'Good algorithm' presentation slideshows

Spring 2014 Program Analysis and Verification Lecture 1: Introduction

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Introduction

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11. CLAIMS Project Status report by the International Bureau

Committee of Experts Thirty-Fifth session Geneva, October 25 to 29, 2004. 11. CLAIMS Project Status report by the International Bureau. World Intellectual Property Organization. CLAIMS Project update. Agenda. 1. Categorization assistance. 2. Natural language search in the IPC.

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Section 4: Parallel Algorithms

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Introduction

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Javier Junquera

Molecular dynamics in the microcanonical (NVE) ensemble: the Verlet algorithm. Javier Junquera. Equations of motion for atomic systems in cartesian coordinates. Classical equation of motion for a system of molecules interacting via a potential.

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CSE332: Data Abstractions Lecture 20 : Analysis of Fork-Join Parallel Programs

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Original Work by: Dan Grossman Converted to C++/OMP by: Bob Chesebrough Last Updated: Jan 2012

A Sophomoric Introduction to Shared-Memory Parallelism and Concurrency Lecture 2 Analysis of Fork-Join Parallel Programs. Original Work by: Dan Grossman Converted to C++/OMP by: Bob Chesebrough Last Updated: Jan 2012

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Large-scale Deep Unsupervised Learning using Graphics Processors

Large-scale Deep Unsupervised Learning using Graphics Processors. Rajat Raina Anand Madhavan Andrew Y. Ng Stanford University. Learning from unlabeled data. Classify. vs. car. motorcycle. Learn higher-level representation. Deep Belief Networks Sparse Coding. Input space.

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CSE332: Data Abstractions Lecture 19: Analysis of Fork-Join Parallel Programs

CSE332: Data Abstractions Lecture 19: Analysis of Fork-Join Parallel Programs. Dan Grossman Spring 2010. Where are we. Done: How to use fork , and join to write a parallel algorithm Why using divide-and-conquer with lots of small tasks is best Combines results in parallel

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Class 6: Programming with Data

Class 6: Programming with Data. David Evans cs1120 Fall 2009. Ways to Design Programs. Think about what you want to do , and turn that into code. Think about what you need to represent , and design your code around that. Which is better?. History of Scheme.

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Sorting and Efficiency

Sorting and Efficiency. Eric Roberts CS 106B January 30, 2013. Sorting.

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Algorithms

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Active Learning

Active Learning. Lecture 26th. Maria Florina Balcan. Active Learning. Data Source. Expert / Oracle. Unlabeled examples. Learning Algorithm. Request for the Label of an Example. A Label for that Example. Request for the Label of an Example. A Label for that Example.

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Tutorial 4

Tutorial 4. Association rule mining. Goal: Find all rules that satisfy the user-specified minimum support ( minsup ) and minimum confidence ( minconf ). Assume all data are categorical. No good algorithm for numeric data.

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Indoor Positioning

Indoor Positioning. Kalid Azad Advisor: Prof. Littman (MAE dept) Co-advisor: Prof. Cook Cs398 Project Proposal. Problem Description . What is indoor positioning? Find your location accurately indoors (like GPS) Why is it important? Unsolved problem

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Algorithms: The recipe for computation

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