Blog

Mar 29, 2019 · newsletter

Three exciting developments at TensorFlow Dev Summit 2019

The third annual TensorFlow Dev Summit was held. The entire first day of talks was live-streamed, and videos are available on the TensorFlow YouTube channel. I stayed up late (from the UK) watching a few of the talks, and here’s an entirely idiosyncratic description of three things I find exciting. A broader recap is provided by the TensorFlow team themselves.

TensorFlow Federated

TensorFlow Federated is a library for federated learning. Given the title of our last report, you already know we think this is an exciting area. It was a group of Google researchers who coined the term “federated learning” with the release of the Federated Averaging algorithm for learning from decentralized data, so it makes sense that Federated Learning is getting the TensorFlow treatment.

The library includes both the high-level “Federated Learning” API designed for wrapping existing TensorFlow models, and a low-level “Federated Core” API, which is intended to the development of new federated learning algorithms. The project is nascent, but particular attention has been paid to separating the various concerns of building a federated learning system. For instance, without some thought, it would easy to start entangling model operations and network communication. By providing high level abstractions, TensorFlow Federated allows experts in each area to contribute their respective knowledge, which could substantially lower the bar to entry for participating in federated learning research. The ability to wrap an existing Keras model is particularly appealing.

TensorFlow Federated currently includes only a simulated runtime (where all federated nodes actually live on the same, local, device), so it does not out-of-the-box “solve” federated learning. Eventually, other runtimes will be supported with the same API, meaning that the gap between researching novel federated approaches and deploying them to real federated networks is minimised.

TensorFlow Probability

Likewise to federated learning, probabilistic programming is also something we’ve written about at Fast Forward Labs. Since our probabilistic programming report was released, the ecosystem has evolved. PyMC and Stan both continue to develop, but perhaps the most notable change has been the emergence of so-called “deep probabilistic programming” libraries. These libraries combine the ability of deep neural networks to learn complex functions with the probabilistic paradigm of computing with distributions. Pyro.ai runs atop PyTorch, and until recently, Edward was the go-to library for TensorFlow.

TF Probability takes a broader approach to probabilistic programming than Edward, introducing layers of abstraction into the “probprog” stack. At its base, it has complete interoperability with TensorFlow, which means all of TF’s linear algebra operations are available. On top of this is a set of “statistical building blocks” (their language): probability distributions and bijectors (composable transformations of random variables). There are also model building tools, including probabilistic programming language Edward2 - unsurprisingly, the successor to Edward - and a “probabilistic layers” component that can be used with high level APIs like Keras. Finally, there are a suite of probabilistic inference methods including Monte Carlo and Variational Inference.

The library has been evolving rapidly over the last year, but seems to be converging to a useful suite of tools. The TF Dev Summit demo of TensorFlow Probability included a simple and compelling walk through of building a regression model then including aleatoric (observation noise) and epistemic (model) uncertainty. It is encouraging to see probabilistic programming continue to mature and gain traction with existing software ecosystems.

Swift for TensorFlow

Anyone at CFFL will tell you this particular newsletter author has a bit of a soft spot for probabilistic programming. Despite developments in TF Probability, of all the developments announced at the TF Dev Summit, I find the progress in Swift for TensorFlow (which has moved into beta) the most promising.

Swift is an open source language from Apple, first designed to provide a better development experience for iOS and OSX (but now also supporting Linux, less a few niceties like automatic test discovery). Chris Lattner, who implemented much of the Swift language while at Apple, is now working on bringing TensorFlow to Swift. However, this is not a simple wrapper around the existing TensorFlow codebase, but rather a re-imagining of what tools for machine learning can be. Swift for TensorFlow integrates automatic differentiation into the Swift language itself, allowing easy definition of performant custom operations. Swift is fast, expressive and has an advanced type system. At CFFL, we love Python, and the Swift for TensorFlow team have also provided an almost native experience for interoperability with Python.

The watch words are “differentiable computing,” and we eagerly await more demo applications in this ecosystem. One advantage to Swift, being designed as a replacement for C family languages is that it is not necessary to wrap C libraries for performant numerical code. This means one can move up and down the numerical computing stack in the same language, which is also great for pedagogy. Speaking of pedagogy, Jeremy Howard of Fast.ai has noticed Swift too, and at the TF Dev Summit, it was announced that the next iteration of the Fast.ai course will include a Swift component from Chris Lattner himself.

Read more

Newer
Apr 2, 2019 · featured post
Older
Mar 20, 2019 · featured post

Latest posts

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.
...read more
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.
...read more
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.
...read more
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.
...read more
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.
...read more
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.
...read more

Popular posts

Oct 30, 2019 · newsletter
Exciting Applications of Graph Neural Networks
Nov 14, 2018 · post
Federated learning: distributed machine learning with data locality and privacy
Apr 10, 2018 · post
PyTorch for Recommenders 101
Oct 4, 2017 · post
First Look: Using Three.js for 2D Data Visualization
Aug 22, 2016 · whitepaper
Under the Hood of the Variational Autoencoder (in Prose and Code)
Feb 24, 2016 · post
"Hello world" in Keras (or, Scikit-learn versus Keras)

Reports

In-depth guides to specific machine learning capabilities
FF24

Text Style Transfer

The NLP task of text style transfer (TST) aims to automatically control the style attributes of a piece of text while preserving the content, which is an important consideration for making NLP more user-centric. In this report, we explore text style transfer through an applied use case — neutralizing subjectivity bias in free text. Along the way, we describe our sequence-to-sequence modeling approach leveraging HuggingFace Transformers, and present a set of custom, reference-free evaluation metrics for quantifying model performance. Finally, we conclude with a discussion of ethics centered around our prototype: Exploring Intelligent Writing Assistance.
Read the report  →
FF22

Inferring Concept Drift Without Labeled Data

Concept drift occurs when the statistical properties of a target domain change overtime causing model performance to degrade. Drift detection is generally achieved by monitoring a performance metric of interest and triggering a retraining pipeline when that metric falls below some designated threshold. However, this approach assumes ample labeled data is available at prediction time - an unrealistic constraint for many production systems. In this report, we explore various approaches for dealing with concept drift when labeled data is not readily accessible.
Read the report  →
FF19

Session-based Recommender Systems

Being able to recommend an item of interest to a user (based on their past preferences) is a highly relevant problem in practice. A key trend over the past few years has been session-based recommendation algorithms that provide recommendations solely based on a user’s interactions in an ongoing session, and which do not require the existence of user profiles or their entire historical preferences. This report explores a simple, yet powerful, NLP-based approach (word2vec) to recommend a next item to a user. While NLP-based approaches are generally employed for linguistic tasks, here we exploit them to learn the structure induced by a user’s behavior or an item’s nature.
Read the report  →
FF18

Few-Shot Text Classification

Text classification can be used for sentiment analysis, topic assignment, document identification, article recommendation, and more. While dozens of techniques now exist for this fundamental task, many of them require massive amounts of labeled data in order to be useful. Collecting annotations for your use case is typically one of the most costly parts of any machine learning application. In this report, we explore how latent text embeddings can be used with few (or even zero) training examples and provide insights into best practices for implementing this method.
Read the report  →

Prototypes

Machine learning prototypes and interactive notebooks
Notebook

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
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

Cloudera Fast Forward Labs

Making the recently possible useful.

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.

Cloudera   Blog   Twitter

©2022 Cloudera, Inc. All rights reserved.