Hui Jiang

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Recent Publication (from 2012)

Books:

  1. H. Jiang, "Machine Learning Fundamentals", Cambridge University Press, 2021. (bibtex)

Referred Journal Papers:

  1. S. Zhang, C. Liu, H. Jiang, S. Wei, L. Dai, Y. Hu, “Non-Recurrent Neural Structure for Long-Term Dependency,” IEEE/ACM Trans. on Audio, Speech and Language Processing, pp. 871-884, Vol. 25, No. 4, April 2017.
  2. S. Zhang, H. Jiang, L. Dai, “Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks,” Journal of Machine Learning Research (JMLR), 17(37):1−33, 2016.
  3. S. Xue, H. Jiang, L. Dai, Q. Liu, “Speaker Adaptation of Hybrid NN/HMM Model for Speech Recognition Based on Singular Value Decomposition,” Journal of Signal Processing Systems, pp. 175-185, Vol. 82, No. 2, February 2016.
  4. P. Zhou, H. Jiang, L. Dai, Y. Hu and Q. Liu, “State-Clustering based Multiple Deep Neural Networks Modelling Approach for Speech Recognition,” IEEE/ACM Trans. on Audio, Speech and Language Processing, pp.631-642, Vol. 23, No. 4, April 2015.
  5. H. Jiang, Z. Pan, P. Hu, “Discriminative learning of generative models: large margin multinomial mixture models for document classification,” Pattern Analysis and Applications, Volume 18, Issue 3, pp 535-551, August 2015.
  6. S. Xue, O. Abdel-Hamid, H. Jiang, L. Dai, Q. Liu, “Fast Adaptation of Deep Neural Network based on Discriminant Codes for Speech Recognition,” IEEE/ACM Trans. on Audio, Speech and Language Processing, pp.1713-1725, Vol. 22, No. 12, December 2014.
  7. O. Abdel-Hamid, A. Mohamed, H. Jiang, L. Deng, G. Penn, D. Yu, “Convolutional Neural Networks for Speech Recognition,” IEEE/ACM Trans. on Audio, Speech and Language Processing, pp.1533-1545, Vol. 22, No. 10, October 2014. ( 2016 IEEE SPS Best Paper Award)
  8. E. P. Wilkinson, O. Abdel-Hamid, J. J. Galvin, H. Jiang and Q.-J. Fu, ``Voice Conversion in Cochlear Implantation,“ The Laryngoscope, Vol. 123, Issue S13, pp.S29-S43, January 2013.
  9. P. Hu, S. Bull and H. Jiang, ”Gene network modular-based classi fication of microarray samples,“ BMC Bioinformatics, 13(Suppl 10):S17, 2012.

On-going work in arXiv.org

Referred Conference Papers:

  1. C. Wang, J. Gaspers, Q. Do and H. Jiang, “Exploring Cross-Lingual Transfer Learning with Unsupervised Machine Translation.” Findings of the Association for Computational Linguistics (ACL), August 2021.
  2. C. Wang and H. Jiang, “Explicit Utilization of General Knowledge in Machine Reading Comprehension”, Proc. of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), July 2019. (paper)
  3. C. Wang and H. Jiang, “The Lower The Simpler: Simplifying Hierarchical Recurrent Models”, Proc. of North American Chapter of the Association for Computational Linguistics (NAACL), June 2019. (paper)
  4. K. Jiang, D. Wu and H. Jiang, “FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase,” Proc. of North American Chapter of the Association for Computational Linguistics (NAACL), June 2019. (paper, dataset)
  5. K. Joseph and H. Jiang, “Content based News Recommendation via Shortest Entity Distance over Knowledge Graphs,” Proc. of WWW2019 Workshop On Knowledge Graph Technology And Applications, May 2019.
  6. S. Watcharawittayakul, M. Xu and H. Jiang, “Dual Fixed-Size Ordinally Forgetting Encoding (FOFE) for Competitive Neural Language Models,” Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), October, 2018. (paper)
  7. H. Pan and H. Jiang, “Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and Estimation,” Proc. of the 9th Asian Conference on Machine Learning (ACML 2017), November 2017. (paper)
  8. J. Sanu, M. Xu, H. Jiang and Q. Liu, “Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding,” Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017), September, 2017. (paper)
  9. Q. Liu, H. Jiang, A. Evdokimov, Z. Ling, X. Zhu, S. Wei and Y. Hu, “Cause-Effect Knowledge Acquisition and Neural Association Model for Solving A Set of Winograd Schema Problems,” Proc. of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), August 2017. (paper)
  10. M. Xu, H. Jiang and S. Watcharawittayakul, “A Local Detection Approach for Named Entity Recognition and Mention Detection,” Proc. of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), July 2017. (paper)
  11. Q. Chen, X. Zhu, Z. Ling, S. Wei, H. Jiang and D. Inkpen, “Enhanced LSTM for Natural Language Inference,” Proc. of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), July 2017. (paper)
  12. H. Pan and H. Jiang, “A Fast Method for Saliency Detection by Back-propagating a Convolutional Neural Network and Clamping Its Partial Outputs,” Proc. of International Joint Conference on Neural Networks (IJCNN), May 2017. (paper)
  13. S. Zhang, H. Jiang, S. Xiong, S. Wei, L. Dai, “Compact Feedforward Sequential Memory Networks for Large Vocabulary Continuous Speech Recognition,” Proc. of Interspeech, September 2016. (paper)
  14. Q. Liu, H. Jiang, Z. Ling, S. Wei, and Y. Hu, “Probabilistic Reasoning via Deep Learning: Neural Association Models,” Proc. of IJCAI 2016 Workshop on Deep Learning for Artificial Intelligence (DLAI), July 2016. (project)
  15. Q. Chen, X. Zhu, Z. Ling, S. Wei and H. Jiang, “A Neural Network for Document Summarization,” Proc. of 25th International Joint Conference on Artificial Intelligence (IJCAI), July 2016.
  16. Y. Peng and H. Jiang, “Leverage Financial News to Predict Stock Price Movements UsingWord Embeddings and Deep Neural Networks,” Proc. of North American Chapter of the Association for Computational Linguistics (NAACL), June 2016. (paper)
  17. Q. Liu, W. Guo, Z. Ling, H. Jiang and Y. Hu, “Intra-Topic Variability Normalization based on Linear Projection for Topic Classification,” Proc. of North American Chapter of the Association for Computational Linguistics (NAACL), June 2016. (paper)
  18. S. Zhang, H. Jiang, S. Wei, L. Dai, “Rectified Linear Neural Networks with Tied-Scalar Regularization for LVCSR,” Proc. of Interspeech, September 2015. (paper)
  19. S. Zhang, H. Jiang, M. Xu, J. Hou, L. Dai, “The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models,” Proc. of the 53th Annual Meeting of the Association for Computational Linguistics (ACL 2015), July 2015. (paper, project)
  20. Z. Chen, W. Lin, Q. Chen, X. Chen, S. Wei, X. Zhu, H. Jiang, “Revisiting Word Embedding for Contrasting Meaning,” Proc. of the 53th Annual Meeting of the Association for Computational Linguistics (ACL 2015), July 2015. (paper, project)
  21. Q. Liu, H. Jiang, S. Wei, Z.-H. Ling, Y. Hu, “Learning Semantic Word Embeddings based on Ordinal Knowledge Constraints,” Proc. of the 53th Annual Meeting of the Association for Computational Linguistics (ACL 2015), July 2015. (paper, project)
  22. S. Zhang, H. Jiang, L. Dai, “The New HOPE Way to Learn Neural Networks”, Proc. of Deep Learning Workshop at ICML 2015, July 2015. (paper, project)
  23. H. Pan, H. Jiang, “Annealed Gradient Descent for Deep Learning,” Proc. of the 31th Conference on Uncertainty in Artificial Intelligence (UAI 2015), July 2015. (paper, project)
  24. S. Xue, H. Jiang, L. Dai, Q. Liu, ``Unsupervised Speaker Adaptation of Deep Neural Network based on the combination of speaker codes and singular value decomposition for Speech Recognition,'' Proc. of of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'15), Brisbane, Australia, April 2015.
  25. S. Xue, H. Jiang, L. Dai, “Speaker Adaptation of Hybrid NN/HMM Model for Speech Recognition Based on Singular Value Decomposition,” Proc. of International Symposium on Chinese Spoken Language Processing (ISCSLP'2014), Sep 2014.
  26. C. Kong, S. Xue, J. Gao, W. Guo, L. Dai and H. Jiang, “Speaker Adaptive Bottleneck Features Extraction for LVCSR Based on Discriminative Learning of Speaker Codes,” Proc. of International Symposium on Chinese Spoken Language Processing (ISCSLP'2014), Sep 2014.
  27. P. Zhou, L. Dai, H. Jiang, “Sequence Training of Multiple Deep Neural Networks for Better Performance and Faster Training Speed,” Proc. of of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'14), Florence, Italy, May 2014. (pdf)
  28. S. Zhang, Y. Bao, P. Zhou, H. Jiang, L. Dai, “Improving deep neural networks for LVCSR using dropout and shrinking structure,” Proc. of of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'14), Florence, Italy, May 2014. (pdf)
  29. S. Xue, O. Abdel-Hamid, H. Jiang, L. Dai, “Direct Adaptation of Hybrid DNN/HMM Model for Fast Speaker Adaptation,” Proc. of of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'14), Florence, Italy, May 2014. (pdf)
  30. O. Abdel-Hamid, L. Deng, D. Yu and H. Jiang, “Deep Segmental Neural Network for Automatic Speech Recognition,” Proc. of Interspeech, Lyon, France, 2013. (pdf)
  31. O. Abdel-Hamid and H. Jiang, “Rapid and E ffective Speaker Adaptation of Convolutional Neural Network Based Models for Speech Recognition,” Proc. of Interspeech, Lyon, France, 2013. (pdf)
  32. Y. Bao, H. Jiang, L. Dai, C. Liu, “Incoherent Training of Deep Neural Networks to De-correlate Bottleneck Features for Speech Recognition,” Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'13), Vancouver, Canada, 2013. (pdf)
  33. O. Abdel-Hamid and H. Jiang, “Fast Speaker Adaptation Of Hybrid NN/HMM Model for Speech Recognition based on Discriminative Learning of Speaker Code,” Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'13), Canada, 2013. (pdf)
  34. P. Zhou, C. Liu, Q. Liu, L. Dai and H. Jiang, “A cluster-based multiple deep neural networks method for large vocabulary continuous speech recognition,” Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'13), Vancouver, Canada, 2013. (pdf)
  35. J. Pan, C. Liu, Z. Wang, Y. Hu and H. Jiang, “Investigations of Deep Neural Networks for Large Vocabulary Continuous Speech Recognition: Why DNN Surpasses GMMs in Acoustic Modelling”, Proc. of International Symposium on Chinese Spoken Language Processing (ISCSLP'2012), Hong Kong, 2012. (pdf)
  36. Y. Bao, H. Jiang, C. Liu, Y. Hu and L. Dai, “Investigation on dimensionality reduction of concatenated features with deep neural network for LVCSR systems,” Proc. of 11th IEEE International Conference on Signal Processing (ICSP'2012), Beijing, China, 2012.
  37. O. Abdel-Hamid, A. Mohamed, H. Jiang, G. Penn, “Applying Convolutional Neural Networks Concepts to Hybrid NN-HMM Model for Speech Recognition,” Proc. of IEEE International Conference on Acoustic, Speech, Signal Processing (ICASSP'2012), Japan, March 2012. (pdf)
publication/recentpublication.txt · Last modified: 2021/11/08 21:13 by hj