Posts
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Eight Lessons Learned in Two Years of Ph.D.
When I started my program two years ago, I started a habit that proved to be quite beneficial: I dedicated a page in my research notebook titled Lessons Learned. I made sure to update this page on a semi-regular basis, typically whenever I learn something from my mentors/advisors, reflect on my progress, or come across an “Aha” moment about something that I should have done differently. This blog post elaborates and expands on some of the key entries on that notebook page. As it is almost the end of the summer (sad, I know), I hope this article is timely for the many students who are starting their Ph.D. in the fall. You will find that the first four lessons in this article are high-level, more abstract, and related to the way we should view ourselves as Ph.D. students and our research. The last four lessons include more practical advice that you can adopt on a day-to-day basis.
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A Distributional Approach to Controlled Text Generation
I and my co-authors recently wrote a blogpost on our ICLR 2021 paper “A Distributional Approach to Controlled Text Generation”…
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ACL 2020: My Highlights
ACL 2020 has been very special, since it is my first conference to attend. I have found the virtual version to be very nice (Altough my testimony is a bit undermined by the fact that I have not experienced an actual conference before. So, I cannot really compare the virtual version to the actual one). Anyway, I found the discussions, Q&A, chat rooms, and the live talks were very engaging and interesting!
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Current Issues with Transfer Learning in NLP
Natural Language Processing (NLP) has recently witnessed dramatic progress with state-of-the-art results being published every few days. Leaderboard madness is diriving the most common NLP benchmarks such as GLUE and SUPERGLUE with scores that are getting closer and closer to human-level performance. Most of these results are driven by transfer learning from large scale datasets through super large (Billions of parameters) models. My aim in this article is to point out the issues and challenges facing transfer learning and point out some possible solutions to such problems.
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Lightweight and Dynamic Convolutions Explained
Self-attention models suffer from quadratic time complexity in terms of the the input size. We discuss a paper that proposes a variant of the convolution operation named Lightweight Convolutions that scales linearly with the input size while performaing comparably with state-of-the-art self-attention models.
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Paper Discussion: Discrete Generative Models for Sentence Compression
I will discuss the 2016 paper Language as a Latent Variable: Discrete Generative Models for Sentence Compression. The reason why I chose this paper is two-fold : First, it combines a lot of important ideas and concepts such as Variational Auto Encoders, Semi-Supervised learning and Reinforcement Learning.
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Step-by-Step Text Classification Tutorial using Tensorflow
In this post, I will walk you through using Tensorflow to classify news articles. Before you begin, you should have tensorflow, numpy and scikit-learn installed.
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Predicting Movie Genre from Movie Title
In this post we will attempt at the interesting classification problem : Predicting a movie genre from only its title. It would be very interesting to be able to make such prediction. It can be used to cluster movies based on genre. Plus it’s a great way to explore various classification problems and the very famous word embeddings as well.