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

Jul 29, 2022 · post

Ethical Considerations When Designing an NLG System

by Andrew Reed · Blog Series This post serves as Part 4 of a four part blog series on the NLP task of Text Style Transfer. In this post, we expand our modeling efforts to a more challenging dataset and propose a set of custom evaluation metrics specific to our task. Part 1: An Introduction to Text Style Transfer Part 2: Neutralizing Subjectivity Bias with HuggingFace Transformers Part 3: Automated Metrics for Evaluating Text Style Transfer Part 4: Ethical Considerations When Designing an NLG System At last, we’ve made it to the final chapter of this blog series.
...read more
Jul 11, 2022 · post

Automated Metrics for Evaluating Text Style Transfer

by Andrew & Melanie · By Andrew Reed and Melanie Beck Blog Series This post serves as Part 3 of a four part blog series on the NLP task of Text Style Transfer. In this post, we expand our modeling efforts to a more challenging dataset and propose a set of custom evaluation metrics specific to our task. Part 1: An Introduction to Text Style Transfer Part 2: Neutralizing Subjectivity Bias with HuggingFace Transformers Part 3: Automated Metrics for Evaluating Text Style Transfer Part 4: Ethical Considerations When Designing an NLG System In our previous blog post, we took an in-depth look at how to neutralize subjectivity bias in text using HuggingFace transformers.
...read more
May 5, 2022 · post

Neutralizing Subjectivity Bias with HuggingFace Transformers

by Andrew Reed · Blog Series This post serves as Part 2 of a four part blog series on the NLP task of Text Style Transfer. In this post, we expand our modeling efforts to a more challenging dataset and propose a set of custom evaluation metrics specific to our task. Part 1: An Introduction to Text Style Transfer Part 2: Neutralizing Subjectivity Bias with HuggingFace Transformers Part 3: Automated Metrics for Evaluating Text Style Transfer Part 4: Ethical Considerations When Designing an NLG System Subjective language is all around us – product advertisements, social marketing campaigns, personal opinion blogs, political propaganda, and news media, just to name a few examples.
...read more
Mar 22, 2022 · post

An Introduction to Text Style Transfer

by Andrew Reed · Blog Series This post serves as Part 1 of a four part blog series on the NLP task of Text Style Transfer. In this post, we expand our modeling efforts to a more challenging dataset and propose a set of custom evaluation metrics specific to our task. Part 1: An Introduction to Text Style Transfer Part 2: Neutralizing Subjectivity Bias with HuggingFace Transformers Part 3: Automated Metrics for Evaluating Text Style Transfer Part 4: Ethical Considerations When Designing an NLG System Today’s world of natural language processing (NLP) is driven by powerful transformer-based models that can automatically caption images, answer open-ended questions, engage in free dialog, and summarize long-form bodies of text – of course, with varying degrees of success.
...read more
Jan 31, 2022 · post

Why and How Convolutions Work for Video Classification

by Daniel Valdez-Balderas · Video classification is perhaps the simplest and most fundamental of the tasks in the field of video understanding. In this blog post, we’ll take a deep dive into why and how convolutions work for video classification. Our goal is to help the reader develop an intuition about the relationship between space (the image part of video) and time (the sequence part of video), and pave the way to a deep understanding of video classification algorithms.
...read more
Dec 14, 2021 · post

An Introduction to Video Understanding: Capabilities and Applications

by Daniel Valdez Balderas · Video footage constitutes a significant portion of all data in the world. The 30 thousand hours of video uploaded to Youtube every hour is a part of that data; another portion is produced by 770 million surveillance cameras globally. In addition to being plentiful, video data has tremendous capacity to store useful information. Its vastness, richness, and applicability make the understanding of video a key activity within the field of computer vision.
...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

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

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