Adversarial Learning for Debiasing Knowledge Graph Embeddings. - Mario Arduini; Lorenzo Noci; Federico Pirovano; Ce Zhang; Yash Raj Shrestha; Bibek Paudel (CoRR, 2020)

Precise characterization of the prior predictive distribution of deep ReLU networks. - Lorenzo Noci; Gregor Bachmann; Kevin Roth; Sebastian Nowozin; Thomas Hofmann (NeurIPS, 2021)

Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect. - Lorenzo Noci; Kevin Roth; Gregor Bachmann; Sebastian Nowozin; Thomas Hofmann (NeurIPS, 2021)

How Tempering Fixes Data Augmentation in Bayesian Neural Networks. - Gregor Bachmann; Lorenzo Noci; Thomas Hofmann (ICML, 2022)

Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse. - Lorenzo Noci; Sotiris Anagnostidis; Luca Biggio; Antonio Orvieto; Sidak Pal Singh; Aurélien Lucchi (NeurIPS, 2022)

Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning. - Sanghwan Kim; Lorenzo Noci; Antonio Orvieto; Thomas Hofmann (CVPR, 2023)

The Curious Case of Benign Memorization. - Sotiris Anagnostidis; Gregor Bachmann; Lorenzo Noci; Thomas Hofmann (ICLR, 2023)

Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers. - Sotiris Anagnostidis; Dario Pavllo; Luca Biggio; Lorenzo Noci; Aurélien Lucchi; Thomas Hofmann (NeurIPS, 2023)

The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit. - Lorenzo Noci; Chuning Li; Mufan Bill Li; Bobby He; Thomas Hofmann; Chris J. Maddison; Dan Roy (NeurIPS, 2023)

The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit. - Lorenzo Noci; Chuning Li; Mufan Bill Li; Bobby He; Thomas Hofmann; Chris J. Maddison; Daniel M. Roy 001 (CoRR, 2023)

Disentangling Linear Mode-Connectivity. - Gul Sena Altintas; Gregor Bachmann; Lorenzo Noci; Thomas Hofmann (CoRR, 2023)

How Good is a Single Basin? - Kai Lion; Lorenzo Noci; Thomas Hofmann; Gregor Bachmann (AISTATS, 2024)

Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit. - Blake Bordelon; Lorenzo Noci; Mufan Bill Li; Boris Hanin; Cengiz Pehlevan (ICLR, 2024)

Understanding and Minimising Outlier Features in Transformer Training. - Bobby He; Lorenzo Noci; Daniele Paliotta; Imanol Schlag; Thomas Hofmann (NeurIPS, 2024)

Super Consistency of Neural Network Landscapes and Learning Rate Transfer. - Lorenzo Noci; Alexandru Meterez; Thomas Hofmann; Antonio Orvieto (NeurIPS, 2024)

Why do Learning Rates Transfer? Reconciling Optimization and Scaling Limits for Deep Learning. - Lorenzo Noci; Alexandru Meterez; Thomas Hofmann; Antonio Orvieto (CoRR, 2024)

Understanding and Minimising Outlier Features in Neural Network Training. - Bobby He; Lorenzo Noci; Daniele Paliotta; Imanol Schlag; Thomas Hofmann (CoRR, 2024)

Don't be lazy: CompleteP enables compute-efficient deep transformers. - Nolan Dey; Bin Claire Zhang; Lorenzo Noci; Mufan Bill Li; Blake Bordelon; Shane Bergsma; Cengiz Pehlevan; Boris Hanin; Joel Hestness (CoRR, 2025)

Attention Retrieves, MLP Memorizes: Disentangling Trainable Components in the Transformer. - Yihe Dong; Lorenzo Noci; Mikhail Khodak; Mufan Bill Li (CoRR, 2025)

The Importance of Being Lazy: Scaling Limits of Continual Learning. - Jacopo Graldi; Alessandro Breccia; Giulia Lanzillotta; Thomas Hofmann; Lorenzo Noci (CoRR, 2025)