Harshvardhan Gupta harveyslash

Impersonet- Developing a unique writing style by learning from multiple authors [NLP]

By reading vastly different styles of text (such as fiction and philosopy), it may be possible to come up with a writing style that takes inspiration from both, resulting in a completely unique writing style. This is useful for artificial data generation.

Implementation so far/ideas

Character level neural language model
regularized by GANs
leraning by just raw sampling yeilds poor results, so need custom training algorithm (I have a promising one, but not implemented and tested)

<title>Welcome file</title>

Beatest <--> Open Edx Integration

Open Edx provides key features that can be used as a starting point for Beatest MOOC E-Learning.

Key Features to be reused with (almost) no modification:

1. CMS (Content Management System)

Instructors can be manually registered using django admin panel. Then they have access to create courses. These courses will be published by beatest administrators.

	Hawk:6500
	Dove:6325
	Hawk:6200
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	Hawk:6150
	Dove:5925
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	Dove:5875
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	# This function takes in
	#
	# $a0 as max_size
	# $a1 as row
	# $a2 as col
	# $a3 as 1 or -1
	# 1 is = A
	# -1 is =B

	# it returns v0 = 0 if validation failed , else it returns 1

	#
	# Makefile for CCS Experiment 5 - merge sort
	#

	#
	# Location of the processing programs
	#
	RASM = /home/fac/wrc/bin/rasm
	RLINK = /home/fac/wrc/bin/rlink


	# Makefile for CCS Experiment 5 - merge sort
	#

	#
	# Location of the processing programs
	#
	RASM = /home/fac/wrc/bin/rasm
	RLINK = /home/fac/wrc/bin/rlink

	Text generation models typically learn the distribution of a text and
	used this learned information to generate text themselves. By tuning
	various hyperparameters, it is possible to finetune the tradeoff between
	the deviation of the text from the distribution and the robustness of
	the text.

	I present a way to extend this ability of generative models to learn
	multiple distributions in order to generate a style of text
	that is unique from both distributions.
	I also discuss ways to regularize the presented model using Generative

	\documentclass[12pt]{article}

	\pagestyle{empty}

	\usepackage{tikz}
	\usepackage{comment}
	\usepackage{amsmath}
	\usepackage{booktabs}

	\begin{document}

	<h1 id="beatest----open-edx-integration">Beatest <–> Open Edx Integration</h1>
	<p>Open Edx provides key features that can be used as a starting point for Beatest MOOC E-Learning.</p>
	<h3 id="key-features-to-be-reused-with-almost-no-modification">Key Features to be reused with (almost) no modification:</h3>
	<h4 id="cms-content-management-system">1. CMS (Content Management System)</h4>
	<p>Instructors can be manually registered using django admin panel. Then they have access to create courses. These courses will be published by beatest administrators.</p>
	<h4 id="core-lms-learning-management-system">2. Core LMS (Learning Management System)</h4>
	<p>Core aspects include Assignments, Videos, Discussions, Quizzes. They will be used out of the box as is.</p>
	<h3 id="important-features-to-be-modified-with-significant-changes-1">Important Features to be modified with Significant changes [1]:</h3>
	<h4 id="login-system">1. Login System</h4>
	<p>The current login system will be completely redesigned to work with SSO