STATS 326 Applied Time Series ASSIGNMENT FOUR Due: 16 May 2019, 11.00 am (Worth 6% of your final grade) Hand-in to the appropriate STATS 326 Hand-in box in the Student Resource Centre This assignment will be marked out of 100. Please follow the instructions carefully. Marks will be deducted if you include R output, plots […]
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Skip navigation  Programming as Problem Solving (including Advanced) [2019 S1] ANU College of Engineering & Computer Science   Programming as Problem Solving (including Advanced) [2019 S1] ANU College of Engineering & Computer Science Search query • Search ANU web, staff & maps • Search  • • COMP1100/1130 [2019 S1] • Lectures • Labs • Assignments
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Searching Trajectories by Locations – An Efficiency Study Zaiben Chen†, Heng Tao Shen†, Xiaofang Zhou†, Yu Zheng‡, Xing Xie‡ † School of Information Technology & Electrical Engineering The University of Queensland, QLD 4072 Australia ‡Microsoft Research Asia, Beijing 100080 China {zaiben, shenht, zxf}@itee.uq.edu.au, {yuzheng, xingx}@microsoft.com ABSTRACT Trajectory search has long been an attractive and challeng-
Name: Final Exam FE621 • Please note: for the problems using data we are not interested in all the minor steps performed when analyzing the data. However, you should explain your broad reasoning and hand in a final report. • Do not include any failed attempts at modeling just to give volume to the final
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Assignment • Implementation, in simulation, of a virtual memory system based on demand paging • It accepts virtual addresses along with access type (read or write) and outputs the content of corresponding physical addresses or an error • Features: • Physical memory is simulated by an array of bytes (char data type) • Page table
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STATS 326 Applied Time Series ASSIGNMENT FOUR Due: 16 May 2019, 11.00 am (Worth 6% of your final grade) Hand-in to the appropriate STATS 326 Hand-in box in the Student Resource Centre This assignment will be marked out of 100. Please follow the instructions carefully. Marks will be deducted if you include R output, plots
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Switching Convolutional Neural Network for Crowd Counting Deepak Babu Sam∗ Shiv Surya∗ R. Venkatesh Babu Indian Institute of Science Bangalore, INDIA 560012 bsdeepak@grads.cds.iisc.ac.in, shiv.surya314@gmail.com, venky@cds.iisc.ac.in Abstract We propose a novel crowd counting model that maps a given crowd scene to its density. Crowd analysis is com- pounded by myriad of factors like inter-occlusion between people
Final Exam FE621 Posted May 10, 2019, Due May 17, 2019 at 23:55 Name: • Please note: for the problems using data we are not interested in all the minor steps performed when analyzing the data. However, you should explain your broad reasoning and hand in a final report. • Do not include any failed
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Name: Final Exam FE621 • Please note: for the problems using data we are not interested in all the minor steps performed when analyzing the data. However, you should explain your broad reasoning and hand in a final report. • Do not include any failed attempts at modeling just to give volume to the final
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Homework 7: Computation Graphs, Backpropagation, and Neural Networks Instructions: Your answers to the questions below, including plots and mathematical work, should be submitted as a single PDF file. It’s preferred that you write your answers using software that typesets mathematics (e.g. LATEX, LYX, or MathJax via iPython), though if you need to you may scan