BU LISP : Publications

2022

2021

  1. P. Colon-Hernandez, Y. Xin, H. Lieberman, C. Havasi, C. Breazeal, and P. Chin (2021). RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations. Findings of the Association for Computational Linguistics (ACL-IJCNLP). [pdf]
  2. Y. Xin, H. Lieberman, P. Chin (2021). PATCHCOMM: Using Commonsense Knowledge to Guide Syntactic Parsers. Principles of Knowledge Representation and Reasoning (KR).
  3. P. Dai, P. Chin (2021). Training Many-to-Many Recurrent Neural Networks with Target Propagation. International Conference on Artificial Neural Networks (ICANN).
  4. T. Dang, O. Thakkar, S. Ramaswamy, R. Mathews, P. Chin, F. Beaufays (2021). Revealing and Protecting Labels in Distributed Training. Conference on Neural Information Processing Systems (NeurIPS).
  5. L. Greige, P. Chin (2021). Deep Reinforcement Learning for FlipIt Security Game. Complex Networks & Their Applications X.
  6. X. Zhou, S. Qiu, P.S. Joshi, C. Xue, R.J. Killiany, A. Mian, P. Chin, R. Au, V.B. Kolachalama (2021). Enhancing Magnetic Resonance Imaging Driven Alzheimer’s Disease Classification Performance Using Generative Adversarial Learning. Alzheimer’s Research & Therapy.
  7. X. Zhou, X. Wang, G. Brown, C. Wang, P. Chin (2021). Mixed Spatio-Temporal Neural Networks on Real-time Prediction of Crimes. International Conference on Machine Learning and Applications.
  8. S. Huang, X. Zhou, P. Chin (2021). Application of Seq2Seq Models on Code Correction. Frontiers in Artifical Intelligence.

2020

  1. X. Wang, S. Wang, P.Y. Chen, X. Lin, P. Chin (2020). AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). [arXiv]
  2. L. Jensen, J. Harer, P. Chin (2020). NodeDrop: A Method for Finding Sufficient Network Architecture Size. International Joint Conference on Neural Networks (IJCNN). [arXiv]

2019

  1. X. Wang, S. Wang, P.Y. Chen, Y. Wang, B. Kulis, X. Lin, P. Chin (2019). Protecting neural networks with hierarchical random switching: towards better robustness-accuracy trade-off for stochastic defenses. International Joint Conference on Artificial Intelligence (IJCAI). [pdf]
  2. L. Jensen, G. Brown, X. Wang, J. Harer, and P. Chin (2019). Deep Learning for Minimal-context Block Trackingthrough Side-channel Analysis. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 3207-211. [pdf]

2018

  1. S. Wang, X. Wang, S. Ye, P. Zhao, X. Lin (2018). Defending dnn adversarial attacks with pruning and logits augmentation. IEEE Global Conference on Signal and Information Processing (GlobalSIP). [pdf]
  2. S. Wang, X. Wang, P. Zhao, W. Wen, D. Kaeli, P. Chin, X. Lin (2018). Defensive dropout for hardening deep neural networks under adversarial attacks. International Conference on Computer-Aided Design (ICCAD). [pdf]
  3. X. Wang, J. Zhang, T. Xiong, T. D. Tran, P. Chin, R. Etienne-Cummings (2018). Using Deep Learning to Extract Scenery Information in Real Time Spatiotemporal Compressed Sensing. IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-4. [pdf]
  4. X. Wang, K. Zhou, J. Harer, G. Brown, S. Qiu, Z. Dou, J. Wang, A. Hinton, C. A. Gonzalez, P. Chin (2018). Deep learning-based classification and anomaly detection of side-channel signals. Cyber Sensing. [pdf]
  5. W. Ma, K. Cao, X. Li, P. Chin (2018). Tree structured multimedia signal modeling. Artificial Intelligence Research Society Conference (FLAIRS). [pdf]
  6. W. Ma, K. Cao, Z. Ni, P. Chin, X. Li (2018). Sound Signal Processing with Seq2Tree Network. International Conference on Language Resources and Evaluation (LREC).[pdf]
  7. R. Russell, L. Kim, L. Hamilton, T. Lazovich, J. Harer, O. Ozdemir, P. Elingwood, M. McConley (December 2018). Automated Vulnerability Detection in Source Code Using Deep Representation Learning. IEEE International Conference on Machine Learning and Applications (ICMLA).[pdf , arXiv]
  8. J. Harer, O. Ozdemir, T. Lazovich, C. P. Reale, R. L. Russell, L. Y. Kim, P. Chin (2018). Learning to Repair Software Vulnerabilities with Generative Adversarial Networks. Conference on Neural Information Processing Systems (NeurIPS).[pdf]

2017

  1. X. Zhou, C. Wang, Y. Xu, X. Wang, P. Chin (2017). Domain Specific Inpainting With Concurrently Pretrained Generative Adversarial Networks. IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 1185-1189. [pdf]
  2. D. Yang, T. Xiong, D. Xu, S. K. Zhou, Z. Xu, M. Chen, J. H. Park, S. Grbic, T. D. Tran, P. Chin, D. Metaxas, D. Comaniciu (2017). Deep Image-to-Image Recurrent Network with Shape Basis Learning for Automatic Vertebra Labeling in Large-Scale 3D CT Volumes. International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 498-506. [pdf]
  3. D. Yang, T. Xiong, D. Xu, Q. Huang, D. Liu, S. K. Zhou, Z. Xu, J. Park, M. Chen, T. D. Tran, P. Chin, D. Metaxas, D. Comaniciu (2017). Automatic Vertebra Labeling in Large-Scale 3D CT Using Deep Image-to-Image Network With Message Passing and Sparsity Regularization. International Conference on Information Processing in Medical Imaging (IPMI). [pdf]
  4. P. Chin, J. Cohen, A. Albin, M. Hayvanovych, E. Reilly, G. Brown, J. Harer (2017). A Mathematical Analysis of Network Controllability Through Driver Nodes. IEEE Transactions on Computational Social Systems, vol. 4, no. 2, pp. 40-51. [pdf]