shagunsodhani

result dataset

query	url
query1	url1
query2	url2
query1	url3
query2	url4

revenue dataset

shagunsodhani

This week I read upon GraphX, a distributed graph computation framework that unifies graph-parallel and data-parallel computation. Graph-parallel systems efficiently express iterative algorithms (by exploiting the static graph structure) but do not perform well on operations that require a more general view of the graph like operations that move data out of the graph. Data-parallel systems perform well on such tasks but directly implementing graph algorithms on data-parallel systems is inefficient due to complex joins and excessive data movement. This is the gap that GraphX fills in by allowing the same data to be viewed and operated upon both as a graph and as a table.

Preliminaries

Let G = (V, E) be a graph where V = {1, ..., n} is the set of vertices and E is the set of m directed edges. Each directed edge is a tuple of the form (i, j) ∈ E where i ∈ V is the source vertex and j ∈ V is the target vertex. The vertex p

shagunsodhani

Keybase proof

I hereby claim:

• I am shagunsodhani on github.
• I am shagun (https://keybase.io/shagun) on keybase.
• I have a public key whose fingerprint is 052A 444C 26A2 DFAA 1F4E 9A79 48D9 E5AB AD77 C174

To claim this, I am signing this object:

shagunsodhani

Recurrent Neural Network Regularization

Introduction

• The paper explains how to apply dropout to LSTMs and how it could reduce overfitting in tasks like language modelling, speech recognition, image caption generation and machine translation.
• Link to the paper

Dropout

• Regularisation method that drops out (or temporarily removes) units in a neural network.

shagunsodhani

Evaluating Prerequisite Qualities for Learning End-to-end Dialog Systems

Introduction

• The paper presents a suite of benchmark tasks to evaluate end-to-end dialogue systems such that performing well on the tasks is a necessary (but not sufficient) condition for a fully functional dialogue agent.
• Link to the paper

Dataset

• Created using large-scale real-world sources - OMDB (Open Movie Database), MovieLens and Reddit.

shagunsodhani

Neural Generation of Regular Expressions from Natural Language with Minimal Domain Knowledge

Introduction

• Task of translating natural language queries into regular expressions without using domain specific knowledge.
• Proposes a methodology for collecting a large corpus of regular expressions to natural language pairs.
• Reports performance gain of 19.6% over state-of-the-art models.
• Link to the paper

Architecture

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Query Regression Networks for Machine Comprehension

Introduction

• Machine Comprehension (MC) - given a natural language sentence, answer a natural language question.
• End-To-End MC - can not use language resources like dependency parsers. The only supervision during training is the correct answer.
• Query Regression Network (QRN) - Variant of Recurrent Neural Network (RNN).
• Link to the paper

Related Work

shagunsodhani

Topic

Introduction to Deep Learning with Keras

Description

Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

In the talk, I would introduce Keras and talk about how it can be used to accomplish workflows like image classfication and sequence modelling.

shagunsodhani

# Script to calculate entropy for any column in a file.

from __future__ import print_function

import numpy as np

def entropy(file_path, sep, col_index, col_name):
    '''Method to calculate entropy for any col_index
    in a file where columns are seperated by sep'''
    distribution = np.asarray(list(read_column(file_path, sep, col_index)))

shagunsodhani

Improving Word Representations via Global Context and Multiple Word Prototypes

Introduction

• This paper pre-dated papers like Glove and Word2Vec and proposed an architecture that
  • combines local and global context while learning word embeddings to capture the word semantics.
  • learns multiple embeddings per word to account for homonymy and polysemy.
• Link to the paper

Global Context-Aware Neural Language Model