PPT-TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

T Chen T Moreau Z Jiang L Zheng S Jiao E Yan H Shen M Cowan L Wang Y Hu L Ceze C Guestrin and A Krishnamurthy Presentation by Grzegorz . Adam Coates. Stanford University. (Visiting Scholar: Indiana University, Bloomington). What do we want ML to do?. Given image, predict complex high-level patterns:. Object recognition. Detection. Segmentation. Quoc V. Le. Stanford University and Google. Purely supervised. Quoc V. . Le. Almost abandoned between 2000-2006. - . Overfitting. , slow, many local minima, gradient vanishing. In 2006, Hinton, et. al. proposed RBMs to . to Speech . EE 225D - . Audio Signal Processing in Humans and Machines. Oriol Vinyals. UC Berkeley. This is my biased view about deep learning and, more generally, machine learning past and current research!. Impermeable Film . Covering for Strawberry Production. Ruijun Qin, . Oleg Daugovish, Suduan Gao, Brad Hanson, James Gerik and Husein Ajwa. Raised-bed strawberry production. California strawberries are grown mainly in raised-bed systems tarped with standard polyethylene film (PE). . Nuno Lopes . and. José Monteiro. Deriving preconditions by hand is hard; WPs are often non-trivial. WPs derived by hand are often wrong!. Weaker preconditions expose more optimization opportunities. Carey . Nachenberg. Deep Learning for Dummies (Like me) – Carey . Nachenberg. (Like me). The Goal of this Talk?. Deep Learning for Dummies (Like me) – Carey . Nachenberg. 2. To provide you with . By Namita Dave. Overview. What are compiler optimizations?. Challenges with optimizations. Current Solutions. Machine learning techniques. Structure of Adaptive compilers. Introduction. O. ptimization . CS 501:CS Seminar. Min Xian. Assistant Professor. Department of Computer Science. University of Idaho. Image from NVIDIA. Researchers:. Geoff Hinton. Yann . LeCun. Andrew Ng. Yoshua. . Bengio. …. The Build Alpha is a genetic trading software that offers a wide range of clients to use the best systematic trading strategies based on user-selected fitness functions such as net profits, overall risk, etc. and test criteria. usually machine language, sometimes assembly language as an intermediate. for Java and . .net. , compilers convert code into an intermediate format. a just-in-time (JIT) compiler or interpreter then converts it into machine code. Kexin Pei. 1. , Yinzhi Cao. 2. , Junfeng Yang. 1. , Suman Jana. 1. 1. Columbia University, . 2. Lehigh University. 1. Deep learning (DL) has matched human performance!. Image recognition, speech recognition, machine translation, intrusion detection.... Time Value . of Money . 11-A Grange Building. University Park, PA 16802. financialliteracy.psu.edu. finlit@psu.edu. . 814-863-0214. Description . Time value of money . (TVM). :. . Money that is available at the present time is worth more than the same amount in the future, due to its potential earning capacity . Nuno Lopes . and. José Monteiro. Deriving preconditions by hand is hard; WPs are often non-trivial. WPs derived by hand are often wrong!. Weaker preconditions expose more optimization opportunities. V. . Kain. , M. Fraser, B. Goddard, S. . Hirlander. , M. Schenk, F. . Velotti. CERN, EPFL, University of Malta. Lots of input from S. Levine’s lectures on Deep Reinforcement Learning at UC Berkeley .