Jianshu Chen

Jianshu Chen

Principal Scientist at Amazon

Full publication list (reverse chronological order)

Conference papers

  1. Rui Yang, Xiaoman Pan, Feng Luo, Shuang Qiu, Han Zhong, Dong Yu, Jianshu Chen, “Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment”, Proc. International Conference on Machine Learning (ICML), July 2024 [arXiv:2402.10207, February 2024].
  2. Xinran Zhao, Hongming Zhang, Xiaoman Pan, Wenlin Yao, Dong Yu, Tongshuang Wu, Jianshu Chen, “Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models”, arXiv preprint [arXiv:2402.17124], February, 2024.
  3. Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, Jianshu Chen, Kaiqiang Song, Sangwoo Cho, Yaser Yacob, Dong Yu, “MMC: Advancing Multimodel Chart Understanding with Large-Scale Instruction Tuning”, Proc. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Mexico City, Mexico, June, 2024 [arXiv:2311.10774, November, 2023].
  4. Xuansheng Wu, Wenlin Yao, Jianshu Chen, Xiaoman Pan, Xiaoyang Wang, Ninghao Liu, Dong Yu, “From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning”, Proc. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Mexico City, Mexico, June, 2024 [arXiv:2310.00492, October 2023].
  5. Jiaao Chen, Xiaoman Pan, Kaiqiang Song, Dian Yu, Dong Yu, Jianshu Chen, “Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models”, , arXiv preprint [arXiv:2308.00304], August 2023.
  6. Neeraj Varshney, Wenlin Yao, Hongming Zhang, Jianshu Chen, Dong Yu, “A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation”, arXiv preprint [arXiv:2307.03987], July 2023.
  7. Keming Lu, Xiaoman Pan, Kaiqiang Song, Hongming Zhang, Dong Yu, Jianshu Chen, “PIVOINE: Instruction Tuning for Open-world Information Extraction”, arXiv preprint [arXiv:2305.14898], May 2023.
  8. Zhenhailong Wang, Xiaoman Pan, Dian Yu, Dong Yu, Jianshu Chen, Heng Ji, “Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks”, Findings of Annual Meeting of the Association for Computational Linguistics (ACL), July 2023.
  9. Jianshu Chen, “Learning Language Representations with Logical Inductive Bias”, Proc. International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023.
  10. Xiaoman Pan, Wenlin Yao, Hongming Zhang, Dian Yu, Dong Yu, Jianshu Chen, “Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models”, Proc. International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023 (Spotlight).
  11. Yulai Zhao, Jianshu Chen, Simon S. Du, “Blessing of Class Diversity in Pre-training”, Proc. Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, April 2023.
  12. Yue Yang, Wenlin Yao, Hongming Zhang, Xiaoyang Wang, Dong Yu, Jianshu Chen, “Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination”, Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, UAE, December 2022.
  13. Xiang Yue, Xiaoman Pan, Wenlin Yao, Dian Yu, Dong Yu, Jianshu Chen, “C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References”, Proc. 60th Annual Meeting of the Association for Computational Linguistics (ACL), May 2022.
  14. Chao Zhao, Wenlin Yao, Dian Yu, Kaiqiang Song, Dong Yu, Jianshu Chen, “Learning-by-Narrating: Narrative Pre-training for Zero-Shot Dialogue Comprehension”, Proc. 60th Annual Meeting of the Association for Computational Linguistics (ACL), May 2022.
  15. Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Claire Cardie, “Improving Multiple-Choice Machine Reading Comprehension with Contextualized Knowledge from Scripts”, Proc. 60th Annual Meeting of the Association for Computational Linguistics (ACL), May 2022.
  16. Wenlin Yao, Xiaoman Pan, Lifeng Jin, Jianshu Chen, Dian Yu and Dong Yu, “Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories”, Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP), November 2021.
  17. Yiwu Zhong, Liwei Wang, Jianshu Chen, Dong Yu, and Yin Li, “Comprehensive Image Captioning via Scene Graph Decomposition”, Proc. European Conference on Computer Vision (ECCV), August 2020.
  18. Wenhu Chen, Jianshu Chen, Yu Su, Zhiyu Chen, and William Yang Wang, “Logical Natural Language Generation from Open-Domain Tables”, Proc. the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), Seattle, WA, July 2020.
  19. Hongyu Gong, Yelong Shen, Dian Yu, Jianshu Chen, and Dong Yu, “Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension”, Proc. the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), Seattle, WA, July 2020.
  20. Linfeng Song, Kun Xu, Yue Zhang, Jianshu Chen, and Dong Yu, “ZPR2: Joint Zero Pronoun Recovery and Resolution using Multi-Task Learning and BERT”, Proc. the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), Seattle, WA, July 2020.
  21. Anji Liu, Jianshu Chen, Mingze Yu, Yu Zhai, Xuewen Zhou, and Ji Liu, “Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search”, Proc. International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020. (Oral, 1.85%) [paper] [code]
  22. Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, William Yang Wang, “TabFact: A Large-scale Dataset for Table-based Fact Verification”, Proc. International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020. [paper] [code]
  23. Adithya M. Devraj and Jianshu Chen, “Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization”, Proc. Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019. [paper]
  24. Wenhu Chen, Jianshu Chen, Pengda Qin, Xifeng Yan and William Yang Wang, “Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention”, Proc. the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, July 2019. [paper] [code]
  25. Chih-Kuan Yeh, Jianshu Chen, Chengzhu Yu, Dong Yu, “Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching”, Proc. International Conference on Learning Representations (ICLR), New Orlean, LA, May 2019. [paper]
  26. Jiaao Chen, Jianshu Chen, Zhou Yu, “Incorporating Structured Commonsense Knowledge in Story Completion”, Proc. 33rd AAAI Conference on Artificial Intelligence (AAAI), Honolulu, HI, January 2019. [paper]
  27. Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song, “Coupled Variational Bayes via Optimization Embedding”, Proc. Advances in Neural Information Processing Systems (NeurIPS), Montreal, Canada, December 2018. [paper] [code]
  28. Yelong Shen*, Jianshu Chen*, Po-Sen Huang*, Yuqing Guo, Jianfeng Gao, “M-Walk: Learning to Walk in Graph with Monte Carlo Tree Search”, Proc. Advances in Neural Information Processing Systems (NeurIPS), Montreal, Canada, December 2018 (*Equal contribution). [paper] [code]
  29. Wenhu Chen, Jianshu Chen, Yu Su, Xin Wang, Dong Yu, Xifeng Yan, William Yang Wang, “XL-NBT: A Cross-lingual Neural Belief Tracking Framework”, Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP), Brussels, Belgium, November 2018. [paper] [code]
  30. Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song, “SBEED Learning: Convergent Control with Nonlinear Function Approximation”, Proc. International Conference on Machine Learning (ICML), Stockholm, Sweden, July 2018. [paper]
  31. Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li and Li Deng, “Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes”, Proc. Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, Dec. 2017.
  32. Yu Liu*, Jianshu Chen* and Li Deng*, “Unsupervised Sequence Classification using Sequential Output Statistics”, Proc. Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, Dec. 2017. (*Equal contribution)
  33. Simon Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou, “Stochastic Variance Reduction Methods for Policy Evaluation”, Proc. International Conference on Machine Learning (ICML), Sydney, Australia, August 2017.
  34. Zhe Gan, P. D. Singh, Ameet Joshi, Xiaodong He, Jianshu Chen, Jianfeng Gao, and Li Deng, “Character-Level Deep Conflation For Business Data Analytics”, Proc. IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), New Orlean, LA, March 2017.
  35. Omid Alipourfard, Hongqiang Harry Liu, Jianshu Chen, Shivaram Venkataraman, Minlan Yu, Ming Zhang, “CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics”, Proc. of USENIX NSDI. Boston, MA, March 2017.
  36. Ji He, Mari Ostendorf, Xiaodong He, Jianshu Chen, Jianfeng Gao, Lihong Li and Li Deng, “Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads”, Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, TX, November, 2016.
  37. Yue Zhao, Jianshu Chen and H. Vincent Poor, “Efficient Neural Network Architecture for Topology Identification in Smart Grid”, IEEE Global Conference on Signal and Informaiton Processing (GlobalSIP), Washington, DC, December 2016.
  38. Yue Zhao, Jianshu Chen and H. Vincent Poor, “Learning to Infer: A New Variational Inference Approach For Power Grid Topology Identification”, IEEE Workshop on Statistical Signal Processing (SSP), Palma de Mallorca, Spain, June 2016.
  39. Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf, “Deep Reinforcement Learning with an Action Space Defined by Natural Language”, Proc. the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016), Berlin, Germany, August, 2016.
  40. Jianshu Chen, Ji He, Xiaodong He, Lin Xiao, Jianfeng Gao, and Li Deng, “Interpreting the Prediction Process of a Deep Network Constructed from Supervised Topic Models”, Proc. IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP 2016), Shanghai, China, March, 2016.
  41. Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, Li Deng, “End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture”, Proc. Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, Dec. 2015. [paper] [code]
  42. Jianshu Chen, Yue Zhao, Andrea Goldsmith, H. Vincent Poor, “Line Outage Detection in Power Transmission Networks via Message Passing Algorithms”, Proc. 48th Asilomar Conference on Signals, Systems and Computers (Asilomar 2014), Pacific Grove, CA, Nov. 2014.
  43. Yue Zhao, Jianshu Chen, Andrea Goldsmith, H. Vincent Poor, “ Dynamic Joint Outage Identification and State Estimation in Power Systems”, Proc. 48th Asilomar Conference on Signals, Systems and Computers (Asilomar 2014), Pacific Grove, CA, Nov. 2014.
  44. Zaid J. Towfic, Jianshu Chen, and Ali H. Sayed, “Dictionary Learning over Large Distributed Models via Dual-ADMM Strategies”, IEEE Machine Learning for Signal Processing Workshop (MLSP 2014), Reims, France, September, 2014.
  45. Jianshu Chen, Zaid Towfic, and Ali H. Sayed. “Online Dictionary Learning over Distributed Models”, International Conference on Acoustic, Speech and Signal Processing (ICASSP), Florence, Italy, May, 2014.
  46. Li Deng, and Jianshu Chen. “Sequence Classification using the High-Level Features Extracted from Deep Neural Networks”, International Conference on Acoustic, Speech and Signal Processing (ICASSP), Florence, Italy, May, 2014.
  47. Jianshu Chen, and Ali H. Sayed. “On the probability distribution of distributed optimization strategies”, IEEE Global Conference on Signal and Informaiton Processing (GlobalSIP 2013), Austin, TX, pp.1–4 December 2013.
  48. Jianshu Chen, Yue Zhao, Andrea Goldsmith, and H. Vincent Poor, “Optimal joint detection and estimation in linear models”, IEEE Conference on Decision and Control (CDC 2013), Florence, Italy, December 2013.
  49. Jianshu Chen and Ali H. Sayed. “On the Benefits of Diffusion Cooperation for Distributed Optimization and Learning”, European Signal Processing Conference (EUSIPCO 2013), Marrakech, Morrocco, pp. 1–5, September 2013.
  50. Zaid J. Towfic, Jianshu Chen, and Ali H. Sayed. “Distributed Inference over Regression and Classification Models”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), Vancouver, Canada, pp. 5406–5410, May 2013.
  51. Sergio V. Macua, Jianshu Chen, Santiago Zazo, and Ali H. Sayed. “Cooperative Off-Policy Prediction of Markov Decision Processes in Adaptive Networks”, submitted to IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), Vancouver, Canada, pp. 4539–4543 May 2013.
  52. Ali H. Sayed, Sheng-Yuan Tu, and Jianshu Chen. “Online Learning and Adaptation over Networks: More Information is Not Necessarily Better”, Proc. Information Theory and Applications (ITA), San Diego, CA, pp. 1–8, Februrary 2013.
  53. Jianshu Chen, Ali H. Sayed. “On the Limit Behavior of Distributed Optimization Strategies,” Proc. 50th Annual Alloerton Conference on Communication, Control, and Computing, Monticello, IL, pp. 1535–1542, October 2012.
  54. Zaid J. Towfic, Jianshu Chen, and Ali H. Sayed. “On the Generalization Ability of Distributed Online Learners,” Proc. IEEE Machine Learning for Signal Processing Workshop (MLSP 2012), Santander, Spain, pp. 1–6, September 2012.
  55. Jianshu Chen, Ali H. Sayed. “Distributed Pareto-Optimal Solutions Via Diffusion Adaptation,” Proc. IEEE Statistical Signal Processing Workshop (SSP 2012), Ann Arbor, MI, pp. 648–651, August 2012.
  56. Xiaochuan Zhao, Jianshu Chen, and Ali H. Sayed. “Beam Coordination via Diffusion Adaptation over Array Networks,” Proc. IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2012), Cesme, Turkey, pp. 105–109, June 2012.
  57. Zaid J. Towfic, Jianshu Chen, and Ali H. Sayed. “Distributed Learning via Diffu- sion Adaptation with Application to Ensemble Learning,” Proc. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Bruges, Belgium, pp. 245–250, April 2012.
  58. Zaid J. Towfic, Jianshu Chen, and Ali H. Sayed. “Distributed Throughput Optimization over P2P Mesh Networks using Diffusion Adaptation,” Proc. IEEE International Conference on Communications (ICC 2012), Ottawa, Canada, pp. 648-652, June 2012.
  59. Jianshu Chen, Ali H. Sayed. “Performance of Diffusion Adaptation for Collaborative Optimization,” Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), Kyoto, Japan, pp. 3753–3756, March 2012.
  60. Jianshu Chen, Sheng-Yuan Tu and Ali H. Sayed. “Distributed optimization via diffusion adaptation,” Proc. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2011), San Juan, Puerto Rico, pp. 281– 284, December 2011.
  61. Zaid J. Towfic, Jianshu Chen, and Ali H. Sayed. “Collaborative Learning Of Mix- ture Models Using Diffusion Adaptation,” Proc. IEEE Machine Learning for Signal Processing Workshop (MLSP 2011), Beijing, China, pp. 1–6, September 2011.
  62. Jianshu Chen, Ali H. Sayed. “Bio-inspired cooperative optimization with application to bacteria motility,” Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), Prague, Czech, pp. 5788–5791, May 2011.
  63. Jianshu Chen, Xiaochuan Zhao, Ali H. Sayed. “Bacterial motility via diffusion adaptation,” Proc. 44th Asilomar Conference on Signals, Systems and Computers (Asilomar 2010), Pacific Grove, CA, pp. 1930–1934, Nov. 2010.

Journal papers

  1. Yue Zhao, Jianshu Chen, and H. Vincent Poor, “A Learning-to-Infer Method for Real-Time Power Grid Multi-Line Outage Identification”, IEEE Transactions on Smart Grid, Vol. 11, No. 1, January 2020, pp. 555-564.
  2. Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, and Claire Cardie, “DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension”, Transactions of the Association for Computational Linguistics (TACL), Vol. 7, March 2019, pp. 217-231.
  3. Zaid J. Towfic, Jianshu Chen, and Ali H. Sayed. “Excess-Risk of Distributed Stochastic Learners”, IEEE Transactions on Information Theory, Vol. 62, No. 10, October 2016, pp. 5753–5785.
  4. Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, and Rabab Ward, “Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval”, IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol.24, No. 4, April 2016, pp. 694-707. (2018 IEEE Signal Processing Society Best Paper Award)
  5. Jianshu Chen and Ali H. Sayed. “On the Learning Behavior of Adaptive Networks — Part I: Transient Analysis”, IEEE Transactions on Information Theory, Vol. 61, No. 6, June 2015, pp. 3487–3517.
  6. Jianshu Chen and Ali H. Sayed. “On the Learning Behavior of Adaptive Networks — Part II: Performance Analysis”, IEEE Transactions on Information Theory, Vol. 61, No. 6, June 2015, pp. 3518–3548.
  7. Jianshu Chen, Zaid J. Towfic, and Ali H. Sayed. “Dictionary Learning over Distributed Models”, IEEE Transactions on Signal Processing, Vol.63, No. 4, February 2015, pp. 1001–1016.[Software code] [Acknowledgement]
  8. Jun Wang, Jianshu Chen, You Lu, Mario Gerla, and Danijela Cabric. “Robust Power Control under Location and Channel Uncertainty in Cognitive Radio Networks”, IEEE Wireless Communication Letters, Vol.4, No. 2, April 2015, pp. 113–116.
  9. Sergio V. Macua, Jianshu Chen, Santiago Zazo, and Ali H. Sayed. “Distributed Policy Evaluation Under Multiple Behavior Strategies”, IEEE Transactions on Automatic Control, Vol. 60, No. 5, May 2015, pp. 1260 – 1274.
  10. Yue Zhao, Jianshu Chen, Andrea Goldsmith, H. Vincent Poor, “Identification of Out- ages in Power Systems with Uncertain States and Optimal Sensor Locations”, IEEE Journal of Selected Topics in Signal Processing, Vol.8, No.6, December, 2014, pp. 1140–1153.
  11. Jun Wang, Jianshu Chen, and Danijela Cabric. “Stansfield Localization Algorithm: Theoretical Analysis and Distributed Implementation”, IEEE Wireless Communications Letters, Vol. 2, No. 3, June 2013, pp. 327–330.
  12. Jianshu Chen and Ali H. Sayed.“Distributed Pareto Optimization via Diffusion Strategies”, IEEE Journal of Selected Topics in Signal Processing, Vol. 7, No. 2, April 2013, pp. 205-220.
  13. Ali H. Sayed, Sheng-Yuan Tu, Jianshu Chen, Xiaochuan Zhao, and Zaid J. Towfic, “Diffusion Strategies for Adaptation and Learning over Networks”, IEEE Signal Processing Magazine, Vol. 30, May 2013, pp. 155-171.
  14. Jun Wang, Jianshu Chen, Danijela Cabric,“Cramer-Rao Bounds for Joint RSS/DoA- Based Primary-User Localization in Cognitive Radio Networks”, IEEE Transactions on Wireless Communications, Vol. 12, No. 3, March 2013, pp.1363-1375.
  15. Zaid J. Towfic, Jianshu Chen, and Ali H. Sayed. “On distributed online classification in the midst of concept drifts”, Neurocomputing, vol. 112, pp.139–152, July 2013.
  16. Jianshu Chen and Ali H. Sayed. “Diffusion Adaptation Strategies for Distributed Optimization and Learning over Networks”, IEEE Transactions on Signal Processing, Vol. 60, No. 8, August 2012, pp.4289–4305.

Workshop papers

  1. Shiyang Li, Jianshu Chen, and Dian Yu. “Teaching Pretrained Models with Commonsense Reasoning: A Preliminary KB-Based Approach”, NeurIPS-KR2ML Workshop (Knowledge Representation and Reasoning Meets Machine Learning), Vancouver, Canada, December, 2019. [paper]
  2. Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, William Yang Wang. “TabFact: A Large-scale Dataset for Table-based Fact Verification”, NeurIPS-KR2ML Workshop (Knowledge Representation and Reasoning Meets Machine Learning), Vancouver, Canada, December, 2019. [paper] [code] [dataset]
  3. Sichen Zhong, Yue Zhao, and Jianshu Chen. “Learning to Recover Sparse Signals”, NeurIPS Deep Inverse Workshop (Solving inverse problems with deep networks: New architectures, theoretical foundations, and applications), Vancouver, Canada, December, 2019.
  4. Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, and Mari Ostendorf. “Deep Reinforcement Learning with an Action Space Defined by Natural Language”, ICLR Workshop, San Juan, Puerto Rico, May, 2016.
  5. Jianshu Chen, and Li Deng. “A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property”, ICLR Workshop, Banff, Canada, April, 2014.