Chunmei Liu

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All published works

Action	Title	Year	Authors
+ PDF Chat	AdapShare: An RL-Based Dynamic Spectrum Sharing Solution for O-RAN	2024	Sneihil Gopal David Griffith Richard Rouil Chunmei Liu
+ PDF Chat	ProSAS: An O-RAN Approach to Spectrum Sharing between NR and LTE	2024	Sneihil Gopal David Griffith Richard Rouil Chunmei Liu
+	Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS	2020	Felix Olowononi Danda B. Rawat Chunmei Liu
+	Some equalities for estimations of variance components in a general linear model and its restricted and transformed models	2010	Yongge Tian Chunmei Liu
+	On the mean square error of parameter estimates for some biased estimators	2005	Zhifu Wang Xian-Wei Yu Chunmei Liu Mi Ying

Common Coauthors

Coauthor	Papers Together
Sneihil Gopal	2
David Griffith	2
Richard Rouil	2
Yongge Tian	1
Xian-Wei Yu	1
Mi Ying	1
Zhifu Wang	1
Danda B. Rawat	1
Felix Olowononi	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Explaining and Harnessing Adversarial Examples	2014	Ian Goodfellow Jonathon Shlens Christian Szegedy	1
+	The singular value decomposition of matrices and cheap numerical filtering of systems of linear equations	1972	L. J. Crone	1
+	More on maximal and minimal ranks of Schur complements with applications	2003	Yongge Tian	1
+	Ridge Regression: Applications to Nonorthogonal Problems	1970	Arthur E. Hoerl Robert W. Kennard	1
+	Rank equalities related to outer inverses of matrices and applications	2001	Yongge Tian	1
+	A property of partitioned generalized regression	1992	Markku Nurhonen Simo Puntanen	1
+	On Biased Estimation in Linear Models	1973	Lawrence S. Mayer Thomas A. Willke	1
+	Cochran's statistical theorem for outer inverses of matrices and matrix quadratic forms	2005	Yongge Tian George P. H. Styan	1
+	Cochran's statistical theorem revisited	2004	Yongge Tian George P. H. Styan	1
+	Equalities and Inequalities for Ranks of Matrices<sup>†</sup>	1974	George Matsaglia George P. H. Styan	1
+	A note on equality of MINQUE and simple estimator in the general Gauss-Markov model	1997	Jürgen Groβ	1
+	The minimum mean square error linear estimator and ridge regression	2003	Zhifu Wang Xian-Wei Yu	1
+	Do Deep Nets Really Need to be Deep?	2013	Jimmy Ba Rich Caruana	1
+	Generalized Inverse of Matrices and Its Applications	1973	K. S. Banerjee	1
+	Some matrix results related to a partitioned singular linear model	1996	Simo Puntanen	1
+	Continuous control with deep reinforcement learning	2015	Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver Daan Wierstra	1
+ PDF Chat	The Limitations of Deep Learning in Adversarial Settings	2016	Nicolas Papernot Patrick McDaniel Somesh Jha Matt Fredrikson Z. Berkay Celik Ananthram Swami	1
+	Value Iteration Networks	2016	Aviv Tamar Yi Wu Garrett Thomas Sergey Levine Pieter Abbeel	1
+	Generalized Inverse of Matrices and its Applications	1972	Alan J. Mayne	1
+ PDF Chat	Improving the Robustness of Deep Neural Networks via Stability Training	2016	Stephan Zheng Yang Song Thomas Leung Ian Goodfellow	1
+	Adversarial examples in the physical world	2016	Alexey Kurakin Ian Goodfellow Samy Bengio	1
+	On the Effectiveness of Defensive Distillation	2016	Nicolas Papernot Patrick McDaniel	1
+	Defensive Distillation is Not Robust to Adversarial Examples	2016	Nicholas Carlini David Wagner	1
+	Reinforcement Learning with Unsupervised Auxiliary Tasks	2016	Max Jaderberg Volodymyr Mnih Wojciech Marian Czarnecki Tom Schaul Joel Z. Leibo David Silver Koray Kavukcuoglu	1
+ PDF Chat	Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics	2017	Xin Li Fuxin Li	1
+	Delving into Transferable Adversarial Examples and Black-box Attacks	2016	Yanpei Liu Xinyun Chen Chang Liu Dawn Song	1
+	NIPS 2016 Tutorial: Generative Adversarial Networks	2017	Ian Goodfellow	1
+	Cyber-Physical Systems Security—A Survey	2017	Abdulmalik Humayed Jingqiang Lin Fengjun Li Bo Luo	1
+	Adversarial Attacks on Neural Network Policies	2017	Sandy H. Huang Nicolas Papernot Ian Goodfellow Yan Duan Pieter Abbeel	1
+	On Detecting Adversarial Perturbations	2016	Jan Hendrik Metzen Tim Genewein Volker Fischer Bastian Bischoff	1
+	Detecting Adversarial Samples from Artifacts.	2017	Reuben Feinman Ryan R. Curtin Saurabh Shintre Andrew B. Gardner	1
+ PDF Chat	Practical Black-Box Attacks against Machine Learning	2017	Nicolas Papernot Patrick McDaniel Ian Goodfellow Somesh Jha Z. Berkay Celik Ananthram Swami	1
+	Tactics of Adversarial Attack on Deep Reinforcement Learning Agents	2017	Yen-Chen Lin Zhang-Wei Hong Yuan-Hong Liao Meng-Li Shih Ming-Yu Liu Min Sun	1
+	Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks	2018	Weilin Xu David Evans Yanjun Qi	1
+ PDF Chat	SafetyNet: Detecting and Rejecting Adversarial Examples Robustly	2017	Jiajun Lu Theerasit Issaranon David Forsyth	1
+	Extending Defensive Distillation	2017	Nicolas Papernot Patrick McDaniel	1
+	Delving into adversarial attacks on deep policies	2017	Jernej Kos Dawn Song	1
+	Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods	2017	Nicholas Carlini David Wagner	1
+	Unified theory of linear estimation	1971	C. Radhakrishna Rao	1
+	Proximal Policy Optimization Algorithms	2017	John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov	1
+	Synthesizing Robust Adversarial Examples	2017	Anish Athalye Logan Engstrom Andrew Ilyas Kevin Kwok	1
+	Cascade Adversarial Machine Learning Regularized with a Unified Embedding	2017	Taesik Na Jong Hwan Ko Saibal Mukhopadhyay	1
+ PDF Chat	ZOO	2017	Pin‐Yu Chen Huan Zhang Yash Sharma Jinfeng Yi Cho‐Jui Hsieh	1
+	Detecting Adversarial Attacks on Neural Network Policies with Visual Foresight	2017	Yen-Chen Lin Ming-Yu Liu Min Sun Jia‐Bin Huang	1
+	Countering Adversarial Images using Input Transformations	2017	Chuan Guo Mayank Rana Moustapha Cissé Laurens van der Maaten	1
+	Provable defenses against adversarial examples via the convex outer adversarial polytope	2017	J. Zico Kolter Eric Wong	1
+	Mitigating Adversarial Effects Through Randomization	2017	Cihang Xie Jianyu Wang Zhishuai Zhang Zhou Ren Alan Yuille	1
+ PDF Chat	Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing Their Input Gradients	2018	Andrew Slavin Ross Finale Doshi‐Velez	1
+	Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning	2018	Battista Biggio Fabio Roli	1
+	Distilling the Knowledge in a Neural Network	2015	Geoffrey E. Hinton Oriol Vinyals Jay B. Dean	1