Pinto, A., Rangamani, A. & Poggio, T. On Generalization Bounds for Neural Networks with Low Rank Layers. (2024).

Rangamani, A., Lindegaard, M., Galanti, T. & Poggio, T. Feature learning in deep classifiers through Intermediate Neural Collapse. (2023).

Xu, M. et al. The Janus effects of SGD vs GD: high noise and low rank. (2023).

Xie, Y., Li, Y. & Rangamani, A. Skip Connections Increase the Capacity of Associative Memories in Variable Binding Mechanisms. (2023).

Banburski, A. & Rangamani, A. Neural Collapse in Deep Homogeneous Classifiers and the role of Weight Decay. IEEE International Conference on Acoustics, Speech and Signal Processing (2022).

Rangamani, A. & Xie, Y. Understanding the Role of Recurrent Connections in Assembly Calculus. (2022).

Xu, M. et al. Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss. (2021).

Rangamani, A., Rosasco, L. & Poggio, T. For interpolating kernel machines, the minimum norm ERM solution is the most stable. (2020).