COMP9444 Neural Networks and Deep Learning Outline 9b. Generative Adversarial Networks Artist-Critic Co-Evolution Co-Evolution Paradigms Blind Watchmaker (GP Artist, Human Critic) Evolutonary Art (GP Artist, GP or NN Critic) Generative Adversarial Networks (CNN Artist, CNN Critic) COMP9444 ⃝c Alan Blair, 2017-20 COMP9444 ⃝c Alan Blair, 2017-20 COMP9444 20T2 Generative Adversarial […]
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A Reconstruction-Free Projection Selection Procedure for Binary Tomography Using Convolutional Neural Networks Gergely Pap1, Ga ́bor L ́ek ́o2(B), and Tama ́s Gr ́osz1 1 Department of Computer Algorithms and Artificial Intelligence, University of Szeged, A ́rpa ́d t ́er 2, Szeged 6720, Hungary {papg,groszt}@inf.u-szeged.hu 2 Department of Image Processing and Computer Graphics, University of
2020/8/14 https://www.cse.unsw.edu.au/~cs9444/20T2/quiz/ans/quiz5_answers.html COMP9444 Neural Networks and Deep Learning Quiz 5 (Recurrent Networks) This is an optional quiz to test your understanding of Recurrent Networks from Week 5. 1. Explain the format and method by which input was fed to the NetTalk system, and the target output. Characters were fed to NetTalk using a sliding window
CMIT SUMMER PROJECT 2020 (CHEN/ALPERS): TOMOGRAPHIC RECONSTRUCTION OF POLYGONS USING CONVOLUTIONAL NEURAL NETWORKS SUPERVISOR: DR. A. ALPERS Abstract. I would like you to do something very similar as in [2] (i.e., implement and play around with a convolutional neural network), but apply this to a data set that I have from a rather different application
COMP9444 Neural Networks and Deep Learning What is a Neural Network? COMP9444 ⃝c Alan Blair, 2020 COMP9444 ⃝c Alan Blair, 2020 COMP9444 20t2 Neuroanatomy 2 COMP9444 20t2 Neuroanatomy 3 1b. Neuroanatomy massivelyparalleldistributedprocessormadeupofsimpleprocessing units Why Neural Networks? Sub-Symbolic Processing biologically inspired good learning properties continuous, nonlinear well adapted to certain tasks
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2020/8/14 COMP9444 Exercises 5 Solutions COMP9444 Neural Networks and Deep Learning Term 2, 2020 Solutions to Exercise 5: Hidden Units and Convolution This page was last updated: 06/22/2020 15:47:48 1. Hidden Unit Geometry Consider a fully connected feedforward neural network with 6 inputs, 2 hidden units and 3 outputs, using tanh activation at the hidden
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UNSW ⃝c Alan Blair, 2013-20 UNSW ⃝c Alan Blair, 2013-20 COMP9444 Neural Networks and Deep Learning Course Web Page COMP9444 20T2 Overview 2 COMP9444 20T2 Overview 3 Lecture / Lab Schedule Lectures 1a. Overview https://www.cse.unsw.edu.au/~cs9444/20T2/ https://webcms3.cse.unsw.edu.au/COMP9444/20T2/ Online Lectures (Weeks 1-5, 7-10) ◮ Monday 5pm-7pm and Tuesday 5pm-7pm Students are required to
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2020/8/14 COMP9444 Exercise 4 Solutions COMP9444 Neural Networks and Deep Learning Term 2, 2020 Solutions to Exercises 4: PyTorch This page was last updated: 06/22/2020 14:15:01 Download the zip file Ex4code.zip and unzip it. This will create a directory Ex4code with two very simple PyTorch programs, slope.py and xor.py. 1. Adjusting the Learning Rate and
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2020/8/14 https://www.cse.unsw.edu.au/~cs9444/20T2/quiz/ans/quiz3_answers.html COMP9444 Neural Networks and Deep Learning Quiz 3 (Convolutional Networks) This is an optional quiz to test your understanding of the material from Weeks 3 and 4. 1. Sketch the following activation functions, and write their formula: Sigmoid, Tanh, ReLU. = 1/(1 + exp(- )) = tanh( ) = 0, if ¡Ü 0
2020/8/14 https://www.cse.unsw.edu.au/~cs9444/20T2/quiz/ans/quiz6_answers.html COMP9444 Neural Networks and Deep Learning Quiz 6 (Word Vectors) This is an optional quiz to test your understanding of Word Vectors from Week 5. 1. What are the potential benefits of continuous word representations compared to synonyms or taxonomies? Synonyms, antonyms and taxonomy require human effort, may be incomplete, and force discrete