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Fast Forward Labs research now available without a subscription

Moving forward, all new reports will be publicly available and free to download. In addition, we will be providing access to updated versions of older reports over time, so check back often to explore available free research.

Free research reports

Explore our latest research reports and prototypes, freely accessible to all. 

Inferring Concept Drift Without Labeled Data

Concept drift occurs when the statistical properties of a target domain change over time 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.

 Inferring Concept Drift Without Labeled Data

Exploring Multi-Objective Hyperparameter Optimization

We develop machine learning models against the “usual suspect” metrics like predictive accuracy, recall, and precision. However, these metrics are rarely truly all we care about. Production models must also satisfy physical requirements such as latency or memory footprint, or fairness constraints. Hyperparameter optimization becomes even more challenging when we have multiple metrics to optimize. Our latest research examines this “multi-objective” hyperparameter optimization scenario in detail.

Exploring Multi-Objective Hyperparameter Optimization

Deep Learning for Automatic Offline Signature Verification

Handwritten signature verification aims to automatically discriminate between genuine and forged signatures, and is a particularly important challenge due to the ubiquity of handwritten signatures as a form of identification in legal, financial, and administrative domains. This research cycle explored the use of deep metric learning approaches - specifically siamese networks - combined with novel feature extraction methods to improve upon traditional techniques.

Few-Shot Text Classification

Session-Based Recommender Systems

Recommendation systems have become a cornerstone of modern life, spanning sectors that include online retail, music and video streaming, and even content publishing. These systems help us navigate the sheer volume of content on the internet, allowing us to discover what’s interesting or important to us. 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.

Few-Shot Text Classification

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.

Few-Shot Text Classification

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.


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

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.

Causality for Machine 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.

Interpretability: 2020 Edition

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.

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.

Fast Forward Labs Deep Learning for Image Analysis - 2019 Edition report preview

Learning with Limited Labeled Data

 Being able to learn with limited labeled data relaxes the stringent labeled data requirement for supervised machine learning. This report focuses on active learning, a technique that relies on collaboration between machines and humans to label smartly. Active learning reduces the number of labeled examples required to train a model, saving time and money while obtaining comparable performance to models trained with much more data. With active learning, enterprises can leverage their large pool of unlabeled data to open up new product possibilities..

Fast Forward Labs Learning with Limited Labeled Data

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.

Fast Forward Labs Deep Learning for Image Analysis - 2019 Edition report preview


This report explores methods for extractive summarization, a capability that allows one to automatically summarize documents. This technique has a wealth of applications: from the ability to distill thousands of product reviews, extract the most important content from long news articles, or automatically cluster customer bios into personas.

Fast Forward Labs Deep Learning for Image Analysis - 2019 Edition report preview

Deep Learning for Image Analysis - 2019 Edition

Convolutional neural networks (CNNs or ConvNets) excel at learning meaningful representations of features and concepts within images, making CNNs valuable for solving problems in multiple domains, from medical imaging to manufacturing. In this report, we show how to select the right deep learning models for image analysis tasks and techniques for debugging deep learning models.

Fast Forward Labs Deep Learning for Image Analysis - 2019 Edition report preview

Deep Learning: Image Analysis

This report explores the history and current state of deep learning, explains how to apply it, and predicts future developments.

Subscription-only reports

Updated versions of older reports will be available for free in the future, so check back often.

Fast Forward Labs Transfer Learning for NLP report preview

Transfer Learning for NLP

Natural language processing (NLP) technologies can translate language, answer questions, and generate human-like text, but the underlying deep learning techniques require costly datasets, infrastructure, and expertise. In this report, we show how to use transfer learning to adapt existing models to any NLP application, making it easier to build high-performance NLP systems.

Image of Multi-Task Learning Report and Prototype

Multi-task learning

In this report, we focus on multi-task learning, a new approach to machine learning that allows algorithms to master tasks in parallel.

Semantic recommendations

In this report, we show how using the semantic content of items can help solve common recommendation pitfalls such as the cold start problem, and open up new product possibilities.


In this report, we show how to make models interpretable without sacrificing their capabilities or accuracy.

Probabilistic programming

Here, we show how to use probabilistic programming and Bayesian inference to easily build tools that make better predictions for more effective decision making.

Probabilistic methods for realtime streams

Here, we explore probabilistic methods that offer highly efficient models for extracting value from streams of data as they are generated.

Natural language generation

In this report, we look at how machine systems can turn highly structured data into human language narrative.

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