Deep Learning Question-Answer Model with Demo
  How amazing would be a model that can answer questions from any paragraph by extracting word(s) from the paragraph that are most relevant. Deep learning has made this possible! See below a demo for such a question-answer super model. In this demo you can put in any context up to 300 words... Read more
Why are Convnets Often Better Than the Rest? Part II
Following on Part I of “Why are Convnets often Better than the Rest?”, we will now look at a traditional neural network’s weakness. As before, I am going to assume you understand the basics of neural network modeling already, including how images are applied to input layers. Through the... Read more
Lessons Learned and Reinforced from Writing My Own Deep Learning Package
At work, I’ve been rolling my own deep learning package to experiment with graph convolutional neural networks. I did this because in graph-centric deep learning, an idea I picked up from this paper, the inputs, convolution kernels, and much more, are being actively developed, and the standard APIs don’t... Read more
Why are Convnets Often Better Than the Rest? Part I
Introduction In this series, I will explore convolutional neural networks in comparison to standard neural networks. To begin with, the former is an evolution of the latter. Through analyzing this evolution, it is fascinating to see how particular design differences have such a great impact on performance and overall... Read more
Reel Reviews: Neural Networks for Sentiment Analysis
This is a joint article authored in collaboration between Kannan Sankaran and Win Suen. The Problem Over the past few years, there has been burgeoning interest in neural networks from data science and engineering communities. The advent of ever larger datasets, efficient commodity hardware, and powerful open source libraries... Read more
The History of Neural Networks and AI: Part III
This article is the third and final article in a three-part series about the history of neural networks and artificial intelligence. To view the first article that dives into the earliest developments of artificial intelligence, click here. For a better picture of how neural networks and artificial intelligence technologies... Read more
The History of Neural Networks and AI: Part II
This article is the second article in a three-part series about the history of neural networks and artificial intelligence. To view the first article, click here. After the beginning era of AI, a British researcher specializing in artificial intelligence, Donald Michie, designed a machine made from matchboxes in 1963... Read more
An Overview of Proxy-label Approaches for Semi-supervised Learning
Note: Parts of this post are based on my ACL 2018 paper Strong Baselines for Neural Semi-supervised Learning under Domain Shift with Barbara Plank. Table of contents: Self-training Multi-view training Co-training Democratic Co-learning Tri-training Tri-training with disagreement Asymmetric tri-training Multi-task tri-training Self-ensembling Ladder networks Virtual Adversarial Training ΠΠ model Temporal Ensembling Mean Teacher... Read more
Datasets Are Books, Not Houses
What’s content addressing? What does it have to do with datasets? Why am I on this site in the first place? Read on, dear reader. Read on. The world of linked data is built on shaky foundations that prevent a true data commons from emerging. The problem isn’t with... Read more
The History of Neural Networks and AI: Part I
Although machine learning has only become mainstream in the last decade, there are many essential contributors to the field dating back as far as the 1940s. In order to understand the infinite possibilities presented today in the fields of AI, deep learning and more, it is important to understand... Read more
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