Online Bounded Component Analysis: A Simple Recurrent Neural Network with Local Update Rule for Unsupervised Separation of Dependent and Independent Sources. - Berfin Simsek; Alper T. Erdogan (ACSSC, 2019)

Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape. - Johanni Brea; Berfin Simsek; Bernd Illing; Wulfram Gerstner (CoRR, 2019)

Implicit Regularization of Random Feature Models. - Arthur Jacot; Berfin Simsek; Francesco Spadaro; Clément Hongler; Franck Gabriel (ICML, 2020)

Kernel Alignment Risk Estimator: Risk Prediction from Training Data. - Arthur Jacot; Berfin Simsek; Francesco Spadaro; Clément Hongler; Franck Gabriel (NeurIPS, 2020)

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances. - Berfin Simsek; François Ged; Arthur Jacot; Francesco Spadaro; Clément Hongler; Wulfram Gerstner; Johanni Brea (ICML, 2021)

Deep Linear Networks Dynamics: Low-Rank Biases Induced by Initialization Scale and L2 Regularization. - Arthur Jacot; François Ged; Franck Gabriel; Berfin Simsek; Clément Hongler (CoRR, 2021)

Understanding out-of-distribution accuracies through quantifying difficulty of test samples. - Berfin Simsek; Melissa Hall; Levent Sagun (CoRR, 2022)

Should Under-parameterized Student Networks Copy or Average Teacher Weights? - Berfin Simsek; Amire Bendjeddou; Wulfram Gerstner; Johanni Brea (NeurIPS, 2023)

MLPGradientFlow: going with the flow of multilayer perceptrons (and finding minima fast and accurately). - Johanni Brea; Flavio Martinelli; Berfin Simsek; Wulfram Gerstner (CoRR, 2023)

Expand-and-Cluster: Exact Parameter Recovery of Neural Networks. - Flavio Martinelli; Berfin Simsek; Johanni Brea; Wulfram Gerstner (CoRR, 2023)

Learning Associative Memories with Gradient Descent. - Vivien Cabannes; Berfin Simsek; Alberto Bietti (ICML, 2024)

Expand-and-Cluster: Parameter Recovery of Neural Networks. - Flavio Martinelli; Berfin Simsek; Wulfram Gerstner; Johanni Brea (ICML, 2024)

The Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escaping, and Network Embedding. - Zhengqing Wu; Berfin Simsek; François Ged (CoRR, 2024)

Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence. - Berfin Simsek; Amire Bendjeddou; Daniel Hsu 001 (AISTATS, 2025)

Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escape, and Network Embedding. - Zhengqing Wu; Berfin Simsek; François Gaston Ged (ICLR, 2025)

Flat Channels to Infinity in Neural Loss Landscapes. - Flavio Martinelli; Alexander van Meegen; Berfin Simsek; Wulfram Gerstner; Johanni Brea (CoRR, 2025)