Blog

Jan 18, 2017 · post

New Research on Probabilistic Programming

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We’re excited to release the latest research from our machine intelligence R&D team! 

This report and prototype explore probabilistic programming, an emerging programming paradigm that makes it easier to construct and fit Bayesian inference models in code. It’s advanced statistics, simplified for data scientists looking to build models fast.

Bayesian inference has been popular in scientific research for a long time. The statistical technique allows us to encode expert knowledge into a model by stating prior beliefs about what we think our data looks like. These prior beliefs are then updated in light of new data, providing not one prediction, but a full distribution of likely answers with baked-in confidence rates. This allows us to asses the risk of our decisions with more nuance.

Bayesian methods lack widespread commercial use because they’re tough to implement. But probabilistic programming reduces what used to take months of thorny statistical sampling into an afternoon of work.

This will further expand the utility of machine learning. Bayesian models aren’t black boxes, a criterion for regulated industries like healthcare. Unlike deep learning networks, they don’t require large, clean data sets or large amounts of GPU processing power to deliver results. And they bridge human knowledge with data, which may lead to breakthroughs in areas as diverse as anomaly detection and music analysis. 

Our work on probabilistic programming includes two prototypes and a report that teaches you:

  • How Bayesian inference works and where it’s useful
  • Why probabilistic programming is becoming possible now
  • When to use probabilistic programming and what the code looks like
  • What tools and languages exist today and how they compare
  • Which vendors offer probabilistic programming products

Finally, as in all our research, we predict where this technology is going, and applications for which it will be useful in the next couple of years.

Probabilistic Real Estate Prototype

One powerful feature of probabilistic programming is the ability to build hierarchical models, which allow us to group observations together and learn from their similarities. This is practical in contexts like user segmentation: individual users often shares tastes with other users of the same sex, age group, or location, and hierarchical models provide more accurate predictions about individuals by leveraging learnings from the group.

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We explored using probabilistic programming for hierarchical models in our Probabilistic Real Estate prototype. This prototype predicts future real estate prices across the New York City boroughs. It enables you to input your budget (say $1.6 million) and shows you the probability of finding properties in that price range across different neighborhoods and future time periods.

Hierarchical models helped make predictions in neighborhoods with sparse pricing data. In our model, we declared that apartments are in neighborhoods and neighborhoods are in boroughs; on average, apartments in one neighborhood are more similar to others in the same location than elsewhere. By modeling this way, we could learn about the West Village not only from the West Village, but also from the East Village and Brooklyn. That means, with little data about the West Village, we could use data from the East Village to fill in the gaps! 

Many companies suffer from imperfect, incomplete data. These types of inferences can be invaluable to improve predictions based on real-world dependencies.

Play around with the prototype! You’ll see how the color gradients give you an intuitive sense for what probability distributions look like in practice.

Read more

Newer
Jan 25, 2017 · interview
Older
Jan 11, 2017 · interview

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Reports

In-depth guides to specific machine learning capabilities
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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.
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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.
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FF16

Structural Time Series

Time series data is ubiquitous. This report examines generalized additive models, which give us a simple, flexible, and interpretable means for modeling time series by decomposing them into structural components. We look at the benefits and trade-offs of taking a curve-fitting approach to time series, and demonstrate its use via Facebook’s Prophet library on a demand forecasting problem.
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FF15

Meta-Learning

In contrast to how humans learn, deep learning algorithms need vast amounts of data and compute and may yet struggle to generalize. Humans are successful in adapting quickly because they leverage their knowledge acquired from prior experience when faced with new problems. In this report, we explain how meta-learning can leverage previous knowledge acquired from data to solve novel tasks quickly and more efficiently during test time
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Automated Question Answering

Automated question answering is a user-friendly way to extract information from data using natural language. Thanks to recent advances in natural language processing, question answering capabilities from unstructured text data have grown rapidly. This blog series offers a walk-through detailing the technical and practical aspects of building an end-to-end question answering system.
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FF13

Causality for Maching Learning

The intersection of causal inference and machine learning is a rapidly expanding area of research that's already yielding capabilities to enable building more robust, reliable, and fair machine learning systems. This report offers an introduction to causal reasoning including causal graphs and invariant prediction and how to apply causal inference tools together with classic machine learning techniques in multiple use-cases.
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Interpretability

Interpretability, or the ability to explain why and how a system makes a decision, can help us improve models, satisfy regulations, and build better products. Black-box techniques like deep learning have delivered breakthrough capabilities at the cost of interpretability. In this report, recently updated to include techniques like SHAP, we show how to make models interpretable without sacrificing their capabilities or accuracy.
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Deep Learning for Anomaly Detection

From fraud detection to flagging abnormalities in imaging data, there are countless applications for automatic identification of abnormal data. This process can be challenging, especially when working with large, complex data. This report explores deep learning approaches (sequence models, VAEs, GANs) for anomaly detection, when to use them, performance benchmarks, and product possibilities.
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Federated Learning

Federated Learning makes it possible to build machine learning systems without direct access to training data. The data remains in its original location, which helps to ensure privacy and reduces communication costs. Federated learning is a great fit for smartphones and edge hardware, healthcare and other privacy-sensitive use cases, and industrial applications such as predictive maintenance.
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Deep Learning for Image Analysis: 2019 Edition

Convolutional Neural Networks (CNN) excel at learning meaningful representations of features and concepts within images. These capabilities make CNNs extremely valuable for solving problems in domains such as medical imaging, autonomous driving, manufacturing, robotics, and urban planning. In this report, we show how to select the right deep learning models for image analysis tasks and techniques for debugging deep learning models.
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Deep Learning: Image Analysis

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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

About

Cloudera Fast Forward is an applied machine learning reseach group.
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