Multitask Learning

Cited by (1,024)

Items 101–200 of 1,024

1. Multitask Prompted Training Enables Zero-Shot Task Generalization
   Sanh, Victor, Webson, Albert, Raffel, Colin, Bach, Stephen H., Sutawika, Lintang, Alyafeai, Zaid, Chaffin, Antoine, Stiegler, Arnaud, Scao, Teven Le, Raja, Arun et al. · 2021 · arXiv
2. Small-footprint keyword spotting using deep neural networks
   Guoguo Chen, Carolina Parada, Georg Heigold · 2014 · IEEE International Conference on Acoustics Speech and Signal Processing
3. Predictive Business Process Monitoring with LSTM Neural Networks
   Niek Tax, Ilya Verenich, Marcello La Rosa, Marlon Dumas · 2017 · Lecture notes in computer science
4. Multitask Compressive Sensing
   Shihao Ji, David B. Dunson, Lawrence Carin · 2008 · IEEE Transactions on Signal Processing
5. Model-based Reinforcement Learning: A Survey
   Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker · 2023 · Foundations and Trends® in Machine Learning
6. Multiple Instance Detection Network with Online Instance Classifier Refinement
   Peng Tang, Xinggang Wang, Xiang Bai, Wenyu Liu · 2017 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
7. A Survey on Metric Learning for Feature Vectors and Structured Data
   Aurélien Bellet, Amaury Habrard, Marc Sebban · 2013 · arXiv (Cornell University)
8. Never-ending learning
   Tom M. Mitchell, William W. Cohen, Estevam Rafael Hruschka Junior, Partha Pratim Talukdar, Bishan Yang, Justin Betteridge, A. Carlson, Bhavana Bharat Dalvi, Matt Gardner, Bryan Kisiel et al. · 2018 · Communications of the ACM
9. UNIFIEDQA: Crossing Format Boundaries with a Single QA System
   Khashabi, Daniel, Min, Sewon, Khot, Tushar, Sabharwal, Ashish, Tafjord, Oyvind, Clark, Peter, Hajishirzi, Hannaneh · 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020
10. Ordinal Regression Methods: Survey and Experimental Study
    Pedro Antonio Gutiérrez, María Pérez‐Ortiz, Javier Sánchez‐Monedero, Francisco Fernández‐Navarro, César Hervás‐Martínez · 2015 · IEEE Transactions on Knowledge and Data Engineering
11. Multi-task learning for classification with dirichlet process priors
    Ya Xue, Xuejun Liao, Lawrence Carin, Balaji Krishnapuram, Peter Bartlett · 2007
12. Bregman Divergence-Based Regularization for Transfer Subspace Learning
    Si Si, Dacheng Tao, Bo Geng · 2009 · IEEE Transactions on Knowledge and Data Engineering
13. Style Transfer in Text: Exploration and Evaluation
    Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, Rui Yan · 2018 · Proceedings of the AAAI Conference on Artificial Intelligence
14. A review on neural networks with random weights
    Weipeng Cao, Xizhao Wang, Zhong Ming, Jinzhu Gao · 2017 · Neurocomputing
15. Untitled work
    2017 · neural information processing systems
16. Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
    Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald J. Tesauro · 2018 · arXiv (Cornell University)
17. Machine Learning Paradigms for Speech Recognition: An Overview
    Li Deng, Xiao Li · 2013 · IEEE Transactions on Audio Speech and Language Processing
18. Methodologies for Cross-Domain Data Fusion: An Overview
    Zheng, Yu · 2015 · IEEE Transactions on Big Data
19. A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets
    Bayoudh, Khaled, Knani, Raja, Hamdaoui, Fayçal, Mtibaa, Abdellatif · 2022 · The Visual Computer
20. Deep Multitask Learning for Railway Track Inspection
    Xavier Gibert, Vishal M. Patel, Rama Chellappa · 2016 · IEEE Transactions on Intelligent Transportation Systems
21. Multifactorial Evolutionary Algorithm With Online Transfer Parameter Estimation: MFEA-II
    Kavitesh Kumar Bali, Yew-Soon Ong, Abhishek Gupta, Puay Siew Tan · 2019 · IEEE Transactions on Evolutionary Computation
22. Exploiting Task Relatedness for Multiple Task Learning
    Shai Ben-David, Reba Schuller · 2003 · Lecture notes in computer science
23. Learning Deep Representation for Face Alignment with Auxiliary Attributes
    Zhanpeng Zhang, Ping Luo, Chen Change Loy, Xiaoou Tang · 2015 · IEEE Transactions on Pattern Analysis and Machine Intelligence
24. PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
    Tang, Peng, Wang, Xinggang, Bai, Song, Shen, Wei, Bai, Xiang, Liu, Wenyu, Yuille, Alan · 2020 · IEEE Transactions on Pattern Analysis and Machine Intelligence
25. Coupled semi-supervised learning for information extraction
    J. Andrew Carlson, Justin Betteridge, Richard C. Wang, Estevam Rafael Hruschka Junior, Tom M. Mitchell · 2010 · ACM International Conference on Web Search and Data Mining (WSDM)
26. CoNet
    Guangneng Hu, Yu Zhang, Qiang Yang · 2018 · ACM International Conference on Information and Knowledge Management (CIKM)
27. 12-in-1: Multi-Task Vision and Language Representation Learning
    Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, Stefan Lee · 2020 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
28. Deep learning based brain tumor segmentation: a survey
    Zhihua Liu, Lei Tong, Long Chen, Zheheng Jiang, Feixiang Zhou, Qianni Zhang, Xiangrong Zhang, Yaochu Jin, Huiyu Zhou · 2022 · Complex & Intelligent Systems
29. Precision health data: Requirements, challenges and existing techniques for data security and privacy
    Chandra Thapa, Seyit Ahmet Camtepe · 2020 · Computers in Biology and Medicine
30. Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification
    Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, Rogério Feris · 2017 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
31. Transfer Learning in Collaborative Filtering for Sparsity Reduction
    Weike Pan, Evan Wei Xiang, Nathan Liu, Qiang Yang · 2010 · Proceedings of the AAAI Conference on Artificial Intelligence
32. One-shot Learning with Memory-Augmented Neural Networks
    Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap · 2016 · arXiv (Cornell University)
33. Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks
    Yu–Gang Jiang, Zuxuan Wu, Jun Wang, Xiangyang Xue, Shih‐Fu Chang · 2017 · IEEE Transactions on Pattern Analysis and Machine Intelligence
34. Learning active facial patches for expression analysis
    Lin Zhong, Qingshan Liu, Peng Yang, Bo Liu, Junzhou Huang, D. Metaxas · 2012 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
35. Deep learning with convolutional neural network in radiology
    Yasaka, Koichiro, Akai, Hiroyuki, Kunimatsu, Akira, Kiryu, Shigeru, Abe, Osamu · 2018 · Japanese Journal of Radiology
36. Semi-Supervised Sequence Modeling with Cross-View Training
    Kevin Clark, Minh-Thang Luong, Christopher D. Manning, Quoc Viet Le · 2018 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
37. Self-supervised speech representation learning: A review
    Mohamed, Abdelrahman, Lee, Hung-yi, Borgholt, Lasse, Havtorn, Jakob D., Edin, Joakim, Igel, Christian, Kirchhoff, Katrin, Li, Shang-Wen, Livescu, Karen, Maaløe, Lars et al. · 2022 · IEEE Journal of Selected Topics in Signal Processing
38. Survey of Personalization Techniques for Federated Learning
    Viraj Kulkarni, Milind Kulkarni, Aniruddha Pant · 2020 · World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4)
39. Machine learning of molecular electronic properties in chemical compound space
    Grégoire Montavon, Matthias Rupp, Vivekanand V. Gobre, Álvaro Vázquez‐Mayagoitia, Katja Hansen, Alexandre Tkatchenko, Klaus‐Robert Müller, O. Anatole von Lilienfeld · 2013 · MPG.PuRe (Max Planck Society)
40. Visual Classification With Multitask Joint Sparse Representation
    Xiao–Tong Yuan, Xiaobai Liu, Shuicheng Yan · 2012 · IEEE Transactions on Image Processing
41. Multi-target regression via input space expansion: treating targets as inputs
    Eleftherios Spyromitros-Xioufis, Grigorios Tsoumakas, William Groves, Ioannis P. Vlahavas · 2016 · Machine Learning
42. Meta-Learning
    Joaquin Vanschoren · 2019 · The Springer series on challenges in machine learning
43. A Survey on Transfer Learning
    Santisudha Panigrahi, Anuja Nanda, Tripti Swarnkar · 2020 · Smart innovation, systems and technologies
44. Recurrent neural networks for time series classification
    Michael Hüsken, Peter Stagge · 2003 · Neurocomputing
45. Learning to learn with the informative vector machine
    Neil David Lawrence, John C. Platt · 2004 · International Conference on Machine Learning
46. Pattern-Affinitive Propagation Across Depth, Surface Normal and Semantic Segmentation
    Zhenyu Zhang, Zhen Cui, Chunyan Xu, Yan Yan, Nicu Sebe, Jian Jun Yang · 2019 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
47. Multi-task Self-Supervised Learning for Human Activity Detection
    Saeed, Aaqib, Ozcelebi, Tanir, Lukkien, Johan · 2019 · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
48. Transfer learning for collaborative filtering via a rating-matrix generative model
    Bin Li, Qiang Yang, Xiangyang Xue · 2009 · International Conference on Machine Learning
49. Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data
    Taewook Kim, Ha Young Kim · 2019 · PLoS ONE
50. A selective review of group selection in high-dimensional models
    Jian Huang, Patrick John Breheny, Shuangge Ma · 2012
51. Knowledge transfer via multiple model local structure mapping
    Jing Gao, Wei Fan, Jing Jiang, Jiawei Han · 2008 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
52. Co-clustering based classification for out-of-domain documents
    Wenyuan Dai, Gui-Rong Xue, Qiang Yang, Yong Hai Yu · 2007 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
53. Handbook of Mathematical Methods in Imaging
    Matti J. Lassas, Mikko Salo, Gunther A. Uhlmann · 2010 · Springer eBooks
54. Machine translation of cortical activity to text with an encoder–decoder framework
    Makin, Joseph G., Moses, David A., Chang, Edward F. · 2020 · Nature Neuroscience
55. A convex formulation for learning task relationships in multi-task learning
    Yu Zhang, Dit‐Yan Yeung · 2010
56. Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces
    Lotte, Fabien · 2015 · Proceedings of the IEEE
57. Robust visual domain adaptation with low-rank reconstruction
    I‐Hong Jhuo, Dong Liu, D. T. Lee, Shih-Fu Chang · 2012 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
58. A Survey of Transfer Learning for Convolutional Neural Networks
    Ricardo Ribani, Maurício Marengoni · 2019 · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
59. A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action
    Jason H. Yang, Sarah N. Wright, Meagan Hamblin, Douglas McCloskey, Miguel A. Alcantar, Lars C. Schrübbers, Allison J. Lopatkin, Sangeeta Satish, Amir Nili, Bernhard Ørn Palsson et al. · 2019 · Cell
60. An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation
    Daniel Michelsanti, Zheng‐Hua Tan, Shi-Xiong Zhang, Yong Xu, Meng Yu, Dong Yu, Jesper Jensen · 2021 · IEEE/ACM Transactions on Audio Speech and Language Processing
61. Integrating low-rank and group-sparse structures for robust multi-task learning
    Jianhui Chen, Jiayu Zhou, Jieping Ye · 2011 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
62. Learning with Whom to Share in Multi-task Feature Learning
    Zhuoliang Kang, Kristen Lorraine Grauman, Fei Sha · 2011
63. Learning modular neural network policies for multi-task and multi-robot transfer
    Coline Devin, Abhishek Gupta, Trevor Darrell, Pieter Abbeel, Sergey Levine · 2017 · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings
64. Machine learning for metabolic engineering: A review
    Christopher E. Lawson, Jose Manuel Martí, Tijana Radivojević, Sai Vamshi R. Jonnalagadda, Reinhard Gentz, Nathan J. Hillson, Sean Peisert, Joonhoon Kim, Blake Alexander Simmons, Christopher J. Petzold et al. · 2020 · Metabolic Engineering
65. Ask the GRU
    Trapit Bansal, David Belanger, Andrew McCallum · 2016 · ACM Conference on Recommender Systems (RecSys)
66. Recommending what video to watch next
    Zhe Zhao, Lichan Hong, Wei Li, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed H. · 2019 · ACM Conference on Recommender Systems (RecSys)
67. How Well Do We Know the Neutron-Matter Equation of State at the Densities Inside Neutron Stars? A Bayesian Approach with Correlated Uncertainties
    C. Drischler, R. J. Furnstahl, J. A. Melendez, Daniel R. Phillips · 2020 · Physical Review Letters
68. Multi-Task CNN Model for Attribute Prediction
    Abrar H. Abdulnabi, Gang Wang, Jiwen Lu, Kui Jia · 2015 · IEEE Transactions on Multimedia
69. Robust multi-task feature learning
    Pinghua Gong, Jieping Ye, Changshui Zhang · 2012 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
70. A Selective Review of Group Selection in High-Dimensional Models
    Jian Huang, Patrick John Breheny, Shuangge Ma · 2012 · Statistical Science
71. On label dependence and loss minimization in multi-label classification
    Krzysztof Dembczyński, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier · 2012 · Machine Learning
72. Review and prospect of data-driven techniques for load forecasting in integrated energy systems
    Jizhong Zhu, Hanjiang Dong, Weiye Zheng, Shenglin Li, Yanting Huang, Lei Xi · 2022 · Applied Energy
73. A Robust Deep Model for Improved Classification of AD/MCI Patients
    Feng Li, Loc Tran, Kim‐Han Thung, Shuiwang Ji, Dinggang G. Shen, Li Jiang · 2015 · IEEE Journal of Biomedical and Health Informatics
74. Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis
    Stergios Christodoulidis, Marios Anthimopoulos, Lukas Ebner, Andreas Christe, Stavroula Georgia Mougiakakou · 2016 · IEEE Journal of Biomedical and Health Informatics
75. Remarks on multi-output Gaussian process regression
    Haitao Liu, Jianfei Cai, Yew-Soon Ong · 2018 · Knowledge-Based Systems
76. Learning to Learn: Model Regression Networks for Easy Small Sample Learning
    Yu-Xiong Wang, Martial Hebert · 2016 · Lecture notes in computer science
77. A survey on data‐efficient algorithms in big data era
    Adadi, Amina · 2021 · Journal of Big Data
78. Learning by Playing - Solving Sparse Reward Tasks from Scratch
    Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Van de Wiele, Tom, Volodymyr Mnih, Nicolas Heess, Jost Tobias Springenberg · 2018 · arXiv (Cornell University)
79. Multi-class pattern classification using neural networks
    Guobin Ou, Yi Lu Murphey · 2006 · Pattern Recognition
80. UniT: Multimodal Multitask Learning with a Unified Transformer
    Hu, Ronghang, Singh, Amanpreet · 2021 · 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
81. Deep learning methods in transportation domain: a review
    Hoàng Nguyên, Minh Kieu, Tao Wen, Chen Cai · 2018 · IET Intelligent Transport Systems
82. Inductive biases for deep learning of higher-level cognition
    Goyal, Anirudh, Bengio, Yoshua · 2022 · Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
83. Speech Model Pre-Training for End-to-End Spoken Language Understanding
    Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, Yoshua Bengio · 2019 · Interspeech (USB)
84. Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach
    Hu Han, Anil K. Jain, Wang Sheng Fang, Shiguang Shan, Xilin Chen · 2017 · IEEE Transactions on Pattern Analysis and Machine Intelligence
85. Blockchain-enabled Federated Learning: A Survey
    Qu, Youyang, Uddin, Md Palash, Gan, Chenquan, Xiang, Yong, Gao, Longxiang, Yearwood, John · 2023 · ACM Computing Surveys
86. Multilingual acoustic models using distributed deep neural networks
    Georg Heigold, Vincent Vanhoucke, Andrew Senior, P. Nguyen, M. Ranzato, Matthieu Devin, J. Dean · 2013 · IEEE International Conference on Acoustics Speech and Signal Processing
87. Learning Algorithms for the Classification Restricted Boltzmann Machine
    Hugo Larochelle, Université De Sherbrooke, Michael Mandel, Razvan Pascanu, Yoshua Bengio, Daniel Lee · 2012
88. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network
    R.I. Zubatyuk, Justin S. Smith, Jerzy Leszczyński, Olexandr Isayev · 2019 · Science Advances
89. Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty
    Brady Allred, Brandon T. Bestelmeyer, Chad S. Boyd, Christopher F. Brown, Kirk W. Davies, Michael C. Duniway, Lisa M. Ellsworth, Tyler A. Erickson, Samuel D. Fuhlendorf, Timothy V. Griffiths et al. · 2021 · Methods in Ecology and Evolution
90. Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning
    Max Ferguson, Ronay Ak, Yung-Tsun Tina Lee, Kincho H. Law · 2018 · Smart and Sustainable Manufacturing Systems
91. Tree ensembles for predicting structured outputs
    Dragi Kocev, Celine Vens, Jan Struyf, Sašo Džeroski · 2012 · Pattern Recognition
92. MtNet: A Multi-Task Neural Network for Dynamic Malware Classification
    Wenyi Huang, Jack W. Stokes · 2016 · Lecture notes in computer science
93. Survey on Multi-Output Learning
    Donna Xu, Yaxin Shi, Ivor Wai-Hung Tsang, Yew-Soon Ong, Chen Gong, Xiaobo Shen · 2019 · IEEE Transactions on Neural Networks and Learning Systems
94. Learning Task Grouping and Overlap in Multi-task Learning
    Abhishek Kumar, Hal Daumé · 2012 · arXiv (Cornell University)
95. Cross-Task Generalization via Natural Language Crowdsourcing Instructions
    Mishra, Swaroop, Khashabi, Daniel, Baral, Chitta, Hajishirzi, Hannaneh · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
96. Transfer Learning
    Qiang Yang, Yu Zhang, Wenyuan Dai, Sinno Jialin Pan · 2020 · Cambridge University Press eBooks
97. MultiMAE: Multi-modal Multi-task Masked Autoencoders
    Bachmann, Roman, Mizrahi, David, Atanov, Andrei, Zamir, Amir · 2022 · Lecture notes in computer science
98. Ensemble Pruning Via Semi-definite Programming
    Yi Zhang, Samuel Burer, W. Nick Street · 2006
99. Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
    Sanmit Narvekar, Bei Peng, Matteo Leonetti, Jivko Sinapov, Matthew Edmund Taylor, Peter Stone · 2020 · arXiv (Cornell University)
100. A Comparative Review of Recent Kinect-Based Action Recognition Algorithms
     Lei Wang, Du Q. Huynh, Piotr Koniusz · 2019 · IEEE Transactions on Image Processing