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‪Yuchi Tian‬ - ‪Google Scholar‬

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kpei AT cs DOT columbia DOT edu  Computer Science final year student Kexin Pei recently received a research assistantship of about HK$360,000 per year to support him to study for a Master of  Kexin Pei1, Yinzhi Cao2, Junfeng Yang1, Suman Jana1. 1Columbia University, 2 Lehigh University. 1. Deep learning (DL) has matched human performance! Kexin Pei (Visiting Undergraduate Student) Ganesh Raghavendran (Masters Student) Maria Reynoso (Visiting Undergraduate Student) Jiawei Song (Masters   Jul 17, 2020 Juliane Sempionatto, Muyang Lin, Lu Yin, Ernesto de la Paz, Kexin Pei, Thitaporn Sonsa-ard, Andre de Loyola Silva, Ahmed Khorshed, Fangyu  Deeptest: Automated testing of deep-neural-network-driven autonomous cars‏. Y Tian, K Pei, S Jana, B Ray‏. Proceedings of the 40th international conference  metal–organic framework for tumor imaging and radioisotope therapy†.

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Kexin Pei yYinzhi Caoz Junfeng Yang Suman Janay yColumbia University zLehigh University y{kpei, junfeng, suman}@cs.columbia.edu zyinzhi.cao@lehigh.edu Abstract Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including self-driving cars and malware detection, where the correctness and pre- Kexin Pei. We at USENIX assert that Black lives matter: Read the USENIX Statement on Racism and Black, African-American, and African Diaspora Inclusion. By Kexin Pei, Yinzhi Cao, junfeng Yang, Suman Jana cacm.acm.org — Home/Magazine Archive/November 2019 (Vol. 62, No. 11)/DeepXplore: Automated Whitebox Testing of Deep Learning/Full TextBy Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman JanaCommunications of the ACM,November 2019,Vol. 2017-08-28 · Recent advances in Deep Neural Networks (DNNs) have led to the development of DNN-driven autonomous cars that, using sensors like camera, LiDAR, etc., can drive without any human intervention. Most major manufacturers including Tesla, GM, Ford, BMW, and Waymo/Google are working on building and testing different types of autonomous vehicles.

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Kexin pei

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See the complete profile on LinkedIn and discover Kexin’s Read Kexin Pei's latest research, browse their coauthor's research, and play around with their algorithms ‪Ph.D. of Computer Science, Columbia University‬ - ‪‪Cited by 1,956‬‬ - ‪Security‬ - ‪Program Analysis‬ - ‪Machine Learning‬ Kexin Pei's 19 research works with 1,625 citations and 3,186 reads, including: XDA: Accurate, Robust Disassembly with Transfer Learning Kexin Pei. Columbia University, New York, NY, USA, Yinzhi Cao. The Johns Hopkins University, Baltimore, MD, USA, Junfeng Yang. Columbia University, New York, NY, USA, Suman Jana. Columbia University, New York, NY, USA Kexin Pei. Latest. NEUZZ: Efficient Fuzzing with Neural Program Smoothing; Powered by the Academic theme for Hugo. Cite ×.

Kexin pei

8. papers. 0. results. Research Areas. Transfer Learning • Malware Detection • Autonomous Vehicles • Vulnerability Detection • Language Modelling • Self-Supervised Kexin Pei 05.08.2017 French Revolution The Beginning of French Revolution Resentment economic enlightment leadership Storming the Bastille May -June May 7-15 Barcelona Economic: Enlightment: Leadership: Factors Bad harvest High taxes High prices Huge debts Ideas: Liberty Equality Kexin Pei is on Facebook.
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Nanoscale metal–organic frameworks (nMOFs) have been widely used in biomedical applications including cancer imaging and drug "HERCULE: Attack Story Reconstruction via Community Discovery on Correlated Log Graph," Proceedings of the 32nd Annual Computer Security Applications Conference , Los Angeles, CA, December 2016 (22.8%) Kexin Pei, Zhongshu Gu, Brendan Saltaformaggio, Shiqing Ma, Fei Wang, Zhiwei Zhang, Luo Si, Xiangyu Zhang, Dongyan Xu. Recommended Citation.

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Fei Wang 0001. Junfeng Yang. Justin Whitehouse Se hela listan på cs.columbia.edu Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, and Suman Jana, Columbia University Abstract: Due to the increasing deployment of Deep Neural Networks (DNNs) in real-world security-critical domains including autonomous vehicles and collision avoidance systems, formally checking security properties of DNNs, especially under different attacker capabilities, is becoming crucial. Share your videos with friends, family, and the world 12/16/2020 ∙ by Kexin Pei, et al. ∙ 0 ∙ share read it. Are you a researcher? Expose your work to one of the largest A.I. communities in the Radionuclides for cancer theranostic have confronted problems such as limitation in real-time visualization and unsatisfactory therapeutic effect sacrificed by the nonspecific distribution.

Python probabilistic type inference with natural language support. Cite. Authors. Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana. Abstract. Neural networks are increasingly deployed in real-world safety-critical  Kexin Pei's Homepage.