PPT-Neural Architectures with Memory

Nitish Gupta Shreya Rajpal 25 th April 2017 1 Story Comprehension 2 Joe went to the kitchen Fred went to the kitchen Joe picked up the milk Joe travelled to his office Joe left the milk Joe went to the bathroom . and Connectionism. Stephanie Rosenthal. September 9, 2015. Associationism. and the Brain. Aristotle counted four laws of association when he examined the processes of remembrance and recall:. The law of contiguity. Things or events that occur close to each other in space or time tend to get linked together . Kong Da, Xueyu Lei & Paul McKay. Digit Recognition. Convolutional Neural Network. Inspired by the visual cortex. Our example: Handwritten digit recognition. Reference: . LeCun. et al. . Back propagation Applied to Handwritten Zip Code Recognition. 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. Banafsheh. . Rekabdar. Biological Neuron:. The Elementary Processing Unit of the Brain. Biological Neuron:. A Generic Structure. Dendrite. Soma. Synapse. Axon. Axon Terminal. Biological Neuron – Computational Intelligence Approach:. 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. Software Architecture. Describe how the various software components are to be organized and how they should interact.. It describe the organization and interaction of software components.. System Architecture. What are Artificial Neural Networks (ANN)?. ". Colored. neural network" by Glosser.ca - Own work, Derivative of File:Artificial neural . network.svg. . Licensed under CC BY-SA 3.0 via Commons - https://commons.wikimedia.org/wiki/File:Colored_neural_network.svg#/media/File:Colored_neural_network.svg. 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. Abhinav . Podili. , Chi Zhang, Viktor . Prasanna. Ming Hsieh Department of Electrical Engineering. University of Southern California. {. podili. , zhan527, . prasanna. }@usc.edu. fpga.usc.edu. ASAP, July 2017. 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. Understand how multiprocessor architectures are classified.. Appreciate the factors that create complexity in multiprocessor systems.. Become familiar with the ways in which some architectures transcend the traditional von Neumann paradigm.. Beth A. Taylor, Ph.D.. Director, Exercise Physiology Research, Department of Cardiology, Hartford Hospital. Associate Professor, Department of Kinesiology, University of Connecticut. Background. Mild CNS complaints second most commonly reported adverse effect of statin drugs. CUDA Lecture 3. Parallel Architectures and Performance Analysis. Conventional Von Neumann architecture consists of a processor executing a program stored in a (main) memory:. Each main memory location located by its address. Addresses start at zero and extend to 2. Recall: Microprocessors are classified by how memory is organized. Tightly-coupled multiprocessor systems use the same memory. They are also referred to as . shared memory multiprocessors. .. The processors do not necessarily have to share the same block of physical memory: .