How to Represent Part-Whole Hierarchies in a Neural Network
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Backpropagation and Deep Learning in the Brain
Backpropagation and Deep Learning in the Brain Simons Institute -- Computational Theories of the Brain 2018 Timothy Lillicrap DeepMind, UCL With: Sergey Bartunov, Adam Santoro, Jordan Guerguiev, Blake Richards, Luke Marris, Daniel Cownden, Colin Akerman, Douglas Tweed, Geoffrey Hinton The “credit assignment” problem The solution in artificial networks: backprop Credit assignment by backprop works well in practice and shows up in virtually all of the state-of-the-art supervised, unsupervised, and reinforcement learning algorithms. Why Isn’t Backprop “Biologically Plausible”? Why Isn’t Backprop “Biologically Plausible”? Neuroscience Evidence for Backprop in the Brain? A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: How to convince a neuroscientist that the cortex is learning via [something like] backprop - To convince a machine learning researcher, an appeal to variance in gradient estimates might be enough. - But this is rarely enough to convince a neuroscientist. - So what lines of argument help? How to convince a neuroscientist that the cortex is learning via [something like] backprop - What do I mean by “something like backprop”?: - That learning is achieved across multiple layers by sending information from neurons closer to the output back to “earlier” layers to help compute their synaptic updates. How to convince a neuroscientist that the cortex is learning via [something like] backprop 1. Feedback connections in cortex are ubiquitous and modify the -
A Boltzmann Machine Implementation for the D-Wave
A Boltzmann Machine Implementation for the D-Wave John E. Dorband Ph.D. Dept. of Computer Science and Electrical Engineering University of Maryland, Baltimore County Baltimore, Maryland 21250, USA [email protected] Abstract—The D-Wave is an adiabatic quantum computer. It is an understatement to say that it is not a traditional computer. It can be viewed as a computational accelerator or more precisely a computational oracle, where one asks it a relevant question and it returns a useful answer. The question is how do you ask a relevant question and how do you use the answer it returns. This paper addresses these issues in a way that is pertinent to machine learning. A Boltzmann machine is implemented with the D-Wave since the D-Wave is merely a hardware instantiation of a partially connected Boltzmann machine. This paper presents a prototype implementation of a 3-layered neural network where the D-Wave is used as the middle (hidden) layer of the neural network. This paper also explains how the D-Wave can be utilized in a multi-layer neural network (more than 3 layers) and one in which each layer may be multiple times the size of the D- Wave being used. Keywords-component; quantum computing; D-Wave; neural network; Boltzmann machine; chimera graph; MNIST; I. INTRODUCTION The D-Wave is an adiabatic quantum computer [1][2]. Its Figure 1. Nine cells of a chimera graph. Each cell contains 8 qubits. function is to determine a set of ones and zeros (qubits) that minimize the objective function, equation (1). -
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design Jeffrey Dean Google Research [email protected] Abstract The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore’s Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today. Introduction The past decade has seen a remarkable series of advances in machine learning (ML), and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas [LeCun et al. 2015]. Major areas of significant advances include computer vision [Krizhevsky et al. 2012, Szegedy et al. 2015, He et al. 2016, Real et al. 2017, Tan and Le 2019], speech recognition [Hinton et al. -
Spring 2013 Mapping (CBAM)
UBATOR http://inc.uscd.edu/ In this Issue: UCSD Creates Center for Brain Activity Mapping On the Cover UCSD Creates Responding to President Barack Obama’s “grand challenge” to chart Center for Brain the function of the human brain in unprecedented detail, the University Activity Mapping of California, San Diego has established the Center for Brain Activity Spring 2013 Mapping (CBAM). The new center, under the aegis of the interdisciplinary Kavli Institute for Brain and Sejnowski Elected to Mind, will tackle the technological and biological challenge of developing a new American Academy generation of tools to enable recording of neuronal activity throughout the brain. It of Arts and Sciences will also conduct brain-mapping experiments and analyze the collected data. Pg 3 Ralph Greenspan–one of the original architects of a visionary proposal that eventually led to the national BRAIN Initiative launched by President Obama in April–has been named CBAM’s founding director. (cont on page 2) Faculty Spotlight: Garrison W. Cottrell Pg 4 Faculty Spotlight: Patricia Gorospe Pg 7 INC Events Pg 9 INC at a glance Pg 10 From left, Nick Spitzer, Ralph Greenspan, and Terry Sejnowski. (Photos by Erik Jepsen/UC San Diego Publications) 1 INSTITUTE FOR NEURAL COMPUTATION Brain Activity Mapping, cont from page 1 UC San Diego Chancellor Pradeep K. Khosla, who attended Obama’s unveiling of the BRAIN Initiative, said: “I am pleased to announce the launch of the Spring 2013 Center for Brain Activity Mapping. This new center will require the type of in-depth and impactful research that we are so good at producing at UC San Diego. -
SFR27 13 Harris Et Al.Indd
From “The Neocortex,” edited by W. Singer, T. J. Sejnowski and P. Rakic. Strüngmann Forum Reports, vol. 27, J. R. Lupp, series editor. Cambridge, MA: MIT Press. ISBN 978-0-262-04324-3 13 Functional Properties of Circuits, Cellular Populations, and Areas Kenneth D. Harris, Jennifer M. Groh, James DiCarlo, Pascal Fries, Matthias Kaschube, Gilles Laurent, Jason N. MacLean, David A. McCormick, Gordon Pipa, John H. Reynolds, Andrew B. Schwartz, Terrence J. Sejnowski, Wolf Singer, and Martin Vinck Abstract A central goal of systems neuroscience is to understand how the brain represents and processes information to guide behavior (broadly defi ned as encompassing perception, cognition, and observable outcomes of those mental states through action). These con- cepts have been central to research in this fi eld for at least sixty years, and research efforts have taken a variety of approaches. At this Forum, our discussions focused on what is meant by “functional” and “inter-areal,” what new concepts have emerged over the last several decades, and how we need to update and refresh these concepts and ap- proaches for the coming decade. In this chapter, we consider some of the historical conceptual frameworks that have shaped consideration of neural coding and brain function, with an eye toward what aspects have held up well, what aspects need to be revised, and what new concepts may foster future work. Conceptual frameworks need to be revised periodically lest they become counter- productive and actually blind us to the signifi cance of novel discoveries. -
ARCHITECTS of INTELLIGENCE for Xiaoxiao, Elaine, Colin, and Tristan ARCHITECTS of INTELLIGENCE
MARTIN FORD ARCHITECTS OF INTELLIGENCE For Xiaoxiao, Elaine, Colin, and Tristan ARCHITECTS OF INTELLIGENCE THE TRUTH ABOUT AI FROM THE PEOPLE BUILDING IT MARTIN FORD ARCHITECTS OF INTELLIGENCE Copyright © 2018 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Acquisition Editors: Ben Renow-Clarke Project Editor: Radhika Atitkar Content Development Editor: Alex Sorrentino Proofreader: Safis Editing Presentation Designer: Sandip Tadge Cover Designer: Clare Bowyer Production Editor: Amit Ramadas Marketing Manager: Rajveer Samra Editorial Director: Dominic Shakeshaft First published: November 2018 Production reference: 2201118 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK ISBN 978-1-78913-151-2 www.packt.com Contents Introduction ........................................................................ 1 A Brief Introduction to the Vocabulary of Artificial Intelligence .......10 How AI Systems Learn ........................................................11 Yoshua Bengio .....................................................................17 Stuart J. -
Fast Neural Network Emulation of Dynamical Systems for Computer Animation
Fast Neural Network Emulation of Dynamical Systems for Computer Animation Radek Grzeszczuk 1 Demetri Terzopoulos 2 Geoffrey Hinton 2 1 Intel Corporation 2 University of Toronto Microcomputer Research Lab Department of Computer Science 2200 Mission College Blvd. 10 King's College Road Santa Clara, CA 95052, USA Toronto, ON M5S 3H5, Canada Abstract Computer animation through the numerical simulation of physics-based graphics models offers unsurpassed realism, but it can be computation ally demanding. This paper demonstrates the possibility of replacing the numerical simulation of nontrivial dynamic models with a dramatically more efficient "NeuroAnimator" that exploits neural networks. Neu roAnimators are automatically trained off-line to emulate physical dy namics through the observation of physics-based models in action. De pending on the model, its neural network emulator can yield physically realistic animation one or two orders of magnitude faster than conven tional numerical simulation. We demonstrate NeuroAnimators for a va riety of physics-based models. 1 Introduction Animation based on physical principles has been an influential trend in computer graphics for over a decade (see, e.g., [1, 2, 3]). This is not only due to the unsurpassed realism that physics-based techniques offer. In conjunction with suitable control and constraint mechanisms, physical models also facilitate the production of copious quantities of real istic animation in a highly automated fashion. Physics-based animation techniques are beginning to find their way into high-end commercial systems. However, a well-known drawback has retarded their broader penetration--compared to geometric models, physical models typically entail formidable numerical simulation costs. This paper proposes a new approach to creating physically realistic animation that differs Emulation for Animation 883 radically from the conventional approach of numerically simulating the equations of mo tion of physics-based models. -
Neural Networks for Machine Learning Lecture 4A Learning To
Neural Networks for Machine Learning Lecture 4a Learning to predict the next word Geoffrey Hinton with Nitish Srivastava Kevin Swersky A simple example of relational information Christopher = Penelope Andrew = Christine Margaret = Arthur Victoria = James Jennifer = Charles Colin Charlotte Roberto = Maria Pierro = Francesca Gina = Emilio Lucia = Marco Angela = Tomaso Alfonso Sophia Another way to express the same information • Make a set of propositions using the 12 relationships: – son, daughter, nephew, niece, father, mother, uncle, aunt – brother, sister, husband, wife • (colin has-father james) • (colin has-mother victoria) • (james has-wife victoria) this follows from the two above • (charlotte has-brother colin) • (victoria has-brother arthur) • (charlotte has-uncle arthur) this follows from the above A relational learning task • Given a large set of triples that come from some family trees, figure out the regularities. – The obvious way to express the regularities is as symbolic rules (x has-mother y) & (y has-husband z) => (x has-father z) • Finding the symbolic rules involves a difficult search through a very large discrete space of possibilities. • Can a neural network capture the same knowledge by searching through a continuous space of weights? The structure of the neural net local encoding of person 2 output distributed encoding of person 2 units that learn to predict features of the output from features of the inputs distributed encoding of person 1 distributed encoding of relationship local encoding of person 1 inputs local encoding of relationship Christopher = Penelope Andrew = Christine Margaret = Arthur Victoria = James Jennifer = Charles Colin Charlotte What the network learns • The six hidden units in the bottleneck connected to the input representation of person 1 learn to represent features of people that are useful for predicting the answer. -
Lecture Notes Geoffrey Hinton
Lecture Notes Geoffrey Hinton Overjoyed Luce crops vectorially. Tailor write-ups his glasshouse divulgating unmanly or constructively after Marcellus barb and outdriven squeakingly, diminishable and cespitose. Phlegmatical Laurance contort thereupon while Bruce always dimidiating his melancholiac depresses what, he shores so finitely. For health care about working memory networks are the class and geoffrey hinton and modify or are A practical guide to training restricted boltzmann machines Lecture Notes in. Trajectory automatically learn about different domains, geoffrey explained what a lecture notes geoffrey hinton notes was central bottleneck form of data should take much like a single example, geoffrey hinton with mctsnets. Gregor sieber and password you may need to this course that models decisions. Jimmy Ba Geoffrey Hinton Volodymyr Mnih Joel Z Leibo and Catalin Ionescu. YouTube lectures by Geoffrey Hinton3 1 Introduction In this topic of boosting we combined several simple classifiers into very complex classifier. Separating Figure from stand with a Parallel Network Paul. But additionally has efficient. But everett also look up. You already know how the course that is just to my assignment in the page and writer recognition and trends in the effect of the motivating this. These perplex the or free liquid Intelligence educational. Citation to lose sight of language. Sparse autoencoder CS294A Lecture notes Andrew Ng Stanford University. Geoffrey Hinton on what's nothing with CNNs Daniel C Elton. Toronto Geoffrey Hinton Advanced Machine Learning CSC2535 2011 Spring. Cross validated is sparse, a proof of how to list of possible configurations have to see. Cnns that just download a computational power nor the squared error in permanent electrode array with respect to the. -
The Dartmouth College Artificial Intelligence Conference: the Next
AI Magazine Volume 27 Number 4 (2006) (© AAAI) Reports continued this earlier work because he became convinced that advances The Dartmouth College could be made with other approaches using computers. Minsky expressed the concern that too many in AI today Artificial Intelligence try to do what is popular and publish only successes. He argued that AI can never be a science until it publishes what fails as well as what succeeds. Conference: Oliver Selfridge highlighted the im- portance of many related areas of re- search before and after the 1956 sum- The Next mer project that helped to propel AI as a field. The development of improved languages and machines was essential. Fifty Years He offered tribute to many early pio- neering activities such as J. C. R. Lick- leiter developing time-sharing, Nat Rochester designing IBM computers, and Frank Rosenblatt working with James Moor perceptrons. Trenchard More was sent to the summer project for two separate weeks by the University of Rochester. Some of the best notes describing the AI project were taken by More, al- though ironically he admitted that he ■ The Dartmouth College Artificial Intelli- Marvin Minsky, Claude Shannon, and never liked the use of “artificial” or gence Conference: The Next 50 Years Nathaniel Rochester for the 1956 “intelligence” as terms for the field. (AI@50) took place July 13–15, 2006. The event, McCarthy wanted, as he ex- Ray Solomonoff said he went to the conference had three objectives: to cele- plained at AI@50, “to nail the flag to brate the Dartmouth Summer Research summer project hoping to convince the mast.” McCarthy is credited for Project, which occurred in 1956; to as- everyone of the importance of ma- coining the phrase “artificial intelli- sess how far AI has progressed; and to chine learning. -
CNN Encoder to Reduce the Dimensionality of Data Image For
CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning Janderson Ferreira1, Agostinho A. F. J´unior1, Yves M. Galv˜ao1 1 1 2 ∗ Bruno J. T. Fernandes and Pablo Barros , 1- Universidade de Pernambuco - Escola Polit´ecnica de Pernambuco Rua Benfica 455, Recife/PE - Brazil 2- Cognitive Architecture for Collaborative Technologies (CONTACT) Unit Istituto Italiano di Tecnologia, Genova, Italy Abstract. Many real-world applications need path planning algorithms to solve tasks in different areas, such as social applications, autonomous cars, and tracking activities. And most importantly motion planning. Although the use of path planning is sufficient in most motion planning scenarios, they represent potential bottlenecks in large environments with dynamic changes. To tackle this problem, the number of possible routes could be reduced to make it easier for path planning algorithms to find the shortest path with less efforts. An traditional algorithm for path planning is the A*, it uses an heuristic to work faster than other solutions. In this work, we propose a CNN encoder capable of eliminating useless routes for motion planning problems, then we combine the proposed neural network output with A*. To measure the efficiency of our solution, we propose a database with different scenarios of motion planning problems. The evaluated metric is the number of the iterations to find the shortest path. The A* was compared with the CNN Encoder (proposal) with A*. In all evaluated scenarios, our solution reduced the number of iterations by more than 60%. 1 Introduction Present in many daily applications, path planning algorithm helps to solve many navigation problems. -
The History Began from Alexnet: a Comprehensive Survey on Deep Learning Approaches
> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches Md Zahangir Alom1, Tarek M. Taha1, Chris Yakopcic1, Stefan Westberg1, Paheding Sidike2, Mst Shamima Nasrin1, Brian C Van Essen3, Abdul A S. Awwal3, and Vijayan K. Asari1 Abstract—In recent years, deep learning has garnered I. INTRODUCTION tremendous success in a variety of application domains. This new ince the 1950s, a small subset of Artificial Intelligence (AI), field of machine learning has been growing rapidly, and has been applied to most traditional application domains, as well as some S often called Machine Learning (ML), has revolutionized new areas that present more opportunities. Different methods several fields in the last few decades. Neural Networks have been proposed based on different categories of learning, (NN) are a subfield of ML, and it was this subfield that spawned including supervised, semi-supervised, and un-supervised Deep Learning (DL). Since its inception DL has been creating learning. Experimental results show state-of-the-art performance ever larger disruptions, showing outstanding success in almost using deep learning when compared to traditional machine every application domain. Fig. 1 shows, the taxonomy of AI. learning approaches in the fields of image processing, computer DL (using either deep architecture of learning or hierarchical vision, speech recognition, machine translation, art, medical learning approaches) is a class of ML developed largely from imaging, medical information processing, robotics and control, 2006 onward. Learning is a procedure consisting of estimating bio-informatics, natural language processing (NLP), cybersecurity, and many others.