http://dx.doi.org/10.3390/rs13071270.

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Unsupervised domain adaptation (UDA) based on adversarial learning for remote-sensing scene classification has become a research hotspot because of the need to alleviating the lack of annotated training data. Existing methods train classifiers according to their ability to distinguish features from source or target domains. However, they suffer from the following two limitations: (1) the classifier is trained on source samples and forms a source-domain-specific boundary, which ignores features from the target domain and (2) semantically meaningful features are merely built from the adversary o

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Dong, Le, Ning Feng, Pinjie Quan, Gaipeng Kong, Xiuyuan Chen, and Qianni Zhang. "Optimal kernel choice for domain adaption learning." Engineering Applications of Artificial Intelligence 51 (May 2016): 163–70. http://dx.doi.org/10.1016/j.engappai.2016.01.022.

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Wang, Yan, Serguei Pakhomov, James O. Ryan, and Genevieve B. Melton. "Domain adaption of parsing for operative notes." Journal of Biomedical Informatics 54 (April 2015): 1–9. http://dx.doi.org/10.1016/j.jbi.2015.01.016.

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10

Li, Rumeng, Xun Wang, and Hong Yu. "MetaMT, a Meta Learning Method Leveraging Multiple Domain Data for Low Resource Machine Translation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8245–52. http://dx.doi.org/10.1609/aaai.v34i05.6339.

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Neural machine translation (NMT) models have achieved state-of-the-art translation quality with a large quantity of parallel corpora available. However, their performance suffers significantly when it comes to domain-specific translations, in which training data are usually scarce. In this paper, we present a novel NMT model with a new word embedding transition technique for fast domain adaption. We propose to split parameters in the model into two groups: model parameters and meta parameters. The former are used to model the translation while the latter are used to adjust the representational

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Ozdagli, Ali, and Xenofon Koutsoukos. "Domain Adaptation for Structural Health Monitoring." Annual Conference of the PHM Society 12, no. 1 (2020): 9. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1184.

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In recent years, machine learning (ML) algorithms gained a lot of interest within structural health monitoring (SHM) community. Many of those approaches assume the training and test data come from similar distributions. However, real-world applications, where an ML model is trained on numerical simulation data and tested on experimental data, are deemed to fail in detecting the damage, as both domain data are collected under different conditions and they don’t share the same underlying features. This paper proposes the domain adaptation approach as a solution to particular SHM problems where t

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12

Chen, Jifa, Guojun Zhai, Gang Chen, Bo Fang, Ping Zhou, and Nan Yu. "Unsupervised Domain Adaption for High-Resolution Coastal Land Cover Mapping with Category-Space Constrained Adversarial Network." Remote Sensing 13, no. 8 (2021): 1493. http://dx.doi.org/10.3390/rs13081493.

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Coastal land cover mapping (CLCM) across image domains presents a fundamental and challenging segmentation task. Although adversaries-based domain adaptation methods have been proposed to address this issue, they always implement distribution alignment via a global discriminator while ignoring the data structure. Additionally, the low inter-class variances and intricate spatial details of coastal objects may entail poor presentation. Therefore, this paper proposes a category-space constrained adversarial method to execute category-level adaptive CLCM. Focusing on the underlying category inform

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Chu, Yongjie, Yong Zhao, Touqeer Ahmad, and Lindu Zhao. "Low-Resolution Face Recognition with Single Sample per Person via Domain Adaptation." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 05 (2019): 1956005. http://dx.doi.org/10.1142/s0218001419560056.

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Numerous low-resolution (LR) face images are captured by a growing number of surveillance cameras nowadays. In some particular applications, such as suspect identification, it is required to recognize an LR face image captured by the surveillance camera using only one high-resolution (HR) profile face image on the ID card. This leads to LR face recognition with single sample per person (SSPP), which is more challenging than conventional LR face recognition or SSPP face recognition. To address this tough problem, we propose a Boosted Coupled Marginal Fisher Analysis (CMFA) approach, which unite

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Twaites, Joshua, Richard Everson, Joss Langford, and Melvyn Hillsdon. "Achieving Accelerometer Wrist and Orientation Invariance in Physical Activity Classification via Domain Adaption." Journal for the Measurement of Physical Behaviour 2, no. 4 (2019): 256–62. http://dx.doi.org/10.1123/jmpb.2018-0058.

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Purpose: Physical activity classifiers are typically trained on data obtained from sensors at a set orientation. Changes in this orientation (such as being on a different wrist) result in performance degradation. This work investigates a method to obtain sensor location and orientation invariance for classification of wrist-mounted accelerometry via a technique known as domain adaption. Methods: Data was gathered from 16 participants who wore accelerometers on both wrists. Physical activity classification models were created using data from each wrist and then used to predict activities when u

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Meng, Juan, Guyu Hu, Dong Li, Yanyan Zhang, and Zhisong Pan. "Generalization Bounds Derived IPM-Based Regularization for Domain Adaptation." Computational Intelligence and Neuroscience 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/7046563.

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Domain adaptation has received much attention as a major form of transfer learning. One issue that should be considered in domain adaptation is the gap between source domain and target domain. In order to improve the generalization ability of domain adaption methods, we proposed a framework for domain adaptation combining source and target data, with a new regularizer which takes generalization bounds into account. This regularization term considers integral probability metric (IPM) as the distance between the source domain and the target domain and thus can bound up the testing error of an ex

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16

Sangaiah, Arun Kumar, Jin Wang, Xiaohu Tian, Jin Liu, and Xin Zhang. "A novel domain adaption approach for neural machine translation." International Journal of Computational Science and Engineering 22, no. 4 (2020): 445. http://dx.doi.org/10.1504/ijcse.2020.10031607.

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Liu, Jin, Xin Zhang, Xiaohu Tian, Jin Wang, and Arun Kumar Sangaiah. "A novel domain adaption approach for neural machine translation." International Journal of Computational Science and Engineering 22, no. 4 (2020): 445. http://dx.doi.org/10.1504/ijcse.2020.109404.

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18

Zhou, Shuren, Peng Luo, Deepak Kumar Jain, Xiangyuan Lan, and Yudong Zhang. "Double-Domain Imaging and Adaption for Person Re-Identification." IEEE Access 7 (2019): 103336–45. http://dx.doi.org/10.1109/access.2019.2930865.

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19

Deng, Wan-Yu, Amaury Lendasse, Yew-Soon Ong, Ivor Wai-Hung Tsang, Lin Chen, and Qing-Hua Zheng. "Domain Adaption via Feature Selection on Explicit Feature Map." IEEE Transactions on Neural Networks and Learning Systems 30, no. 4 (2019): 1180–90. http://dx.doi.org/10.1109/tnnls.2018.2863240.

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20

Wang, Jing, Xueqing Li, and Jixiang Du. "Label Space Embedding of Manifold Alignment for Domain Adaption." Neural Processing Letters 49, no. 1 (2018): 375–91. http://dx.doi.org/10.1007/s11063-018-9822-8.

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21

Song, Yan, Yibin Li, Lei Jia, and Meikang Qiu. "Retraining Strategy-Based Domain Adaption Network for Intelligent Fault Diagnosis." IEEE Transactions on Industrial Informatics 16, no. 9 (2020): 6163–71. http://dx.doi.org/10.1109/tii.2019.2950667.

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22

Liu, Qihe, Xue Li, Mao Ye, Shuzhi Sam Ge, and Xiaosong Du. "Drift Compensation for Electronic Nose by Semi-Supervised Domain Adaption." IEEE Sensors Journal 14, no. 3 (2014): 657–65. http://dx.doi.org/10.1109/jsen.2013.2285919.

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23

Zhang, Xinyu, Sen Li, Xiao-Yuan Jing, Fei Ma, and Chen Zhu. "Unsupervised domain adaption for image-to-video person re-identification." Multimedia Tools and Applications 79, no. 45-46 (2020): 33793–810. http://dx.doi.org/10.1007/s11042-019-08550-9.

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24

Li, Xudong, Mao Ye, Min Fu, Pei Xu, and Tao Li. "Domain adaption of vehicle detector based on convolutional neural networks." International Journal of Control, Automation and Systems 13, no. 4 (2015): 1020–31. http://dx.doi.org/10.1007/s12555-014-0119-z.

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25

Wang, Lujia, Hyunsoo Yoon, Andrea Hawkins-Daarud, et al. "NIMG-30. REPRODUCIBLE RADIOMIC MAPPING OF TUMOR CELL DENSITY BY MACHINE LEARNING AND DOMAIN ADAPTATION." Neuro-Oncology 21, Supplement_6 (2019): vi167. http://dx.doi.org/10.1093/neuonc/noz175.700.

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Abstract BACKGROUND An important challenge in radiomics research is reproducibility. Images are collected on different image scanners and protocols, which introduces significant variability even for the same type of image across institutions. In the present proof-of-concept study, we address the reproducibility issue by using domain adaptation – an algorithm that transforms the radiomic features of each new patient to align with the distribution of features formed by the patient samples in a training set. METHOD Our dataset included 18 patients in training with a total of 82 biopsy sample. The

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Shi, Qian, Mengxi Liu, Xiaoping Liu, et al. "Domain Adaption for Fine-Grained Urban Village Extraction From Satellite Images." IEEE Geoscience and Remote Sensing Letters 17, no. 8 (2020): 1430–34. http://dx.doi.org/10.1109/lgrs.2019.2947473.

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27

Xuesong, Wang, Zhao Jijuan, Cheng Yuhu, and Yu Qiang. "Joint Feature Representation and Classifier Learning Based Unsupervised Domain Adaption ELM." Chinese Journal of Electronics 30, no. 1 (2021): 109–18. http://dx.doi.org/10.1049/cje.2020.11.008.

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28

ZENG, LI, and YI ZHAO. "CHARACTERIZATION OF STATIC BIFURCATIONS FOR MAPS IN THE FREQUENCY DOMAIN." International Journal of Bifurcation and Chaos 17, no. 03 (2007): 975–83. http://dx.doi.org/10.1142/s0218127407017690.

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In this paper n-dimensional discrete-time systems with static bifurcations are considered from the viewpoint of control theory. This paper presents an adaption of available formulas for bifurcation analysis in two-dimensional continuous-time systems to the case of smooth maps using a frequency domain approach. The analyzed bifurcations are the building blocks to understand other more complex singularities and to propose certain methods for controlling the bifurcation behavior in nonlinear maps in the future.

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Shao, Jiajie, Zhiwen Huang, and Jianmin Zhu. "Transfer Learning Method Based on Adversarial Domain Adaption for Bearing Fault Diagnosis." IEEE Access 8 (2020): 119421–30. http://dx.doi.org/10.1109/access.2020.3005243.

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Wang, Yingdong, Jiatong Liu, Qunsheng Ruan, Shuocheng Wang, and Chen Wang. "Cross-subject EEG emotion classification based on few-label adversarial domain adaption." Expert Systems with Applications 185 (December 2021): 115581. http://dx.doi.org/10.1016/j.eswa.2021.115581.

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Wu, J., W. Yao, J. Zhang, and Y. Li. "3D SEMANTIC LABELING OF ALS DATA BASED ON DOMAIN ADAPTION BY TRANSFERRING AND FUSING RANDOM FOREST MODELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1883–87. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1883-2018.

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Labeling 3D point cloud data with traditional supervised learning methods requires considerable labelled samples, the collection of which is cost and time expensive. This work focuses on adopting domain adaption concept to transfer existing trained random forest classifiers (based on source domain) to new data scenes (target domain), which aims at reducing the dependence of accurate 3D semantic labeling in point clouds on training samples from the new data scene. Firstly, two random forest classifiers were firstly trained with existing samples previously collected for other data. They were dif

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Hagmeijer, R. "Grid Adaption Based on Modified Anisotropic Diffusion Equations Formulated in the Parametric Domain." Journal of Computational Physics 115, no. 1 (1994): 169–83. http://dx.doi.org/10.1006/jcph.1994.1185.

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He, Yuwei, Xiaoming Jin, Guiguang Ding, et al. "Heterogeneous Transfer Learning with Weighted Instance-Correspondence Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4099–106. http://dx.doi.org/10.1609/aaai.v34i04.5829.

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Instance-correspondence (IC) data are potent resources for heterogeneous transfer learning (HeTL) due to the capability of bridging the source and the target domains at the instance-level. To this end, people tend to use machine-generated IC data, because manually establishing IC data is expensive and primitive. However, existing IC data machine generators are not perfect and always produce the data that are not of high quality, thus hampering the performance of domain adaption. In this paper, instead of improving the IC data generator, which might not be an optimal way, we accept the fact tha

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Wang, Qi Sheng, and Xue Ling Wang. "Convergence Analysis and the Nested Refinement for the Trapezoid Finite Element." Advanced Materials Research 317-319 (August 2011): 1921–25. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1921.

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In this paper, a class of the new method of nested refinement based on self-adaption grid is discussed. The level trapezoid grid nested refinement on the plan domain and some related properties are investigated, and the convergence results are obtained for the second order self-adjoint elliptic problem on the trapezoid finite element.

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Liu, Xiuping, Hongchen Tan, Xin Tong, Junjie Cao, and Jun Zhou. "Feature preserving GAN and multi-scale feature enhancement for domain adaption person Re-identification." Neurocomputing 364 (October 2019): 108–18. http://dx.doi.org/10.1016/j.neucom.2019.07.063.

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Cao, Xincheng, Binqiang Chen, and Nianyin Zeng. "A deep domain adaption model with multi-task networks for planetary gearbox fault diagnosis." Neurocomputing 409 (October 2020): 173–90. http://dx.doi.org/10.1016/j.neucom.2020.05.064.

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Fu, Hengcheng, Wuneng Zhou, Xiaofeng Wang, and Huanlong Zhang. "Fast and robust visual tracking with hard balanced focal loss and guided domain adaption." Image and Vision Computing 100 (August 2020): 103929. http://dx.doi.org/10.1016/j.imavis.2020.103929.

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Yao, Qunwang, Yi Qin, Xin Wang, and Quan Qian. "Multiscale domain adaption models and their application in fault transfer diagnosis of planetary gearboxes." Engineering Applications of Artificial Intelligence 104 (September 2021): 104383. http://dx.doi.org/10.1016/j.engappai.2021.104383.

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Huang, Chao, Quan Yi Huang, Shao Bo Zhong, and Jian Guo Chen. "Case Reuse Based on Fuzzy Reasoning – Adaption for Emergency Management." Applied Mechanics and Materials 333-335 (July 2013): 1324–27. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1324.

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Emergency management is such a domain where experiential knowledge could be easily collected, and is quite suitable for the application of case based reasoning. However, in practice there are two problems limiting the effectiveness of CBR, the he incomplete information and changing situations. This paper proposed an approach based on fuzzy sets and text mining to solve those two problems, which contains four steps: a) represent the attributes with fuzzy sets, b) extract solution texts with text classification, c) establish connections of attributes and solutions with association rules, and d)

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Zou, Zhengxia, Tianyang Shi, Wenyuan Li, Zhou Zhang, and Zhenwei Shi. "Do Game Data Generalize Well for Remote Sensing Image Segmentation?" Remote Sensing 12, no. 2 (2020): 275. http://dx.doi.org/10.3390/rs12020275.

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Despite the recent progress in deep learning and remote sensing image interpretation, the adaption of a deep learning model between different sources of remote sensing data still remains a challenge. This paper investigates an interesting question: do synthetic data generalize well for remote sensing image applications? To answer this question, we take the building segmentation as an example by training a deep learning model on the city map of a well-known video game “Grand Theft Auto V” and then adapting the model to real-world remote sensing images. We propose a generative adversarial traini

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Rutkevich, Elena D. "The Impact of Immigrant Religions on the Nature of Religious Pluralism in the USA and Western Europe." Sociological Journal 25, no. 2 (2019): 8–32. http://dx.doi.org/10.19181/socjour.2019.25.2.6384.

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Some of the most significant consequences of transnational immigration is growing religious diversity and finding a way to manage it. This article considers the concept of pluralism, the differences in religious pluralism between America and Western Europe occurring due to immigration, as well as the roles and possibilities of immigrant religions in the process of adapting to the host society. The history of immigration, models of immigrant incorporation and adaption, patterns of religious pluralism and types of secularism strongly vary in the aforementioned regions. Religion in America is a p

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Jia, Runda, Shulei Zhang, and Fengqi You. "Transfer learning for end-product quality prediction of batch processes using domain-adaption joint-Y PLS." Computers & Chemical Engineering 140 (September 2020): 106943. http://dx.doi.org/10.1016/j.compchemeng.2020.106943.

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Noh, Kyoung Jun, Jiho Choi, Jin Seong Hong, and Kang Ryoung Park. "Finger-Vein Recognition Using Heterogeneous Databases by Domain Adaption Based on a Cycle-Consistent Adversarial Network." Sensors 21, no. 2 (2021): 524. http://dx.doi.org/10.3390/s21020524.

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The conventional finger-vein recognition system is trained using one type of database and entails the serious problem of performance degradation when tested with different types of databases. This degradation is caused by changes in image characteristics due to variable factors such as position of camera, finger, and lighting. Therefore, each database has varying characteristics despite the same finger-vein modality. However, previous researches on improving the recognition accuracy of unobserved or heterogeneous databases is lacking. To overcome this problem, we propose a method to improve th

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Luo, Junhai, Man Wu, Zhiyan Wang, Yanping Chen, and Yang Yang. "Progressive low-rank subspace alignment based on semi-supervised joint domain adaption for personalized emotion recognition." Neurocomputing 456 (October 2021): 312–26. http://dx.doi.org/10.1016/j.neucom.2021.05.064.

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Liu, Fangyuan, Hao Zhou, Weimin Huang, Yingwei Tian, and Biyang Wen. "Cross-Domain Submesoscale Eddy Detection Neural Network for HF Radar." Remote Sensing 13, no. 13 (2021): 2441. http://dx.doi.org/10.3390/rs13132441.

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With the rapid development of deep learning, the neural network becomes an efficient approach for eddy detection. However, previous work employs a traditional neural network with a focus on improving the detecting accuracy only using limited data under a single scenario. Meanwhile, the experience of detecting eddies from one experiment is not directly inherited from the detection model for other experiments. Therefore, a cross-domain submesoscale eddy detection neural network (CDEDNet) based on the high-frequency radar (HFR) data of the Nansan and Xuwen region is proposed in this paper. Firstl

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Curtis, Robert G., Elisabeth M. Goodwin, and Mark Konyn. "The Automatic Detection of Real-Life Ship Encounters." Journal of Navigation 40, no. 3 (1987): 355–65. http://dx.doi.org/10.1017/s0373463300000618.

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There are now radar installations which track ships automatically, and these radars can provide the researcher with statistics on collision-avoidance manoeuvres. The extraction, by hand, is laborious and time consuming. This paper describes a mathematical model which automatically detects collision-avoidance manoeuvres. The model operates on an adaption of the Range to Domain Over Range Rate (RDRR) principle, which enables ships to be identified for analysis shortly before they are expected to initiate a collision-avoidance manoeuvre. The model has been computerized and results presented.

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Zou, Qin, and Min Wang. "Fast Demodulation Algorithm for Rate Compatible Modulation by Log-Map Principle." Advanced Materials Research 945-949 (June 2014): 2310–14. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2310.

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Rate compatible modulation (RCM) is a promising technology to smooth rate adaption of physical layer in wireless network, and do not request the accurate channel estimation. Since RCM demodulation adopt probability convolution operator in belief propagation algorithm, it greatly bring big computing complexity. This paper proposes fast demodulation algorithm for RCM by using log-map principle. First, we propose a new demodulation algorithm by a novel approach to convert arithmetic domain to logarithm domain. Then, we design a parallel manner that to find multivariable computation in Log-MAP alg

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Tao, Yang, Chunyan Li, Zhifang Liang, Haocheng Yang, and Juan Xu. "Wasserstein Distance Learns Domain Invariant Feature Representations for Drift Compensation of E-Nose." Sensors 19, no. 17 (2019): 3703. http://dx.doi.org/10.3390/s19173703.

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Electronic nose (E-nose), a kind of instrument which combines with the gas sensor and the corresponding pattern recognition algorithm, is used to detect the type and concentration of gases. However, the sensor drift will occur in realistic application scenario of E-nose, which makes a variation of data distribution in feature space and causes a decrease in prediction accuracy. Therefore, studies on the drift compensation algorithms are receiving increasing attention in the field of the E-nose. In this paper, a novel method, namely Wasserstein Distance Learned Feature Representations (WDLFR), i

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Grayver, Alexander V., and Alexey V. Kuvshinov. "Exploring equivalence domain in nonlinear inverse problems using Covariance Matrix Adaption Evolution Strategy (CMAES) and random sampling." Geophysical Journal International 205, no. 2 (2016): 971–87. http://dx.doi.org/10.1093/gji/ggw063.

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Tseng, Shao-Yen, Brian Baucom, and Panayiotis Georgiou. "Unsupervised online multitask learning of behavioral sentence embeddings." PeerJ Computer Science 5 (June 10, 2019): e200. http://dx.doi.org/10.7717/peerj-cs.200.

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Appropriate embedding transformation of sentences can aid in downstream tasks such as NLP and emotion and behavior analysis. Such efforts evolved from word vectors which were trained in an unsupervised manner using large-scale corpora. Recent research, however, has shown that sentence embeddings trained using in-domain data or supervised techniques, often through multitask learning, perform better than unsupervised ones. Representations have also been shown to be applicable in multiple tasks, especially when training incorporates multiple information sources. In this work we aspire to combine

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