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Wednesday, June 24, 2015

Idea of modeling non-linearity as learning distrtibution over function spaces in feedforward neural network

I was thinking about relationship in graphical context of graphical models and feedforward neural network. On one hand, Feedforward Neural Network is a graph of deterministic functions and on the other hand, Graphical models are graph of dependence of random variable which are uncertain. Then I thought what if deterministic non-linearities can be replace with random process which generates functions and shared non-linearity can be inferred.

An interesting idea would be to learn a distribution over function space(which will be used as non-linearity in Feedforward Neural Networks) jointly with backpropagation in an EM like fashion.

To summarize, We want to replace the deterministic function with a learned function by modeling the distribution over function space and inferring the shared non-linearity in neural network.

Sunday, June 7, 2015

Squeezing space with LaTeX

I was trying to find ways  to correct large vertical spaces between paragraphs. After serching a bit on internet, I got the following command along with other options. So I wanted to share it here and for my own future reference.
Remove the spac­ing between para­graphs and have a small para­graph indentation

Make your text block as big as pos­si­ble. The sim­plest way to do that is using the geom­e­try package:
Use a com­pact font such as Times Roman:
Remove space around sec­tion headings.
Beware of enu­mer­ated and item­ized lists. Instead, replace them with com­pact lists.
\item ...
\item ...
If you are allowed, switch­ing to dou­ble col­umn can save heaps of space.
If the rules say 12pt, you can usu­ally get away with 11.5pt with­out any­one noticing:
When you get des­per­ate, you can squeeze the inter-​​line spac­ing using
There is also a savetrees pack­age which does a lot of squeez­ing, but the results don’t always look nice, so it is bet­ter to try one or more of the above tricks instead.