PPT-A 3 : Accelerating Attention Mechanisms in Neural Networks with Approximation

Tae Jun Ham Sung Jun Jung Seonghak Kim Young H Oh Yeonhong Park Yoonho Song JungHun Park Sanghee Lee Kyoung Park Jae W Lee DeogKyoon Jeong SEOUL NATIONAL. 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. 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. Peter Andras. School of Computing and Mathematics. Keele University. p.andras@keele.ac.uk. Overview. High-dimensional functions and low-dimensional manifolds. Manifold mapping. Function approximation over low-dimensional projections.  . G. Riddone. 25.06.2010. CLIC meeting . Contribution from R. Nousiainen, J. Huopana, T. Charles. Content . Recall of main issues. Recall of module heat dissipation . Module cooling scheme. Thermo-mechanical analysis: . 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. 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. 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. . 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:. Ali Cole. Charly. . Mccown. Madison . Kutchey. Xavier . henes. Definition. A directed network based on the structure of connections within an organism's brain. Many inputs and only a couple outputs. Daniel Boonzaaier. Supervisor – Adiel Ismail. April 2017. Content. Project Overview. Checkers – the board game. Background on Neural Networks. Neural Network applied to Checkers. Requirements. Project Plan. S.V. Kuzikov. 1. , A.A. Vikharev. 1. , J.L. Hirshfield. 2,3. 1. Institute of Applied Physics RAS, Nizhny Novgorod, Russia. 2. Yale University, New Haven, CT, USA. 3. Omega-P, Inc., New Haven, CT, USA. W. Wuensch. 11-12-2009. Accelerating structure assembly. Pulsed ΔT in accelerating structure. Beam-loading compensation. Reacting to breakdown. On/off/ramp mechanism. Dynamic vacuum. Refining design and 10% parameter consistency.