Visual Question Answering Demo in Python Notebook

This is an online demo with explanation and tutorial on Visual Question Answering. This is not a naive or hello-world model, this model returns close to state-of-the-art without using any attention models, memory networks (other than LSTM) and fine-tuning, which are essential recipe for current best results.

I have tried to explain different parts, and reasoning behind their choices. This is meant to be an interactive tutorial, feel free to change the model parameters and experiment. If you have latest graphics card execution time should be within a minute.

All the files required to run this ipython notebook can be obtained from

* Jupyter Notebook on Github </p>

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Dokuwiki for Personal Notebook and Note taking

As a researcher, you soon start wondering if you had centralized all your notes, possibly digitized them, life would be much better. Recently when I had to make a tough choice of leaving all my notes from years when I am about to shift country (due to limited air travel baggage), I wish I had them on computer. Since I will be making more notes in the future, at least a lesson is learnt.

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Solutions to Hamilton-Jacobi-Bellman under uncertainity

After doing some reading on decision under un-certainity, I get the feeling that this I will be looking more into this. More so because I have the feeling like there is more to this field, lot of unknowns yet(which is still partly due to my lack of profound knowledge in the field). I feel this field is yet to mature.

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Pascal’s Triangle in Standard ML

It has been a while that I posted something (grad school applications !). For past few weeks I have been learning Standard ML (SML), my first foray into functional programming language. I must say, I was skeptical at first due to ‘no-state’ concept but it is turning out to be great experience. Recursion can only be appreciated when you have to write programs without loop. This makes me rethink about learning Scala and Haskell.

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Monte Carlo Simulation in R

While the last post dealt with Bootstrap Sampling, no sampling discussion can be complete without discussion ‘Monte Carlo’ simulation. Readers please note, I will *not **discuss “MCMC (Markov Chain Monte Carlo)” *(perhaps in the future). MCMC primarily deals with distribution of equilibrium of the given Markov Chain.

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Bootstrap sampling in R

Bootstrapping is a very useful sampling method. While it’s robustness is not that simlar to MCMC or Metropolis-Hastings or Landau. Bootstrapping draws from provided distribution with replacement.

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Why Simulated Annealing works

Of all optimization methods, Simulated Annealing is one of the most fascinating one. If you need a quick refresher in Simulated Annealing then see this slide. Is Simulated Annealing better than other techniques in finding the global optima ? Perhaps. I will discuss why I think it is one of the best optimization technique and why so.

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When in Pain: Read

Human life with all its color and variety has one thing in common, feeling of joy and sadness. No amount of wealth or luck can save you from either. Different people have different means to cope with tumultuous rides of emotion.  While everyone has good handle on joyous days, the gloomy days elapse in a lot of pain.

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Paradigm towards shared Branch Banking

Long time ago when I was in my native town I had to do some banking operations on my account. To my surprise I found that the place didn’t have a branch of the bank where I had my account.

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