PPT-Symbolic/Neural

Reasoning With Declarative Knowledge William W Cohen Machine Learning Department Carnegie Mellon University Google joint with Fan Yang Zhilin Yang Kathryn Rivard Mazaitis What is intelligence. Corina. . Pasareanu. Carnegie Mellon/NASA Ames. c. orina.s.pasareanu@nasa.gov. Overview. “Classical” symbolic execution and its variants. Generalized symbolic execution . D. ynamic and . concolic. 1. Recurrent Networks. Some problems require previous history/context in order to be able to give proper output (speech recognition, stock forecasting, target tracking, etc.. One way to do that is to just provide all the necessary context in one "snap-shot" and use standard learning. Brains and games. Introduction. Spiking Neural Networks are a variation of traditional NNs that attempt to increase the realism of the simulations done. They more closely resemble the way brains actually operate. and parallel corpus generation. Ekansh. Gupta. Rohit. Gupta. Advantages of Neural Machine Translation Models. Require . only a fraction of the memory needed by traditional statistical machine translation (SMT) . Serve as a representation of a specific person, act, deed, place or conflict. They are easily recognizable but not as common as situational archetypes.. The Archetypes Include: . Light vs. Darkness. CAP5615 Intro. to Neural Networks. Xingquan (Hill) Zhu. Outline. Multi-layer Neural Networks. Feedforward Neural Networks. FF NN model. Backpropogation (BP) Algorithm. BP rules derivation. Practical Issues of FFNN. Table of Contents. Part 1: The Motivation and History of Neural Networks. Part 2: Components of Artificial Neural Networks. Part 3: Particular Types of Neural Network Architectures. Part 4: Fundamentals on Learning and Training Samples. Abhishek Narwekar, Anusri Pampari. CS 598: Deep Learning and Recognition, Fall 2016. Lecture Outline. Introduction. Learning Long Term Dependencies. Regularization. Visualization for RNNs. Section 1: Introduction. Omid Kashefi. omid.Kashefi@pitt.edu. Visual Languages Seminar. November, 2016. Outline. Machine Translation. Deep Learning. Neural Machine Translation. Machine Translation. Machine Translation. Use of software in translating from one language into another. Cristian. . Cadar. , Patrice . Godefroid. , . Sarfraz. . Khurshid. , . Corina. . Pasareanu. , . Koushik. . Sen. , Nikolai . Tillmann. , Willem . Visser. Overview. S. ymbolic execution and its variants. Recurrent Neural Network Cell. Recurrent Neural Networks (unenrolled). LSTMs, Bi-LSTMs, Stacked Bi-LSTMs. Today. Recurrent Neural Network Cell.  .  .  .  . Recurrent Neural Network Cell.  .  .  . Psychology 209 – Winter 2017. March 7, 2017. The DNC architecture. Key features of the architecture. Indefinite memory size (Turing). Accurate storage of long lists of items after a single presentation. 18. The Symbolic Toolbox. 2. The Symbolic Toolbox. MATLAB has a set of built-in commands that allow us to work with functions in a fashion similar to Mathematica. .. For more on the commands available, type “help Symbolic Toolbox”.. Learning Objectives~ Ch. 16. Discuss how products, special possessions, and consumption activities gain symbolic meaning and how this meaning is conveyed from one consumer to another.. Identify how marketers can influence or make use of the symbolic meaning that consumption may have for consumers..