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Publications

Journals

  1. Graber, Colin, and Alexander Schwing. "Graph Structured Prediction Energy Networks." Advances in Neural Information Processing Systems. 2019.
  2. Burke, Colin J., et al. "Deblending and classifying astronomical sources with Mask R-CNN deep learning." Monthly Notices of the Royal Astronomical Society 490.3 (2019): 3952-3965.
  3. Wei, Wei, and E. A. Huerta. "Gravitational wave denoising of binary black hole mergers with deep learning." Physics Letters B 800 (2020): 135081.
  4. Lin, Jingxiang, Unnat Jain, and Alexander Schwing. "TAB-VCR: Tags and Attributes based VCR Baselines." Advances in Neural Information Processing Systems. 2019.
  5. Wei, Wei, et al. "Deep transfer learning for star cluster classification: I. application to the PHANGS–HST survey." Monthly Notices of the Royal Astronomical Society 493.3 (2020): 3178-3193.

  6. Teixeira, Thiago SFX, William Gropp, and David Padua. "Managing code transformations for better performance portability." The International Journal of High Performance Computing Applications 33.6 (2019): 1290-1306.

  7. Venkatakrishnan, Ramshankar, Ashish Misra, and Volodymyr Kindratenko. "High-Level Synthesis-Based Approach for Accelerating Scientific Codes on FPGAs." Computing in Science & Engineering 22.4 (2020): 104-109.
  8. Song, Yu, et al. "Deep learning-based automated image segmentation for concrete petrographic analysis." Cement and Concrete Research 135 (2020): 106118.
  9. Khan, Asad, E. A. Huerta, and Arnav Das. "Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers." Physics Letters B, Volume 808, 2020, 135628, ISSN 0370-2693, https://doi.org/10.1016/j.physletb.2020.135628.

Proceedings

  1. Volodymyr Kindratenko, Dawei Mu, Yan Zhan, John Maloney, Sayed Hadi Hashemi, Benjamin Rabe, Ke Xu, Roy Campbell, Jian Peng,and William Gropp. 2020. HAL: Computer System for Scalable Deep Learning. InPractice and Experience in Advanced Research Computing(PEARC ’20), July 26–30, 2020, Portland, OR, USA.ACM, New York, NY, USA, 15 pages. https://doi.org/10.1145/3311790.3396649
  2. Hashemi, Sayed Hadi, et al. "tensorflow-tracing: A Performance Tuning Framework for Production." 2019 {USENIX} Conference on Operational Machine Learning (OpML 19). 2019.
  3. Pearson, Carl, et al. "Update on Triangle Counting on GPU." 2019 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2019.
  4. Mailthody, Vikram S., et al. "Collaborative (cpu+ gpu) algorithms for triangle counting and truss decomposition." 2018 IEEE High Performance extreme Computing Conference (HPEC). IEEE, 2018.
  5. Almasri, Mohammad, et al. "Update on k-truss Decomposition on GPU." 2019 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2019.
  6. Misra, Ashish, and Volodymyr Kindratenko. "HLS-Based Acceleration Framework for Deep Convolutional Neural Networks." International Symposium on Applied Reconfigurable Computing. Springer, Cham, 2020.
  7. Yeh, Raymond A., Yuan-Ting Hu, and Alexander G. Schwing. "Chirality Nets: Exploiting Structure in Human Pose Regression." Conference on Advances in Neural Information Processing Systems Workshop (NeurIPSW). 2019.
  8. Graber, Colin, and Alexander Schwing. "Graph Structured Prediction Energy Net Algorithms." Conference on Advances in Neural Information Processing Systems Workshop (NeurIPSW). 2019.
  9. Fang, Tiantian, and Alexander Schwing. "Co-Generation with GANs using AIS based HMC." Advances in Neural Information Processing Systems. 2019.
  10. Liu, Iou-Jen, Raymond A. Yeh, and Alexander G. Schwing. "PIC: permutation invariant critic for multi-agent deep reinforcement learning." Conference on Robot Learning. 2020.
  11. Graber, Colin, et al. "Unsupervised Discovery of Dynamic Neural Circuits." NeurIPS 2019 Workshop Neuro AI Paper28, (2019).
  12. Carrasquilla, Juan, et al. "Probabilistic Simulation of Quantum Circuits with the Transformer." arXiv preprint arXiv:1912.11052 (2019).
  13. Pearson, Carl, et al. "Node-Aware Stencil Communication for Heterogeneous Supercomputers." 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2020.
  14. Graber, Colin, and Alexander Schwing. "Dynamic Neural Relational Inference for Forecasting Trajectories." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020.
  15. Balakir, Artsiom, Alan Yang, and Elyse Rosenbaum. "An Interpretable Predictive Model for Early Detection of Hardware Failure." 2020 IEEE International Reliability Physics Symposium (IRPS). IEEE, 2020.

Archives

  1. Gupta, Arjun, et al. "Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms." arXiv preprint arXiv:1912.07618 (2019).
  2. Wei, Wei, et al. "Deep Transfer Learning for Star Cluster Classification: I. Application to the PHANGS-HST Survey." arXiv preprint arXiv:1909.02024 (2019).
  3. Gupta, Sidharth, et al. "Random mesh projectors for inverse problems." arXiv preprint arXiv:1805.11718 (2018).
  4. Luo, Shirui, et al. "Review and Examination of Input Feature Preparation Methods and Machine Learning Models for Turbulence Modeling." arXiv preprint arXiv:2001.05485 (2020).
  5. Liu, Iou-Jen, Raymond A. Yeh, and Alexander G. Schwing. "PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning." arXiv preprint arXiv:1911.00025 (2019).
  6. Carrasquilla, Juan, et al. "Probabilistic Simulation of Quantum Circuits with the Transformer." arXiv preprint arXiv:1912.11052 (2019).
  7. Shen, Hongyu, et al. "Deterministic and Bayesian Neural Networks for Low-latency Gravitational Wave Parameter Estimation of Binary Black Hole Mergers." arXiv preprint arXiv:1903.01998 (2019).
  8. Messaoud, Safa, Maghav Kumar, and Alexander G. Schwing. "Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?." arXiv preprint arXiv:2005.01508 (2020).
  9. Lin, Joshua Yao-Yu, et al. "Feature Extraction on Synthetic Black Hole Images." arXiv preprint arXiv:2007.00794 (2020).
  10. Schwartz, Lane, et al. "Neural Polysynthetic Language Modelling." arXiv preprint arXiv:2005.05477 (2020).
  11. Abavisani, Ali, and Mark Hasegawa-Johnson. "Automatic Estimation of Inteligibility Measure for Consonants in Speech." arXiv preprint arXiv:2005.06065 (2020).
  12. Ren, Zhongzheng, Raymond A. Yeh, and Alexander G. Schwing. "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning." arXiv preprint arXiv:2007.01293 (2020).
  13. Hidayetoglu, Mert, Carl Pearson, Vikram Sharma Mailthody, Eiman Ebrahimi, Jinjun Xiong, Rakesh Nagi, and Wen-mei Hwu. "At-Scale Sparse Deep Neural Network Inference With Efficient GPU Implementation." arXiv e-prints (2020): arXiv-2007.
  14. Huerta, E., Asad Khan, Xiaobo Huang, Minyang Tian, Maksim Levental, Ryan Chard, Wei Wei, Maeve Heflin, D. Katz, Volodymyr Kindratenko, Dawei Mu, B. Blaiszik and I. Foster. “Confluence of Artificial Intelligence and High Performance Computing for Accelerated, Scalable and Reproducible Gravitational Wave Detection.” (2020).
  15. Lin, Joshua Yao-Yu, Sneh Pandya, Devanshi Pratap, Xin Liu, and Matias Carrasco Kind. "AGNet: Weighing Black Holes with Machine Learning." arXiv preprint arXiv:2011.15095 (2020).
  16. Luo, Shirui, and Volodymyr Kindratenko. "Hands-on with IBM Visual Insights." Authorea Preprints (2020).

Thesis

  1. Yuan Ma, Accelerating Convolution in Deep Neural Networks on CAPI-based FPGA, Senior Thesis, Spring 2020

  2. Benjamin Rabe, Hyperparameter Tuning and its Effects on Deep Learning Performance and Generalization, MS Thesis, Spring 2020

  3. Jiaying Wu, An Adaptive Pruning Algorithm for DNN Compression, MS Thesis, Spring 2020

  4. Sayed Hadi Hashemi, Timed Execution in Distributed Machine Learning, PhD Thesis, Spring 2020

  5. Sun, Ray. "Environmental curriculum learning for efficiently achieving superhuman play in games." PhD diss., 2020.

Technical Reports

  1. Dcase 2020 Task 2: Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring
  2. Dcase 2020 Task 1: Subtask B: Low-Complexity Acoustic Sceneclassification

Presentations

  1. PEARC20 - Thursday, July 30 • 10:00am - 11:00am - HAL: Computer System for Scalable Deep Learning - Slides

  2. HDF5 User Group Meeting 2020 - October 13-16, 2020 - tar2h5: a data convertor toolset for machine learning

Posters

Software/Model repositories

  1. https://github.com/joshualin24/Deep-Learning-Cosmic-Void-2019
  2. https://github.com/ncsa/swsuite
  3. https://github.com/HDFGroup/tar2h5

Benchmarks


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