PPT-Graph Neural Net work and Reinforcement Learning in EDA and beyond

Callie Hao Assistant Professor ECE Georgia Institute of Technology Sharclab Georgia Tech httpssharclabecegatechedu Background Graph Neural Network GNN Reinforcement Learning RL. Classification of Sleep data. Akane. Sano. akanes@mit.edu. Affective Computing Group. Media Lab. Polysomnography. Multi-parametric test to evaluate . s. leep. Data and Labels. Data:. Healthy . students . Cost function. Machine Learning. Neural Network (Classification). Binary classification. . . 1 output unit. Layer 1. Layer 2. Layer 3. Layer 4. Multi-class classification . (K classes). K output units. Slide #. 1. Bivariate EDA. Describe the . relationship between pairs of . variables. Four characteristics to describe. Association (Direction). Form. Outliers. Strength. Quantitative Bivariate EDA. Slide #. Slide #. 1. Univariate EDA. Purpose – describe the distribution. Distribution . is concerned with what values a variable takes and how often it takes each value. Four characteristics. Shape. Outliers. An Overview . (invited paper). Andrew B. Kahng. †‡. and Farinaz Koushanfar. †∗. . †. ECE and . ‡. CSE Depts., UC San Diego. *. ECE Dept., Rice University . {. abk,fkoushanfar. }@. ucsd.edu. Human-level control through deep . reinforcment. learning. Dueling Network Architectures for Deep Reinforcement Learning. Reinforcement Learning. Reinforcement learning is a computational approach to understanding and automating good directed learning and decision making. It learns by interacting with the environment.. Aaron Schumacher. Data Science DC. 2017-11-14. Aaron Schumacher. planspace.org has these slides. Plan. applications. : . what. t. heory. applications. : . how. onward. a. pplications: what. Backgammon. Associative Learning. 3. Learning to associate one stimulus. with another.. CONDITIONING = LEARNING. Classical Conditioning. Meat Powder. Salivation. Meat Powder. Salivation. Tone. Salivation. Tone. Classical Conditioning. Kretov. Maksim. 5. vision. 1 November 2015. Plan. Part A: Reminders. Key definitions of RL and MDP. Bellman equations. General structure of RL . tasks. Part B: Application to Atari . games. Q-learning. Edubull provides online Dot Net Course. Dot Net training includes .Net Curriculum, Visual .Net, dot Net Basics, Framework, along with Online learning app, dot net framework and Asp Dot Net Video Tutorials Lingxiao Ma. . †. , Zhi Yang. . †. , Youshan Miao. ‡. , Jilong Xue. ‡. , Ming Wu. ‡. , Lidong Zhou. ‡. , . Yafei. Dai. . †. †. . Peking University. ‡ . Microsoft Research. USENIX ATC ’19, Renton, WA, USA. EDA has made available $587 million for disaster recovery grants in areas impacted byHurricanes Harvey, Irma, and Maria and wildfires and other federally declared natural disasters occurring in calend NUS U Page 1 of 1 Citation for Dr Goki Eda Citation for Dr Goki Eda Young Researcher Award Dr Goki Eda is known for his several pioneering work s in the emerging field of two - dimensional (2D Session 5: Reinforcement Learning Kenji Doya Okinawa Institute of Science and Technology Title Reinforcement learning: computational theory and neural mechanisms Abstract Reinforcement learning is a