Blog

Mar 20, 2019 · featured post

Learning with Limited Labeled Data

In recent years, machine learning technologies - especially deep learning - have made breakthroughs which have turned science fiction into reality. Autonomous cars are almost possible, and machines can comprehend language. These technical advances are unprecedented, but they hinge on the availability of vast amounts of data.

For a form of machine learning known as supervised learning, having data itself is not sufficient. Supervised machine learning, while powerful, needs data in a form that can serve as examples for what machines should learn. These examples often manifest themselves in the form of labeled data. The labels are used to teach and guide machines.

Unfortunately, data in the real world does not come nicely packaged with labels. Enterprises collect massive amounts of data - but only a small sliver (if any) of that data is labeled. For example, risk assessment is crucial for financial institutions, because it sets the amount of capital required to absorb systemic instability. Large volumes of contracts and loan agreements exist and can be used to build a risk model, but not all of them have been processed to extract relevant information, such as the purpose of the agreement, the loan amount, and the collateral amount. In corporate IT, customer service chat logs are available and can be used to identify customer concerns and satisfaction levels, but not many are annotated. In healthcare, medical images are abundant and can be used to build a diagnostic model, but these images are rarely labeled properly.

In order to leverage supervised machine learning opportunities, many enterprises attempt to manually label vast amounts of unlabeled data. This undertaking can be prohibitively expensive, inefficient, and time-consuming.

Learning with limited labeled data, then, is a machine learning capability that enables enterprises to leverage their pool of unlabeled data to open up new product possibilities. While there are many approaches, most of them attempt to capture the available labeled data using a representation that can be further adjusted if and when new labels are obtained.

Active learning is one such approach. It takes advantage of collaboration between humans and machines to smartly pick a small subset of data to be labeled. A machine learning model is then built using this small subset of data.

The latest report and prototype from Cloudera Fast Forward Labs explores active learning and its implications. While not a new framework, active learning has recently been adapted to deep learning applications, where the labeled data requirement is even more stringent. Along with the availability of tooling and a maturing supporting ecosystem, active learning is now newly exciting!

The report will be available to corporate subscribers to Cloudera Fast Forward Labs’ research and advising service on April 2nd. The prototype will be made available to the public the same day.

Please join us on Wednesday, April 3rd at 10:00am PST (1:00pm EST) for a live webinar on “Learning with Limited Labeled Data.” Shioulin Sam and Nisha Muktewar of Cloudera Fast Forward Labs will be joined by Sanjay Krishnan (Assistant Professor in Computer Science, University of Chicago) and Ines Montani (Founder of Explosion.ai).

Click here to watch the webinar!

Read more

Newer
Mar 29, 2019 · newsletter
Older
Feb 28, 2019 · newsletter

Latest posts

Jun 22, 2020 · post

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

by Victor · 199 // ... code -- 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. Recently, our team at Fast Forward Labs have been exploring state of the art models for Question Answering and have used the rather excellent HuggingFace transformers library.
...read more
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.
...read more
Apr 1, 2020 · post

Bias in Knowledge Graphs - Part 1

by Keita · Introduction This is the first part of a series to review Bias in Knowledge Graphs (KG). We aim to describe methods of identifying bias, measuring its impact, and mitigating that impact. For this part, we’ll give a broad overview of this topic. image credit: Mediamodifier from Pixabay Motivation Knowledge graphs, graphs with built-in ontologies, create unique opportunities for data analytics, machine learning, and data mining. They do this by enhancing data with the power of connections and human knowledge.
...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

About

Cloudera Fast Forward is an applied machine learning reseach group.
Cloudera   Blog   Twitter