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Chat with AI in RStudio

Interact with Github Copilot and OpenAI’s GPT (ChatGPT) models directly in RStudio. The chattr Shiny add-in makes it easy for you to interact with these and other Large Language Models (LLMs)

Edgar Ruiz

Apr 4, 2024

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LLaMA in R with Keras and TensorFlow

Implementation and walk-through of LLaMA, a Large Language Model, in R, with TensorFlow and Keras

Tomasz Kalinowski

May 25, 2023

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De-noising Diffusion with torch

Currently, in generative deep learning, no other approach seems to outperform the family of diffusion models. Would you like to try for yourself? If so, our torch implementation of de-noising diffusion provides an easy-to-use, easy-to-configure interface

Daniel Falbel, Sigrid Keydana

Apr 13, 2023

Machine Learning Ai Torch Image Recognition Image Processing Generative Models

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FNN-VAE for noisy time series forecasting

FNN-VAE for noisy time series forecasting

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

Sigrid Keydana

Jul 31, 2020

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Time series prediction with FNN-LSTM

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

Sigrid Keydana

Jul 20, 2020

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Deep attractors: Where deep learning meets chaos

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

Sigrid Keydana

Jun 24, 2020

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Easy PixelCNN with tfprobability

PixelCNN is a deep learning architecture - or bundle of architectures - designed to generate highly realistic-looking images. To use it, no reverse-engineering of arXiv papers or search for reference implementations is required: TensorFlow Probability and its R wrapper, tfprobability, now include a PixelCNN distribution that can be used to train a straightforwardly-defined neural network in a parameterizable way

Sigrid Keydana

May 29, 2020

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Experimenting with autoregressive flows in TensorFlow Probability

Experimenting with autoregressive flows in TensorFlow Probability

Continuing from the recent introduction to bijectors in TensorFlow Probability (TFP), this post brings autoregressivity to the table. Using TFP through the new R package tfprobability, we look at the implementation of masked autoregressive flows (MAF) and put them to use on two different datasets

Sigrid Keydana

Apr 24, 2019

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Getting into the flow: Bijectors in TensorFlow Probability

Getting into the flow: Bijectors in TensorFlow Probability

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

Sigrid Keydana

Apr 5, 2019

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Discrete Representation Learning with VQ-VAE and TensorFlow Probability

Discrete Representation Learning with VQ-VAE and TensorFlow Probability

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

Sigrid Keydana

Jan 24, 2019

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Getting started with TensorFlow Probability from R

Getting started with TensorFlow Probability from R

TensorFlow Probability offers a vast range of functionality ranging from distributions over probabilistic network layers to probabilistic inference. It works seamlessly with core TensorFlow and (TensorFlow) Keras. In this post, we provide a short introduction to the distributions layer and then, use it for sampling and calculating probabilities in a Variational Autoencoder

Sigrid Keydana

Jan 8, 2019

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Image-to-image translation with pix2pix

Conditional GANs (cGANs) may be used to generate one type of object based on another - e.g., a map based on a photo, or a color video based on black-and-white. Here, we show how to implement the pix2pix approach with Keras and eager execution

Sigrid Keydana

Sep 20, 2018

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Neural style transfer with eager execution and Keras

Neural style transfer with eager execution and Keras

Continuing our series on combining Keras with TensorFlow eager execution, we show how to implement neural style transfer in a straightforward way. Based on this easy-to-adapt example, you can easily perform style transfer on your own images

Sigrid Keydana

Sep 10, 2018

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Generating images with Keras and TensorFlow eager execution

Generating images with Keras and TensorFlow eager execution

Generative adversarial networks (GANs) are a popular deep learning approach to generating new entities (often but not always images). We show how to code them using Keras and TensorFlow eager execution

Sigrid Keydana

Aug 26, 2018

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