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May 31, 2018 · newsletter

Convolve all the things

While deep learning can be applied generally, much of the excitement around it has stemmed from significant breakthroughs in two main areas: computer vision and natural language processing. Practitioners have typically applied convolutional neural networks (CNNs) to spatial data (e.g. images) and recurrent neural networks (RNNs) to sequence data (e.g. text). However, a recent research paper has shown that convolutional neural networks are not only capable of performing well on sequential data tasks, but they have inherent advantages over recurrent networks and may be a better default starting point.

CNNs were designed originally to take advantage of spatial structure in the input data; for example, a pixel in an image is strongly related to nearby pixels. Sequence data also exhibits a “spatial” structure of sorts, where a particular word is strongly related to surrounding words. The observation is not new, though, and CNNs have been successfully applied to tasks involving sequences for decades. These applications have traditionally been things like sentiment or topic classification, where the output has the freedom to inspect every element in the input sequence. Until fairly recently, CNNs were not popular choices for tasks which involve mapping an input sequence to an output sequence (e.g., time series forecasting).

Vanilla CNNs applied to sequence forecasting have two pitfalls - the output incorporates input from both the past and the future, and they struggle to “see” or “remember” events in the distant past. Luckily, there are solutions for these two shortcomings: causal convolutions and dilated convolutions, respectively. A causal convolution adjusts the convolution kernel to only look at data in the past:

while dilated convolutions introduce gaps that allow the output to incorporate information from the distant past:

CNNs that have been modified for use in temporal domains are called temporal convolutional networks or TCNs. One of the main benefits of using TCNs for sequence modeling tasks is that the convolutions can be computed in parallel since the output at a given timestep does not need to wait for previous timesteps. This is in contrast to an RNN, where each prediction must wait for all previous predictions. One potential downside to TCNs is that they do not encode the history of the sequence in a single hidden state like RNNs do, but instead require the entire input sequence to generate predictions.

The authors of the paper present results that show that simple TCNs can beat popular recurrent architectures at sequence modeling tasks that have traditionally only used recurrent networks. While it would be counter-productive to declare a winner, it may be time to question our assumptions and consider TCNs as a first-class citizen for sequence modeling. If you’ve had success with using convolutional networks for time series or sequence modeling, we’d love to hear more about it!

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Latest posts

Nov 15, 2020 · post

Representation Learning 101 for Software Engineers

by Victor Dibia · Figure 1: Overview of representation learning methods. TLDR; Good representations of data (e.g., text, images) are critical for solving many tasks (e.g., search or recommendations). Deep representation learning yields state of the art results when used to create these representations. In this article, we review methods for representation learning and walk through an example using pretrained models. Introduction Deep Neural Networks (DNNs) have become a particularly useful tool in building intelligent systems that simplify cognitive tasks for users.
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Jun 22, 2020 · post

How to Explain HuggingFace BERT for Question Answering NLP Models with TF 2.0

by Victor · Given a question and a passage, the task of Question Answering (QA) focuses on identifying the exact span within the passage that answers the question. Figure 1: In this sample, a BERTbase model gets the answer correct (Achaemenid Persia). Model gradients show that the token “subordinate ..” is impactful in the selection of an answer to the question “Macedonia was under the rule of which country?". This makes sense .. good for BERTbase.
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Jun 16, 2020 · notebook

Evaluating QA: Metrics, Predictions, and the Null Response →

by Melanie · A deep dive into computing QA predictions and when to tell BERT to zip it! In our last post, Building a QA System with BERT on Wikipedia, we used the HuggingFace framework to train BERT on the SQuAD2.0 dataset and built a simple QA system on top of the Wikipedia search engine. This time, we’ll look at how to assess the quality of a BERT-like model for Question Answering.
qa.fastforwardlabs.com
May 19, 2020 · notebook

Building a QA System with BERT on Wikipedia →

by Melanie · So you’ve decided to build a QA system. You want to start with something simple and general so you plan to make it open domain using Wikipedia as a corpus for answering questions. You want to use the best NLP that your compute resources allow (you’re lucky enough to have access to a GPU) so you’re going to focus on the big, flashy Transformer models that are all the rage these days.
qa.fastforwardlabs.com
Apr 28, 2020 · notebook

Intro to Automated Question Answering →

by Melanie · Welcome to the first edition of the Cloudera Fast Forward blog on Natural Language Processing for Question Answering! Throughout this series, we’ll build a Question Answering (QA) system with off-the-shelf algorithms and libraries and blog about our process and what we find along the way. We hope to wind up with a beginning-to-end documentary that provides:
qa.fastforwardlabs.com
Apr 1, 2020 · newsletter

Enterprise Grade ML

by Shioulin · At Cloudera Fast Forward, one of the mechanisms we use to tightly couple machine learning research with application is through application development projects for both internal and external clients. The problems we tackle in these projects are wide ranging and cut across various industries; the end goal is a production system that translates data into business impact. What is Enterprise Grade Machine Learning? Enterprise grade ML, a term mentioned in a paper put forth by Microsoft, refers to ML applications where there is a high level of scrutiny for data handling, model fairness, user privacy, and debuggability.
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Reports

In-depth guides to specific machine learning capabilities

Prototypes

Machine learning prototypes and interactive notebooks
Library

NeuralQA

A usable library for question answering on large datasets.
https://neuralqa.fastforwardlabs.com
Notebook

Explain BERT for Question Answering Models

Tensorflow 2.0 notebook to explain and visualize a HuggingFace BERT for Question Answering model.
https://colab.research.google.com/drive/1tTiOgJ7xvy3sjfiFC9OozbjAX1ho8WN9?usp=sharing
Notebooks

NLP for Question Answering

Ongoing posts and code documenting the process of building a question answering model.
https://qa.fastforwardlabs.com
Notebook

Interpretability Revisited: SHAP and LIME

Explore how to use LIME and SHAP for interpretability.
https://colab.research.google.com/drive/1pjPzsw_uZew-Zcz646JTkRDhF2GkPk0N

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Cloudera Fast Forward is an applied machine learning reseach group.
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