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changes.mady.by.user Dawei Mu

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changes.mady.by.user Volodymyr Kindratenko

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  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).
  17. Chaman, Anadi, and Ivan Dokmani?. "Truly shift-invariant convolutional neural networks." arXiv preprint arXiv:2011.14214 (2020).
  18. Wei, Wei, Asad Khan, E. A. Huerta, Xiaobo Huang, and Minyang Tian. "Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers." arXiv preprint arXiv:2010.15845 (2020).
  19. Wei, Wei, and E. A. Huerta. "Deep learning for gravitational wave forecasting of neutron star mergers." arXiv preprint arXiv:2010.09751 (2020).
  20. Wei, Wei, E. A. Huerta, Mengshen Yun, Nicholas Loutrel, Roland Haas, and Volodymyr Kindratenko. "Deep Learning with Quantized Neural Networks for Gravitational Wave Forecasting of Eccentric Compact Binary Coalescence." arXiv preprint arXiv:2012.03963 (2020).
  21. Miranda, Brando. An Empirical Study of Meta-Learning: a step towards rigorously understanding meta-learning algorithms. 2020.
  22. Wang, Jiahong. "GTAMesh Dataset: Semantic 3D Perception Dataset." (2020).

  23. Luo, Di, Zhuo Chen, Kaiwen Hu, Zhizhen Zhao, Vera Mikyoung Hur, and Bryan K. Clark. "Gauge Invariant Autoregressive Neural Networks for Quantum Lattice Models." arXiv preprint arXiv:2101.07243 (2021).

  24. Graber, Colin, Grace Tsai, Michael Firman, Gabriel Brostow, and Alexander Schwing. "Panoptic Segmentation Forecasting." arXiv preprint arXiv:2104.03962 (2021).

  25. Ren, Zhongzheng, Ishan Misra, Alexander G. Schwing, and Rohit Girdhar. "3D Spatial Recognition without Spatially Labeled 3D." arXiv preprint arXiv:2105.06461 (2021).

  26. Luo, Shirui, Jiahuan Cui, Vignesh Sella, Jian Liu, Seid Koric, and Volodymyr Kindratenko. "Turbomachinery Blade Surrogate Modeling using Deep Learning." (2021)
  27. Wang, Jiangran, Zhuo Chen, Di Luo, Zhizhen Zhao, Vera Mikyoung Hur, and Bryan K. Clark. "Spacetime Neural Network for High Dimensional Quantum Dynamics." arXiv preprint arXiv:2108.02200 (2021).
  28. Cheng, Bowen, Alexander G. Schwing, and Alexander Kirillov. "Per-Pixel Classification is Not All You Need for Semantic Segmentation." arXiv preprint arXiv:2107.06278 (2021).
  29. Chong, Wing Fung, Haoen Cui, and Yuxuan Li. "Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning." arXiv preprint arXiv:2107.03340 (2021).
  30. https://arxiv.org/abs/2108.07749

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.

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