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1

Geoffrey Hinton on max pooling (reddit AMA) « Mirror Image

https://mirror2image.wordpress.com/2014/11/11/geoffrey-hinton-on-max-pooling-reddit-ama

Mostly AR and Stuff. Geoffrey Hinton on max pooling (reddit AMA). Geoffrey Hinton, neural networks, ML, deep learning pioneer answered “Ask Me Anything” on reddit:. Https:/ www.reddit.com/r/MachineLearning/comments/2lmo0l/ama geoffrey hinton/. Https:/ www.reddit.com/user/geoffhinton. His most controversial answer is:. The pooling operation used in convolutional neural networks is a big mistake and the fact that it works so well is a disaster. Understanding Locally Competitive Networks). Getting it from i...

2

Finds in arxiv, January. « Mirror Image

https://mirror2image.wordpress.com/2014/02/01/finds-in-arxiv-january

Mostly AR and Stuff. Finds in arxiv, January. Convex Relaxations of SE(2) and SE(3) for Visual Pose Estimation. Finding camera position from the series of 2D (or depth) images is one of the most common (and difficult) task of computer vision. The biggest problem here is incorporating rotation into cost(energy) function, that should minimize reprojection(fro 2D) error (see http:/ en.wikipedia.org/wiki/Bundle adjustment. Http:/ arxiv.org/abs/1401.3700. Background removal without nuclear norm. Some &#82...

3

2014 February 01 « Mirror Image

https://mirror2image.wordpress.com/2014/02/01

Mostly AR and Stuff. Finds in arxiv, January. Convex Relaxations of SE(2) and SE(3) for Visual Pose Estimation. Finding camera position from the series of 2D (or depth) images is one of the most common (and difficult) task of computer vision. The biggest problem here is incorporating rotation into cost(energy) function, that should minimize reprojection(fro 2D) error (see http:/ en.wikipedia.org/wiki/Bundle adjustment. Http:/ arxiv.org/abs/1401.3700. Background removal without nuclear norm. Some &#82...

4

Some simple ways to speed up convnet a little « Mirror Image

https://mirror2image.wordpress.com/2014/12/28/some-simple-ways-to-speed-up-convnet-a-little

Mostly AR and Stuff. Some simple ways to speed up convnet a little. There are quite a few complex methods for making convolutional networks converge faster. Natural gradient, dual coordinate ascent, second order hessian free methods and more. However those methods are usually require considerable memory spending and extensive modifications of existing code if you already had some working method before. Here instead I’ll list some. Resulting acceleration is small but still visible (on order of 10%). 2 Ran...

5

2014 December 14 « Mirror Image

https://mirror2image.wordpress.com/2014/12/14

Mostly AR and Stuff. Some thoughts about deep learning criticism. There is some deep learning (specifically #convnet) criticism based on the artificially constructed misclassification examples. There is a new paper. 8220;Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images” by Nguen et al. Http:/ arxiv.org/abs/1412.1897. And other direction of critics is based on the older, widely cited paper. 8220;Intriguing properties of neural networks” by Szegedy et al. In fac...

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Where to wait for an elevator

http://www.johndcook.com/blog/2010/11/29/where-to-wait-for-an-elevator

John D. Cook. Where to wait for an elevator. Imagine a bank of three elevators along a wall. The elevators are in a straight line but they are not evenly spaced. Where do you stand in order to minimize the expected distance you’ll need to walk to catch the first elevator that arrives? This scenario comes from the opening paragraph of a paper. Distance and that the minimal average distance to the elevator is in fact achieved by standing at the median. This problem illustrates three optimization theorems:.

developerstation.org developerstation.org

DeveloperStation.ORG: About Me

http://www.developerstation.org/p/rahul-kavi-my-reserach-page.html

Video Lectures and Talks. I'm a grad student. I write on DeveloperStation. This blog) to keep a "dairy" of some sort to look-up for myself in the future and to help others in the process. I always like to keep a track of present trends in Technology, Programming. You can follow me on Google Plus. My research publications are listed on my Google Scholar page. Paulo Coelho: I have learned. My Amazon Wish List. Subscribe to: Posts (Atom). Fav Networking, Linux, Computer Vision Blogs.

developerstation.org developerstation.org

DeveloperStation.ORG: very basic intro to Caffe

http://www.developerstation.org/2015/08/intro-to-caffe-tutorial.html

Video Lectures and Talks. Monday, August 10, 2015. Very basic intro to Caffe. This tutorial just gives you a basic idea of what Caffe can do (not for advanced use of Caffe through defining your own layers, using C /Python API) for defining your own network to solve a Computer Vision task. I'm assuming you already have a background in Machine Learning, Statistical Pattern Recognition and have built Neural Networks in the past using some programming language. Create train.txt and test.txt with file...You t...

developerstation.org developerstation.org

DeveloperStation.ORG: February 2014

http://www.developerstation.org/2014_02_01_archive.html

Video Lectures and Talks. Wednesday, February 5, 2014. Fun with my Raspberry Pi. I ordered a Raspberry Pi recently. I've heard so much about this device in the recent past, I decide to try it out. I was reminded of few microprocessor(fun class) and electrical engineering(not so fun time) classes that I took in my undergrad. I wrote a simple program that could turn a Stepper Motor using a motor controller. Connected to a Raspberry Pi. HDMI cable to connect to a TV or monitor. For powering the stepper motor.

developerstation.org developerstation.org

DeveloperStation.ORG: simple Convolutional Neural Net based object recognition

http://www.developerstation.org/2016/03/simple-convolutional-neural-net-based.html

Video Lectures and Talks. Wednesday, March 9, 2016. Simple Convolutional Neural Net based object recognition. I made a simple object recognition module over the last weekend. I wrote a Theano. Based convolutional neural network. I wrote a simple OpenCV. Based image segmentation program. The output from the image segmentation program is passed to the Theano based Convolutional Neural Network. I used 10,000 images for training and 16,000 images for testing. Posted by Rahul Kavi. On the way to Virtual World.

developerstation.org developerstation.org

DeveloperStation.ORG: June 2013

http://www.developerstation.org/2013_06_01_archive.html

Video Lectures and Talks. Sunday, June 9, 2013. Logisitic Regression in OpenCV. I finished writing a Logistic Regression classifier in OpenCV. I've seen a lot of posts on the web asking for OpenCV's version of the same but its not available. Its easy to write your own logistic regression classifier. I separated out the cost function and gradient descent algorithm (Batch Gradient Descent). You can replace it with your own optimization algorithm. I posted it on my Github page. You can check it out:.

developerstation.org developerstation.org

DeveloperStation.ORG: August 2015

http://www.developerstation.org/2015_08_01_archive.html

Video Lectures and Talks. Monday, August 10, 2015. Very basic intro to Caffe. This tutorial just gives you a basic idea of what Caffe can do (not for advanced use of Caffe through defining your own layers, using C /Python API) for defining your own network to solve a Computer Vision task. I'm assuming you already have a background in Machine Learning, Statistical Pattern Recognition and have built Neural Networks in the past using some programming language. Create train.txt and test.txt with file...You t...

developerstation.org developerstation.org

DeveloperStation.ORG: March 2016

http://www.developerstation.org/2016_03_01_archive.html

Video Lectures and Talks. Sunday, March 13, 2016. Simple Autoencoder on MNIST dataset. So, I had fun with Theano and trained an Autoencoder on a MNIST dataset. More about Autoencoders is available here. More variants of Autoencoders exist (Sparse, Contractive, etc.) are available with different constraints on the hidden layer representation. I trained an vanilla Autoencoder for 100 epochs with 16 mini batch size and learning rate of 0.01. Posted by Rahul Kavi. Wednesday, March 9, 2016. The output from th...

developerstation.org developerstation.org

DeveloperStation.ORG: December 2012

http://www.developerstation.org/2012_12_01_archive.html

Video Lectures and Talks. Sunday, December 23, 2012. Review: Mastering OpenCV with Practical Computer Vision Projects. I got hold of this new book titled " Mastering OpenCV with Practical Computer Vision Projects. I personally liked the part on Point Cloud Library and OpenCV. I liked the Chapter 9 where they discussed Kinect and OpenCV. This chapter was provided as a digital download (as a link). Before trying this book. Posted by Rahul Kavi. Monday, December 17, 2012. Neural Networks in Octave. Review: ...

developerstation.org developerstation.org

DeveloperStation.ORG: Simple Autoencoder on MNIST dataset

http://www.developerstation.org/2016/03/simple-autoencoder-on-mnist-dataset.html

Video Lectures and Talks. Sunday, March 13, 2016. Simple Autoencoder on MNIST dataset. So, I had fun with Theano and trained an Autoencoder on a MNIST dataset. More about Autoencoders is available here. More variants of Autoencoders exist (Sparse, Contractive, etc.) are available with different constraints on the hidden layer representation. I trained an vanilla Autoencoder for 100 epochs with 16 mini batch size and learning rate of 0.01. Posted by Rahul Kavi. Subscribe to: Post Comments (Atom).

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