PPT-Deconvolutional Networks

Matthew D Zeiler Dilip Krishnan Graham W Taylor Rob Fergus Dept of Computer Science Courant Institute New York University Matt Zeiler Overview Unsupervised learning of mid and highlevel image representations. At each of the stations a service is provided that takes up time Workpieces approach the stations and are processed immediately if the server is idle Otherwise the workpieces line up in the buffer in front of a station and wait to receive service Ap elecommunication ne works enabl and link other critical infrastructures so any potential vulnerabilities impact whole economic systems Hence network security and critical infrastructure protection aspects must be at the centre of any IT and telecom Natalie . Enright. . Jerger. Introduction. How to connect individual devices into a group of communicating devices?. A device can be:. Component within a chip. Component within a computer. Computer. Advanced Computer Networks . Cellular/Mobile Wireless Outline. Cellular Architecture. Cellular Standards. GSM, 2G, 2.5G and 3G. Mobile Definitions. Agents, addresses, correspondent. Mobile Architecture. COMS 6998-1, Fall 2012. Instructor: Li . Erran. Li (. lel2139@columbia.edu. ). http://www.cs.columbia.edu/. ~lierranli/coms6998-11Fall2012/. Lecture 12: Mobile Platform Security: Attacks and Defenses. Semantic networks - history. Network notations are almost as old as logic. Porphyry (3rd century AD) – tree-based hierarchies to describe Aristotle’s categories. Frege (1879) - concept writing, a tree notation for the first complete version of first-order logic . 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. Machine . Learning. 1. Last Time. Perceptrons. Perceptron. Loss vs. Logistic Regression Loss. Training . Perceptrons. and Logistic Regression Models using Gradient Descent. 2. Today. Multilayer Neural Networks. First half based on slides by . Kentaro Toyama,. Microsoft Research, India. And their applications to Web. Networks—Physical & Cyber. Typhoid Mary. (Mary Mallon). Patient Zero. (Gaetan Dugas). Applications of Network Theory. Week 5. Applications. Predict the taste of Coors beer as a function of its chemical composition. What are Artificial Neural Networks? . Artificial Intelligence (AI) Technique. Artificial . Neural Networks. Brian Aronson. Review of ego networks. Ego network (personal network). Ego: Focal node/respondent. Alter: Actors ego has ties with. Dyad: Pair of individuals. Ties. (Ego). D. C. B. Tie types. Friends. 1. Local Area Networks. Aloha. Slotted Aloha. CSMA (non-persistent, 1-persistent, . p-persistent). CSMA/CD. Ethernet. Token Ring. Networks: Local Area Networks. 2. Data Link. Layer. 802.3. ). Prof. . Ralucca Gera, . Applied Mathematics Dept.. Naval Postgraduate School. Monterey, California. rgera@nps.edu. Excellence Through Knowledge. Learning Outcomes. I. dentify . network models and explain their structures.