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20162025
most citedComplexity of Linear Regions in Deep Networks

55 citations · 232 across the 42 of their papers we have counts for

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Showing 2023 · cs.LGShow all

9 papers · 2 filters

cs.LG2023★ 2 cited

Towards Causal Representations of Climate Model Data

Julien Boussard, Chandni Nagda, Julia Kaltenborn +5

Climate models, such as Earth system models (ESMs), are crucial for simulating future climate change based on projected Shared Socioeconomic Pathways (SSP) greenhouse gas emissions…

cs.LG2023★ 5 cited

ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning

Julia Kaltenborn, Charlotte E. E. Lange, Venkatesh Ramesh +6

Climate models have been key for assessing the impact of climate change and simulating future climate scenarios. The machine learning (ML) community has taken an increased interest…

cs.LG2023★ 2 cited

SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data

Mélisande Teng, Amna Elmustafa, Benjamin Akera +4

Biodiversity is declining at an unprecedented rate, impacting ecosystem services necessary to ensure food, water, and human health and well-being. Understanding the distribution of…

cs.LG2023

Towards Climate Variable Prediction with Conditioned Spatio-Temporal Normalizing Flows

Christina Winkler, David Rolnick

This study investigates how conditional normalizing flows can be applied to remote sensing data products in climate science for spatio-temporal prediction. The method is chosen due…

cs.LG2023

On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions

Alvaro Carbonero, Alexandre Duval, Victor Schmidt +4

The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorporate the geometric configuration of all a…

cs.LG2023

Hidden symmetries of ReLU networks

J. Elisenda Grigsby, Kathryn Lindsey, David Rolnick

The parameter space for any fixed architecture of feedforward ReLU neural networks serves as a proxy during training for the associated class of functions - but how faithful is thi…