July 15, 2021

Sigrid Keydana

R

Concepts

Meta

AI & Society

The topic of AI fairness metrics is as important to society as it is confusing. Confusing it is due to a number of reasons: terminological proliferation, abundance of formulae, and last not least the impression that everyone else seems to know what they're talking about. This text hopes to counteract some of that confusion by starting from a common-sense approach of contrasting two basic positions: On the one hand, the assumption that dataset features may be taken as reflecting the underlying concepts ML practitioners are interested in; on the other, that there inevitably is a gap between concept and measurement, a gap that may be bigger or smaller depending on what is being measured. In contrasting these fundamental views, we bring together concepts from ML, legal science, and political philosophy.

Aug. 26, 2021

Sigrid Keydana

Concepts

Meta

R

Geometric deep learning is a "program" that aspires to situate deep learning architectures and techniques in a framework of mathematical priors. The priors, such as various types of invariance, first arise in some physical domain. A neural network that well matches the domain will preserve as many invariances as possible. In this post, we present a very conceptual, high-level overview, and highlight a few applications.

March 10, 2021

Sigrid Keydana

Torch

R

Time Series

This post is an introduction to time-series forecasting with torch. Central topics are data input, and practical usage of RNNs (GRUs/LSTMs). Upcoming posts will build on this, and introduce increasingly involved architectures.

Feb. 2, 2021

Sigrid Keydana

Torch

R

Image Recognition & Image Processing

Time Series

Spatial Data

El Niño-Southern Oscillation (ENSO) is an atmospheric phenomenon, located in the tropical Pacific, that greatly affects ecosystems as well as human well-being on a large portion of the globe. We use the convLSTM introduced in a prior post to predict the Niño 3.4 Index from spatially-ordered sequences of sea surface temperatures.

Feb. 4, 2021

Athos Damiani

Torch

R

Audio Processing

This article translates Daniel Falbel's post on "Simple Audio Classification" from TensorFlow/Keras to torch/torchaudio.

Feb. 11, 2021

Sigrid Keydana

Torch

R

Tabular Data

Today we introduce tabnet, a torch implementation of "TabNet: Attentive Interpretable Tabular Learning" that is fully integrated with the tidymodels framework. Per se, already, tabnet was designed to require very little data pre-processing; thanks to tidymodels, hyperparameter tuning (so often cumbersome in deep learning) becomes convenient and even, fun!

Dec. 17, 2020

Sigrid Keydana

Torch

R

Image Recognition & Image Processing

Time Series

Spatial Data

In forecasting spatially-determined phenomena (the weather, say, or the next frame in a movie), we want to model temporal evolution, ideally using recurrence relations. At the same time, we'd like to efficiently extract spatial features, something that is normally done with convolutional filters. Ideally then, we'd have at our disposal an architecture that is both recurrent and convolutional. In this post, we build a convolutional LSTM with torch.

Nov. 30, 2020

Sigrid Keydana

Torch

R

Image Recognition & Image Processing

The need to segment images arises in various sciences and their applications, many of which are vital to human (and animal) life. In this introductory post, we train a U-Net to mark lesioned regions on MRI brain scans.

July 31, 2020

Sigrid Keydana

R

TensorFlow/Keras

Time Series

Unsupervised Learning

In the last part of this mini-series on forecasting with false nearest neighbors (FNN) loss, we replace the LSTM autoencoder from the previous post by a convolutional VAE, resulting in equivalent prediction performance but significantly lower training time. In addition, we find that FNN regularization is of great help when an underlying deterministic process is obscured by substantial noise.

July 20, 2020

Sigrid Keydana

R

TensorFlow/Keras

Time Series

Unsupervised Learning

In a recent post, we showed how an LSTM autoencoder, regularized by false nearest neighbors (FNN) loss, can be used to reconstruct the attractor of a nonlinear, chaotic dynamical system. Here, we explore how that same technique assists in prediction. Matched up with a comparable, capacity-wise, "vanilla LSTM", FNN-LSTM improves performance on a set of very different, real-world datasets, especially for the initial steps in a multi-step forecast.

June 24, 2020

Sigrid Keydana

R

TensorFlow/Keras

Time Series

Unsupervised Learning

In nonlinear dynamics, when the state space is thought to be multidimensional but all we have for data is just a univariate time series, one may attempt to reconstruct the true space via delay coordinate embeddings. However, it is not clear a priori how to choose dimensionality and time lag of the reconstruction space. In this post, we show how to use an autoencoder architecture to circumvent the problem: Given just a scalar series of observations, the autoencoder directly learns to represent attractors of chaotic systems in adequate dimensionality.

May 15, 2020

Sigrid Keydana

R

Privacy & Security

TensorFlow/Keras

Compared to other applications, deep learning models might not seem too likely as victims of privacy attacks. However, methods exist to determine whether an entity was used in the training set (an adversarial attack called member inference), and techniques subsumed under "model inversion" allow to reconstruct raw data input given just model output (and sometimes, context information). This post shows an end-to-end example of model inversion, and explores mitigation strategies using TensorFlow Privacy.

Feb. 19, 2020

Sigrid Keydana

Probabilistic ML/DL

Concepts

Kullback-Leibler divergence is not just used to train variational autoencoders or Bayesian networks (and not just a hard-to-pronounce thing). It is a fundamental concept in information theory, put to use in a vast range of applications. Most interestingly, it's not always about constraint, regularization or compression. Quite on the contrary, sometimes it is about novelty, discovery and surprise.

Nov. 27, 2019

Sigrid Keydana

Packages/Releases

TensorFlow/Keras

Looking for materials to get started with deep learning from R? This post presents useful tutorials, guides, and background documentation on the new TensorFlow for R website. Advanced users will find pointers to applications of new release 2.0 (or upcoming 2.1!) features alluded to in the recent TensorFlow 2.0 post.

Dec. 20, 2019

Sigrid Keydana

Privacy & Security

TensorFlow/Keras

Time Series

Differential Privacy guarantees that results of a database query are basically independent of the presence in the data of a single individual. Applied to machine learning, we expect that no single training example influences the parameters of the trained model in a substantial way. This post introduces TensorFlow Privacy, a library built on top of TensorFlow, that can be used to train differentially private deep learning models from R.

Dec. 10, 2019

Sigrid Keydana

Probabilistic ML/DL

TensorFlow/Keras

Continuing our tour of applications of TensorFlow Probability (TFP), after Bayesian Neural Networks, Hamiltonian Monte Carlo and State Space Models, here we show an example of Gaussian Process Regression. In fact, what we see is a rather "normal" Keras network, defined and trained in pretty much the usual way, with TFP's Variational Gaussian Process layer pulling off all the magic.

May 24, 2019

Sigrid Keydana

Bayesian Modeling

TensorFlow/Keras

This post builds on our recent introduction to multi-level modeling with tfprobability, the R wrapper to TensorFlow Probability. We show how to pool not just mean values ("intercepts"), but also relationships ("slopes"), thus enabling models to learn from data in an even broader way. Again, we use an example from Richard McElreath's "Statistical Rethinking"; the terminology as well as the way we present this topic are largely owed to this book.

Oct. 3, 2019

Sigrid Keydana

Bayesian Modeling

Concepts

TensorFlow Probability, and its R wrapper tfprobability, provide Markov Chain Monte Carlo (MCMC) methods that were used in a number of recent posts on this blog. These posts were directed to users already comfortable with the method, and terminology, per se, which readers mainly interested in deep learning won't necessarily be. Here we try to make up leeway, introducing Hamitonian Monte Carlo (HMC) as well as a few often-heard "buzzwords" accompanying it, always striving to keep in mind what it is all "for".

Aug. 23, 2019

Daniel Falbel

Sigrid Keydana

Image Recognition & Image Processing

TensorFlow/Keras

In image segmentation, every pixel of an image is assigned a class. Depending on the application, classes could be different cell types; or the task could be binary, as in "cancer cell yes or no?". Area of application notwithstanding, the established neural network architecture of choice is U-Net. In this post, we show how to preprocess data and train a U-Net model on the Kaggle Carvana image segmentation data.

March 15, 2019

Sigrid Keydana

Meta

Concepts

Not everybody who wants to get into deep learning has a strong background in math or programming. This post elaborates on a concepts-driven, abstraction-based way to learn what it's all about.

April 5, 2019

Sigrid Keydana

Probabilistic ML/DL

TensorFlow/Keras

Concepts

Unsupervised Learning

Normalizing flows are one of the lesser known, yet fascinating and successful architectures in unsupervised deep learning. In this post we provide a basic introduction to flows using tfprobability, an R wrapper to TensorFlow Probability. Upcoming posts will build on this, using more complex flows on more complex data.

Feb. 7, 2019

Sigrid Keydana

TensorFlow/Keras

Concepts

Audio Processing

Sometimes, deep learning is seen - and welcomed - as a way to avoid laborious preprocessing of data. However, there are cases where preprocessing of sorts does not only help improve prediction, but constitutes a fascinating topic in itself. One such case is audio classification. In this post, we build on a previous post on this blog, this time focusing on explaining some of the non-deep learning background. We then link the concepts explained to updated for near-future releases TensorFlow code.

Jan. 24, 2019

Sigrid Keydana

TensorFlow/Keras

Probabilistic ML/DL

Unsupervised Learning

Mostly when thinking of Variational Autoencoders (VAEs), we picture the prior as an isotropic Gaussian. But this is by no means a necessity. The Vector Quantised Variational Autoencoder (VQ-VAE) described in van den Oord et al's "Neural Discrete Representation Learning" features a discrete latent space that allows to learn impressively concise latent representations. In this post, we combine elements of Keras, TensorFlow, and TensorFlow Probability to see if we can generate convincing letters resembling those in Kuzushiji-MNIST.

Nov. 12, 2018

Sigrid Keydana

Image Recognition & Image Processing

Probabilistic ML/DL

TensorFlow/Keras

In deep learning, there is no obvious way of obtaining uncertainty estimates. In 2016, Gal and Ghahramani proposed a method that is both theoretically grounded and practical: use dropout at test time. In this post, we introduce a refined version of this method (Gal et al. 2017) that has the network itself learn how uncertain it is.

Oct. 22, 2018

Sigrid Keydana

TensorFlow/Keras

Unsupervised Learning

Image Recognition & Image Processing

Like GANs, variational autoencoders (VAEs) are often used to generate images. However, VAEs add an additional promise: namely, to model an underlying latent space. Here, we first look at a typical implementation that maximizes the evidence lower bound. Then, we compare it to one of the more recent competitors, MMD-VAE, from the Info-VAE (information maximizing VAE) family.

Sept. 26, 2018

Sigrid Keydana

TensorFlow/Keras

Tabular Data

Embeddings are not just for use in natural language processing. Here we apply embeddings to a common task in collaborative filtering - predicting user ratings - and on our way, strive for a better understanding of what an embedding layer really does.

Sept. 17, 2018

Sigrid Keydana

Natural Language Processing

TensorFlow/Keras

Image Recognition & Image Processing

Image captioning is a challenging task at intersection of vision and language. Here, we demonstrate using Keras and eager execution to incorporate an attention mechanism that allows the network to concentrate on image features relevant to the current state of text generation.

June 6, 2018

Daniel Falbel

TensorFlow/Keras

Audio Processing

In this tutorial we will build a deep learning model to classify words. We will use the Speech Commands dataset which consists of 65,000 one-second audio files of people saying 30 different words.

July 17, 2018

Nick Strayer

Using Keras to train a convolutional neural network to classify physical activity. The dataset was built from the recordings of 30 subjects performing basic activities and postural transitions while carrying a waist-mounted smartphone with embedded inertial sensors.

July 30, 2018

Sigrid Keydana

Natural Language Processing

TensorFlow/Keras

As sequence to sequence prediction tasks get more involved, attention mechanisms have proven helpful. A prominent example is neural machine translation. Following a recent Google Colaboratory notebook, we show how to implement attention in R.

Jan. 25, 2018

Daniel Falbel

TensorFlow/Keras

Unsupervised Learning

Cloud

In this post we will train an autoencoder to detect credit card fraud. We will also demonstrate how to train Keras models in the cloud using CloudML. The basis of our model will be the Kaggle Credit Card Fraud Detection dataset.

Jan. 9, 2018

Daniel Falbel

TensorFlow/Keras

Natural Language Processing

In this post we will use Keras to classify duplicated questions from Quora. Our implementation is inspired by the Siamese Recurrent Architecture, with modifications to the similarity measure and the embedding layers (the original paper uses pre-trained word vectors)

Jan. 29, 2018

Leon Eyrich Jessen

TensorFlow/Keras

Tabular Data

The aim of this post is to illustrate how deep learning is being applied in cancer immunotherapy (Immuno-oncology or Immunooncology) - a cancer treatment strategy, where the aim is to utilize the cancer patient's own immune system to fight the cancer.

- Articles (33)
- AI & Society (1)
- Audio Processing (3)
- Bayesian Modeling (2)
- Cloud (1)
- Concepts (7)
- Image Recognition & Image Processing (7)
- Meta (3)
- Natural Language Processing (3)
- Packages/Releases (1)
- Privacy & Security (2)
- Probabilistic ML/DL (5)
- R (12)
- Spatial Data (2)
- Tabular Data (3)
- TensorFlow/Keras (21)
- Time Series (7)
- Torch (6)
- Unsupervised Learning (7)