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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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Nov 15, 2022 · newsletter

CFFL November Newsletter

November 2022 Perhaps November conjures thoughts of holiday feasts and festivities, but for us, it’s the perfect time to chew the fat about machine learning! Make room on your plate for a peek behind the scenes into our current research on harnessing synthetic image generation to improve classification tasks. And, as usual, we reflect on our favorite reads of the month. New Research! In the first half of this year, we focused on natural language processing with our Text Style Transfer blog series.
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Nov 14, 2022 · post

Implementing CycleGAN

by Michael Gallaspy · Introduction This post documents the first part of a research effort to quantify the impact of synthetic data augmentation in training a deep learning model for detecting manufacturing defects on steel surfaces. We chose to generate synthetic data using CycleGAN,1 an architecture involving several networks that jointly learn a mapping between two image domains from unpaired examples (I’ll elaborate below). Research from recent years has demonstrated improvement on tasks like defect detection2 and image segmentation3 by augmenting real image data sets with synthetic data, since deep learning algorithms require massive amounts of data, and data collection can easily become a bottleneck.
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Oct 20, 2022 · newsletter

CFFL October Newsletter

October 2022 We’ve got another action-packed newsletter for October! Highlights this month include the re-release of a classic CFFL research report, an example-heavy tutorial on Dask for distributed ML, and our picks for the best reads of the month. Open Data Science Conference Cloudera Fast Forward Labs will be at ODSC West near San Fransisco on November 1st-3rd, 2022! If you’ll be in the Bay Area, don’t miss Andrew and Melanie who will be presenting our recent research on Neutralizing Subjectivity Bias with HuggingFace Transformers.
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Sep 21, 2022 · newsletter

CFFL September Newsletter

September 2022 Welcome to the September edition of the Cloudera Fast Forward Labs newsletter. This month we’re talking about ethics and we have all kinds of goodies to share including the final installment of our Text Style Transfer series and a couple of offerings from our newest research engineer. Throw in some choice must-reads and an ASR demo, and you’ve got yourself an action-packed newsletter! New Research! Ethical Considerations When Designing an NLG System In the final post of our blog series on Text Style Transfer, we discuss some ethical considerations when working with natural language generation systems, and describe the design of our prototype application: Exploring Intelligent Writing Assistance.
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Sep 8, 2022 · post

Thought experiment: Human-centric machine learning for comic book creation

by Michael Gallaspy · This post has a companion piece: Ethics Sheet for AI-assisted Comic Book Art Generation I want to make a comic book. Actually, I want to make tools for making comic books. See, the problem is, I can’t draw too good. I mean, I’m working on it. Check out these self portraits drawn 6 months apart: Left: “Sad Face”. February 2022. Right: “Eyyyy”. August 2022. But I have a long way to go until my illustrations would be considered professional quality, notwithstanding the time it would take me to develop the many other skills needed for making comic books.
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Aug 18, 2022 · newsletter

CFFL August Newsletter

August 2022 Welcome to the August edition of the Cloudera Fast Forward Labs newsletter. This month we’re thrilled to introduce a new member of the FFL team, share TWO new applied machine learning prototypes we’ve built, and, as always, offer up some intriguing reads. New Research Engineer! If you’re a regular reader of our newsletter, you likely noticed that we’ve been searching for new research engineers to join the Cloudera Fast Forward Labs team.
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ASR with Whisper

Explore the capabilities of OpenAI's Whisper for automatic speech recognition by creating your own voice recordings!
https://colab.research.google.com/github/fastforwardlabs/whisper-openai/blob/master/WhisperDemo.ipynb
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NeuralQA

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

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Cloudera Fast Forward Labs is an applied machine learning research group. Our mission is to empower enterprise data science practitioners to apply emergent academic research to production machine learning use cases in practical and socially responsible ways, while also driving innovation through the Cloudera ecosystem. Our team brings thoughtful, creative, and diverse perspectives to deeply researched work. In this way, we strive to help organizations make the most of their ML investment as well as educate and inspire the broader machine learning and data science community.

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