Archive | Deep Learning

Differentiable Dynamic Programs and SparseMAP Inference

Two exciting NLP papers at ICML 2018! ICML 2018 accepts are out, and I am excited about two papers that I will briefly outline here. I think both papers are phenomenally good and will bring back structured prediction in NLP to modern deep learning architectures. Differentiable Dynamic Programming for Structured Prediction and Attention Arthur Mensch […]

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Everything is a Model

TLDR: I review a recent systems paper from Google, why it is a wake-up call to the industry, and the recipe it provides for nonlinear product thinking. Here, I will be enumerating my main takeaways from a recent paper, “The Case for Learned Index Structures” by Tim Kraska, Alex Beutel, Ed Chi, Jeffrey Dean, and […]

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A Billion Words and The Limits of Language Modeling

In this post, I will talk about Language Models, when (and when not) to use LSTMs for language modeling, and some state of the art results. While I mostly discuss the “Exploring Limits” paper, I’m adding a few things elementary (for some) here for completeness sake. The Exploring Limits paper is not new, but I think it’s a good illustration […]

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Is BackPropagation Necessary?

In the previous post, we saw how the backprop algorithm itself is a bottleneck in training, and how the Synthetic Gradient approach proposed by DeepMind reduces/avoids network locking during training. While very clever, there is something unsettling about the solution. It seems very contrived, and definitely resource intensive.  For example, a simple feed forward network under the […]

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Synthetic Gradients .. Cool or Meh?

Synthetic what now? DeepMind recently published about Synthetic Gradients. This post is about that — what they are, and does it make sense for your average Deep Joe to use it. A Computational Graph is the best data structure to represent deep networks. (D)NN training and inference algorithms are examples of data flow algorithms, and […]

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Gradient Noise Injection Is Not So Strange After All

Yesterday, I wrote about a gradient noise injection result at ICLR 2016, and noted the authors of the paper, despite detailed experimentation, were very wishy washy in their explanation of why it works. Fortunately, my Twitter friends, particularly Tim Vieira and Shubhendu Trivedi, grounded this much better than the authors themselves! Shubhendu pointed out Rong Ge (of MSR) […]

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