I want to talk about technical approaches to mitigating algorithmic bias.

It’s 2019, and the majority of the ML community is finally publicly acknowledging the prevalence and consequences of bias in ML models. For years, dozens of reports by organizations such as ProPublica and the New York Times have been exposing the scale of algorithmic discrimination in criminal risk assessment, predictive policing, credit lending, hiring, and more. …

One of my favorite things about deep reinforcement learning is that, unlike supervised learning, it really, really doesn’t want to work. Throwing a neural net at a computer vision problem might get you 80% of the way there. Throwing a neural net at an RL problem will probably blow something up in front of your face — and it will blow up in a different way each time you try.

A lot of the biggest challenges in RL revolve around two questions: how we interact with the environment effectively (e.g. exploration vs. exploitation, sample efficiency), and how we learn from…

This article is a comprehensive overview of Topic Modeling and its associated techniques.

In natural language understanding (NLU) tasks, there is a hierarchy of lenses through which we can extract meaning — from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics. The process of learning, recognizing, and extracting these topics across a collection of documents is called topic modeling.

In this post, we will explore topic modeling through 4 of the most popular techniques today: LSA, pLSA, LDA, and the newer, deep learning-based…

With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification.

Fortunately, however, the most successful approaches to object detection are currently extensions of image classification models. A few months ago, Google released a new object detection API for Tensorflow. …

GoogLeNet, 2014

Over the past few years, much of the progress in deep learning for computer vision can be boiled down to just a handful of neural network architectures. Setting aside all the math, the code, and the implementation details, I wanted to explore one simple question: how and why do these models work?

At the time of writing, Keras ships with six of these pre-trained models already built into the library:

  • VGG16
  • VGG19
  • ResNet50
  • Inception v3
  • Xception
  • MobileNet

The VGG networks, along with the earlier AlexNet from 2012, follow the now archetypal layout of basic conv nets: a series of convolutional…

Before I started my most recent job at ThinkTopic, the concepts of “functional programming” and “machine learning” belonged to two different worlds entirely. One was a programming paradigm surging in popularity as the world turned towards simplicity, composability, and immutability to maintain complex scaling applications; the other was a tool to teach computers to autocomplete doodles and make music. Where was the overlap?

The more I worked with the two, the more I began realizing that the overlap is both practical and theoretical. Firstly, machine learning is not a stand-alone endeavor; it needs to be rapidly incorporated into complex scaling…

Source: http://nicolesnovelreads.blogspot.com/2015/12/ranking-harry-potter-books-and-films.html

I don’t have anything against plays, per se. I’m just as excited for Harry Potter and the Cursed Child as the next millennial who grew up staring out their window at night, waiting for their letter from Hogwarts, only to get screwed over by what can only be assumed was an incompetent owl delivery service. There’s just something about the magic of the books that seems untouchable — irreplicable.

That being said, in honor of the upcoming play, I’m going to try to recreate a bit of that magic.

Language models are a fundamental aspect of natural language processing (NLP)…

Joyce Xu

Deep learning, RL, NLP, CV, and all that jazz. @DeepMindAI, @sidewalklabs, @Stanford

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