Generating randoms from a specified CDF

This post deals with generating random numbers given a CDF (Cumulative distribution function). CDF may be specified as an analytical function or as a table of values. We also assume that we have a source of pseudo-random uniformly distributed numbers. Probability Integral Transform At the core of this issue is the 'Probability Integral Transform'. It states that, … Continue reading Generating randoms from a specified CDF →

NetVLAD – Supervised Place Recognition

Download PPT - Google Docs Vector of locally aggregated descriptors (VLAD) [1] is a simple and popular technique for computing a fingerprint of an image for place recognition. It basically forms say K=64 clusters of SIFT like descriptors (descriptors at SIFT feature points). Then, for every descriptor subtracts it from cluster center and adds it up. … Continue reading NetVLAD – Supervised Place Recognition →

Recurrent Neural Net: Memo

RNN (Recurrent Neural nets) are used to model sequences. Unlike the usual feedforward nets which are stateless in terms on inputs, RNNs have memory. In particular, its inputs are the output of previous step and also new observation in current step. The basic RNN are notoriously hard to train. LSTM (Long short term memory) networks … Continue reading Recurrent Neural Net: Memo →

Generative Networks : Memo

Ian Goodfellow's one of the popular works is the GAN (Generative Adversarial Networks). These networks basically can generate images (which look like real images). In the coming future, I wish to get into this a bit. Below could be a good start point : a) Tutorial by Goodfellow in NIPS2016 : [Arxiv] [Slides]

Soft Indicator Function

Very often we come across indicator functions denoting class membership. These functions in their native form are neither continuous nor differentiable. I will describe a trick to convert such indicator functions to an approximate continuous and differentiable function. This blog is organized as follows: Describe a computation case with indicator function Trick to convert More remarks … Continue reading Soft Indicator Function →

Reinforcement Learning : Memo

I came across this tutorial series on Reinforcement Learning by Arthur Juliani: [WWW] Fundamentals textbook : Reinforcement Learning: An Introduction - By Richard S. Sutton and Andrew G. Barto Freely available online : https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html Video Tutorial by R. Sutton. OpenAI Gym OpenAI is an research organization for RL. They have a environment called OpenAI-Gym (Python), useful … Continue reading Reinforcement Learning : Memo →

Convolutional Networks

Continuing further with Deep Learning, here I will briefly describe what I learned on convolutional network (CNN). If you understand the basics of a simple 2-layer network (fully connected) and can implement it yourself from scratch you are all set to understand the mighty daddy (ie. CNN). Again it is important to understand that CNN, … Continue reading Convolutional Networks →

Deep Residual Nets with Tensorflow

Git Gist : https://gist.github.com/mpkuse/6f9dcd419effa707422eb2c5097f51b4 Deep Residual Nets (ResNets) from Microsoft Research has become one of the popular deep learning network architecture. Already 800+ citation, given that the paper appeared in 2015. Recently, I ported all my code from Caffe to Tensorflow. While it is lot easier to deal with caffe but I must say, the control you … Continue reading Deep Residual Nets with Tensorflow →

Toy Kernels

There is a whole lot of literature on mapping data onto higher dimensional spaces. Another closely related and popular term is the kernel trick. See top publications of Prof. Bernhard Schölkopf for example. In this short entry, I start with a linearly non-separable data in 2D. I use a mapping to 3D for this data and visualize. … Continue reading Toy Kernels →

Robust Keypoint Point Matching

Came across this interesting paper which does feature matching (SIFT-like features) between images under a probabilistic formulation. The methods starts with all matches as inliers and as iterations progress gets rid of matches. About 120 citations as of May 2017. Jiayi Ma, Ji Zhao, Jinwen Tian, Alan L. Yuille, and Zhuowen Tu. Robust Point Matching via … Continue reading Robust Keypoint Point Matching →