activity
20202023
most citedGraph Neural Networks for Improved El Niño Forecasting

14 citations · 36 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023

Predictive Modeling of Equine Activity Budgets Using a 3D Skeleton Reconstructed from Surveillance Recordings

Ernest Pokropek, Sofia Broomé, Pia Haubro Andersen +1

In this work, we present a pipeline to reconstruct the 3D pose of a horse from 4 simultaneous surveillance camera recordings. Our environment poses interesting challenges to tackle…

cs.CV2021

Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition

Sofia Broomé, Ernest Pokropek, Boyu Li +1

Most action recognition models today are highly parameterized, and evaluated on datasets with appearance-wise distinct classes. It has also been shown that 2D Convolutional Neural…

stat.ME2021★ 12 cited

Deep Neural Networks for Detecting Statistical Model Misspecifications. The Case of Measurement Invariance

Artur Pokropek, Ernest Pokropek

While in recent years a number of new statistical approaches have been proposed to model group differences with a different assumption on the nature of the measurement invariance o…

cs.LG2021★ 10 cited

The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks

Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker +5

Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Niño-Southern Oscillation (ENSO). However, current dee…

cs.LG2020★ 14 cited

Graph Neural Networks for Improved El Niño Forecasting

Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker +4

Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Niño-Southern Oscillation (ENSO). However, current dee…