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Implementing rotation equivariance: Group-equivariant CNN from scratch

Implementing rotation equivariance: Group-equivariant CNN from scratch

We code up a simple group-equivariant convolutional neural network (GCNN) that is equivariant to rotation. The world may be upside down, but the network will know

Sigrid Keydana

Mar 27, 2023

Machine Learning Ai Torch Spatial Data Image Recognition Image Processing

blog post

Upside down, a cat’s still a cat: Evolving image recognition with Geometric Deep Learning

In this first in a series of posts on group-equivariant convolutional neural networks (GCNNs), meet the main actors — groups — and concepts (equivariance). With GCNNs, we finally revisit the topic of Geometric Deep Learning, a principled, math-driven approach to neural networks that has consistently been rising in scope and impact

Sigrid Keydana

Mar 9, 2023

Machine Learning Best Practices Ai Torch Spatial Data Concepts

blog post

sparklyr.sedona: A sparklyr extension for analyzing geospatial data

sparklyr.sedona: A sparklyr extension for analyzing geospatial data

We are excited to announce the availability of sparklyr.sedona, a sparklyr extension making geospatial functionalities of the Apache Sedona library easily accessible from R

Yitao Li

Jul 7, 2021

MLOps and Admin Ai Packages Releases Distributed Computing Spatial Data

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Forecasting El Niño-Southern Oscillation (ENSO)

Forecasting El Niño-Southern Oscillation (ENSO)

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

Sigrid Keydana

Feb 2, 2021

Machine Learning Ai Torch Image Recognition Image Processing Time Series Spatial Data

blog post

Convolutional LSTM for spatial forecasting

Convolutional LSTM for spatial forecasting

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

Sigrid Keydana

Dec 17, 2020

Machine Learning Ai Torch Image Recognition Image Processing Time Series Spatial Data

blog post

An introduction to weather forecasting with deep learning

An introduction to weather forecasting with deep learning

A few weeks ago, we showed how to forecast chaotic dynamical systems with deep learning, augmented by a custom constraint derived from domain-specific insight. Global weather is a chaotic system, but of much higher complexity than many tasks commonly addressed with machine and/or deep learning. In this post, we provide a practical introduction featuring a simple deep learning baseline for atmospheric forecasting. While far away from being competitive, it serves to illustrate how more sophisticated and compute-intensive models may approach that formidable task by means of methods situated on the “black-box end” of the continuum

Sigrid Keydana

Sep 1, 2020

keras3 Machine Learning Ai TensorFlow Keras Time Series Spatial Data