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Niclas Alexander Göring

4 accepted papers

2026

Decoupling Dynamical Richness from Representation Learning: Towards Practical Measurement

Yoonsoo Nam, Nayara Fonseca, Seok Hyeong Lee, Chris Mingard, Niclas Alexander Göring, Ouns El Harzli +3
ICLR 2026poster

Dynamic feature transformation (the rich regime) does not always align with predictive performance (better representation), yet accuracy is often used as a proxy for richness, limiting analysis of their relationship. We propose a computationally efficient, performance-independent metric of richness…

Cited by 0Source ↗Scholar ↗
2025

Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking)

Yoonsoo Nam, Seok Hyeong Lee, Clémentine Carla Juliette Dominé, Yeachan Park, Charles London, Wonyl Choi +2
ICML 2025poster

In physics, complex systems are often simplified into minimal, solvable models that retain only the core principles. In machine learning, layerwise linear models (e.g., linear neural networks) act as simplified representations of neural network dynamics. These models follow the dynamical feedback pr…

Cited by 11Source ↗PDF ↗Scholar ↗
2024

Out-of-Domain Generalization in Dynamical Systems Reconstruction

Niclas Alexander Göring, Florian Hess, Manuel Brenner, Zahra Monfared, Daniel Durstewitz
ICML 2024poster

In science we are interested in finding the governing equations, the dynamical rules, underlying empirical phenomena. While traditionally scientific models are derived through cycles of human insight and experimentation, recently deep learning (DL) techniques have been advanced to reconstruct dynami…

Cited by 10Source ↗PDF ↗code ↗Scholar ↗
2023

Bifurcations and loss jumps in RNN training

Lukas Eisenmann, Zahra Monfared, Niclas Alexander Göring, Daniel Durstewitz
NeurIPS 2023spotlight

Recurrent neural networks (RNNs) are popular machine learning tools for modeling and forecasting sequential data and for inferring dynamical systems (DS) from observed time series. Concepts from DS theory (DST) have variously been used to further our understanding of both, how trained RNNs solve com…

Cited by 15Source ↗PDF ↗code ↗Scholar ↗