Introduction

• Introduces fastText, a simple and highly efficient approach for text classification.
• At par with deep learning models in terms of accuracy though an order of magnitude faster in performance.
• Link to the paper
• Link to code

Architecture

• Built on top of linear models with a rank constraint and a fast loss approximation.
• Start with word representations that are averaged into text representation and feed them to a linear classifier.
• Think of text representation as a hidden s...

Why this paper?

First scalable successful combination of reinforcement learning and deep learning. Result outperforms preceding approaches (at Atari games). Only uses pixel data + game scores + number of actions and the same architecture across different games.Why reinforcement learning? That is how animals and humans seem to make decisions in their environments as evidenced by parallels seen in neural data of neurons and temporal difference RL algorithms.

What about previous approaches?

Handcrafted features. When non-linear approximations of Q are used, values are unstable. Other stable neural nets approaches were there, like Q-iteration. They are slow, though - don’t work for large networks.

What are the outcomes?

Tested the method against best performing approaches at the time and a professional game tester. Used 49 different Atari g...

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.
• Consists of ~75K movie entities and ~3.5M training examples.

Tasks