History of AI - Computer Science

A dose of reality 1940s McCulloch & Pitts neurons; Hebb’s learning rule 1950 Turing’s “Computing Machinery and Intelligence” 1954 Georgetown-IBM machine translation experiment

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An Introduction to the Kalman Filter - Computer Science

Welch & Bishop, An Introduction to the Kalman Filter 2 UNC-Chapel Hill, TR 95-041, July 24, 2006 1 T he Discrete Kalman Filter In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-

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A Survey of Augmented Reality - Computer Science

In Presence: Teleoperators and Virtual Environments 6 , 4 (August 1997), 355-385. A Survey of Augmented Reality Ronald T. Azuma Hughes Research Laboratories

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LASzip: lossless compression of LiDAR data

LASzip: lossless compression of LiDAR data Martin Isenburg LAStools http://laszip.org Abstract—Airborne laser scanning technology (LiDAR) makes it easy to collect large amounts of point data that sample the

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A Multi-scale Approach to Nonuniform Diffusion

A Multi-scale Approach to Nonuniform Diffusion TR91-040 September, 1991 Ross T. Whitaker ... the example in Fig. 1a. ... and yet the outline of the shape formed by the overlapping circles is easily distinguishable. This visual boundary includes not only the smooth outline of the

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Real-Time Rendering of Real-World Environments

To appear in Rendering Techniques ’99, Proc. of Eurographics Workshop on Rendering 1 Real-Time Rendering of Real World Environments David K. McAllister, Lars Nyland, Voicu Popescu, Anselmo Lastra, Chris McCue

  World, Environment, Real, Rendering, Rendering of real world environments

SSD: Single Shot MultiBox Detector

SSD: Single Shot MultiBox Detector Wei Liu1, Dragomir Anguelov2, Dumitru Erhan3, Christian Szegedy3, Scott Reed4, Cheng-Yang Fu 1, Alexander C. Berg 1UNC Chapel Hill 2Zoox Inc. 3Google Inc. 4University of Michigan, Ann-Arbor 1wliu@cs.unc.edu, 2drago@zoox.com, 3fdumitru,szegedyg@google.com, 4reedscot@umich.edu, 1fcyfu,abergg@cs.unc.edu Abstract. …

Virtual Memory: Paging - Computer Science

–E.g. 5 levels of tables. • Instead of making tables proportional to size of virtual address space, make them proportional to the size of physical address space. –Virtual address space is growing faster than physical. • Use one entry for each physical page with a hash table –Translation table occupies a very small fraction of physical ...

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A New Approach to Linear Filtering and Prediction Problems

a linear dynamic system (Wiener filter) which accomplishes the prediction, separation, or detection of a random signal.4 ——— 1 This research was supported in part by the U. S. Air Force Office of Scientific Research under Contract AF 49 (638)-382. 2 7212 Bellona Ave. 3 Numbers in brackets designate References at end of paper.

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Stochastic models, estimation, and control

our stochastic models, and Chapter 3 develops both the general concepts and the natural result of static system models. In order to incorporate dynamics into the model, Chapter 4 investigates stochastic processes, concluding with practical linear dynamic system models. The basic form is a …

  Model, Control, And control, Estimation, Stochastic, Stochastic models

FR06/2021 The use of artificial intelligence and machine ...

technique that efficiently learns associations and statistical patterns from a very large dataset. 7. R Sutton, A Barto, Reinforcement Learning: an introduction, MIT Press, 1998. 6 . Chapter 3 – How firms are using AI and ML techniques . ... intelligence and machine learning. - ...

  Introduction, Machine, Statistical, Learning, Machine learning

TensorFlow - Tutorialspoint

TensorFlow — Introduction ... machine learning and deep learning concepts in the easiest manner. It combines the ... nonlinear transformation of input that can be used to generate a statistical model as output. Consider the following steps …

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A Comparison of Two Theories of Learning -- Behaviorism ...

A Comparison of Two Theories of Learning -- Behaviorism and Constructivism as applied to Face-to-Face and Online Learning ... the behaviorists provided for greater use of statistical analysis of experimental results. Their goal ... Skinner’s teaching machine provides a connection to today’s digital world which can be

  Machine, Statistical, Learning

Spatio-Temporal Graph Convolutional Networks: A Deep ...

Classic statistical and machine learning models are two major representatives of data-driven methods. In time-series analysis, autoregressive integrated moving average (ARIMA) and its variants are one of the most consolidated approaches based on classical statistics[Ahmed and Cook, 1979; Williams and Hoel, 2003]. However, this type of model

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Introduction - Deep Learning

Machine Learning and AI CHAPTER 1. INTRODUCTION AI Machine learning Representation learning Deep learning Example: Knowledge bases Example: Logistic regression Example: Shallow Example: autoencoders MLPs Figure 1.4: A Venn diagram showing how deep learning is a kind of representation learning, which is in turn a kind of machine learning, which ...

  Introduction, Machine, Learning, Machine learning

INTRODUCTION MACHINE LEARNING - Stanford University

machine learning. Certainly, many techniques in machine learning derive from the e orts of psychologists to make more precise their theories of animal and human learning through computational models. It seems likely also that the concepts and techniques being explored by researchers in machine learning …

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INTRODUCTION MACHINE LEARNING

machine learning. Certainly, many techniques in machine learning derive from the e orts of psychologists to make more precise their theories of animal and human learning through computational models. It seems likely also that the concepts and techniques being explored by researchers in machine learning may

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arXiv:1406.1078v3 [cs.CL] 3 Sep 2014

3 Statistical Machine Translation In a commonly used statistical machine translation system (SMT), the goal of the system (decoder, specifically) is to find a translation f given a source sentence e, which maximizes p(f je) /p(e jf)p(f); where the first term at the right hand side is called translation model and the latter language model

  Machine, Statistical, Statistical machine

An Introduction to Latent Semantic Analysis

Introduction to Latent Semantic Analysis 2 Abstract Latent Semantic Analysis (LSA) is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text (Landauer and Dumais, 1997). The underlying idea is that the aggregate of all the word

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Machine Learning - Home | Computer Science at UBC

1.1.1 Types of machine learning Machine learning is usually divided into two main types. In the predictive or supervised learning approach, the goal is to learn a mapping from inputs x to outputs y, given a labeled set of input-output pairs D = {(x i,y i)}N i=1. Here D is called the training set, and N is the number of training examples.

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