'Gradient descent' presentation slideshows

Neural networks

1.07k views • 91 slides

Inverse Kinematics (part 2)

Inverse Kinematics (part 2). CSE169: Computer Animation Instructor: Steve Rotenberg UCSD, Winter 2004. Forward Kinematics. We will use the vector: to represent the array of M joint DOF values We will also use the vector:

1.32k views • 80 slides

CSci 6971: Image Registration Lecture 8: Registration Components February 6, 2004

CSci 6971: Image Registration Lecture 8: Registration Components February 6, 2004. Prof. Chuck Stewart, RPI Dr. Luis Ibanez, Kitware. Image Metrics. How similar is image A to image B ?. Image Metrics. Does Image B matches Image A better than Image C ? . Image Metrics.

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CMC3_Tahoe, April 27, 2019

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Lecture 1: Deep Neural Networks and Backpropagation Training

Lecture 1: Deep Neural Networks and Backpropagation Training. Reading and Research in Deep Learning James K Baker. 11-364 Deep Learning R&R. Major Project. Read to get the gist. Hands-on Tutorial Books with Sample Code. Background Tasks. Leading Edge Research Papers.

1.01k views • 54 slides

Non-local means: a look at non-local self-similarity of images

Non-local means: a look at non-local self-similarity of images. IT 530, LECTURE NOTES. Partial Differential Equations (PDEs): Heat Equation. Executing several iterations of this PDE on a noisy image is equivalent to convolving the same image with a Gaussian!

788 views • 52 slides

15-381 / 681 Instructors: Fei Fang (This Lecture) and Dave Touretzky feifang@cmu

Artificial Intelligence: Representation and Problem Solving Optimization (1): Optimization and Convex Optimization. 15-381 / 681 Instructors: Fei Fang (This Lecture) and Dave Touretzky feifang@cmu.edu Wean Hall 4126. Logistics. Complete the CMU 'course hours worked' survey. Recap.

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Informed Search Chapter 4 (b)

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Active Random Fields

Active Random Fields. Adrian Barbu. The MAP Estimation Problem. Estimation problem: Given input data y, solve Example: Image denoising Given noisy image y , find denoised image x Issues Modeling : How to approximate ? Computing : How to find x fast?. Noisy image y.

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The free-energy principle : a rough guide to the brain ? K Friston

The free-energy principle : a rough guide to the brain ? K Friston. Computational Modeling of Intelligence 11.03.04.(Fri) Summarized by Joon Shik Kim. Sufficient Statistics.

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What is the Best Multi-Stage Architecture for Object Recognition

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Optimization/Learning on the GPU (supplement figure slides)

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Numerical Integration and Rigid Body Dynamics for Potential Field Planners

Numerical Integration and Rigid Body Dynamics for Potential Field Planners. David Johnson. Recall. Define the world as a potential field Towards goal Away from obstacles. Robot Motion. Move down the potential field to minimum Think about contours Which way has zero change ?

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The delta rule

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CS448f: Image Processing For Photography and Vision

CS448f: Image Processing For Photography and Vision. The Gradient Domain. Image Gradients. We can approximate the derivatives of an image using differences Equivalent to convolution by [-1 1] Note the zero boundary condition. m =. d/dx. dm/dx =. Image Derivatives.

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Introduction to Python

Introduction to Python. Usman Roshan. Python. Interpreter language: the Python interpreter executes each statement one at a time. No compilation or linking is required. Python programs are called scripts which are simple text files. To execute a script we pass it through the interpreter.

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LECTURE 25: DISCRIMINATIVE TRAINING

LECTURE 25: DISCRIMINATIVE TRAINING. Objectives: Bayes Rule Mutual Information Conditional Likelihood Mutual Information Estimation (CMLE) Maximum MI Estimation (MMIE) Minimum Classification Error (MCE)

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Logistic Regression

Logistic Regression. Geoff Hulten. Overview of Logistic Regression. A linear model for classification and probability estimation. Can be very effective when: The problem is linearly separable Or there are a lot of relevant features (10s - 100s of thousands can work)

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Topological Mapping using Visual Landmarks

Topological Mapping using Visual Landmarks. The work is based on the "Team Localization: A Maximum Likelihood Approach" paper. To simplify the problem, assume: Landmarks are directional (In Player-Stage simulation, landmarks are represented as pairs of color boxes.)

231 views • 4 slides