29 citations · 106 across the 17 of their papers we have counts for
19 papers
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit
Lorenzo Noci, Chuning Li, Mufan Bill Li +4
In deep learning theory, the covariance matrix of the representations serves as a proxy to examine the network's trainability. Motivated by the success of Transformers, we study th…
The Hessian perspective into the Nature of Convolutional Neural Networks
Sidak Pal Singh, Thomas Hofmann, Bernhard Schölkopf
While Convolutional Neural Networks (CNNs) have long been investigated and applied, as well as theorized, we aim to provide a slightly different perspective into their nature -- th…
Phenomenology of Double Descent in Finite-Width Neural Networks
Sidak Pal Singh, Aurelien Lucchi, Thomas Hofmann +1
`Double descent' delineates the generalization behaviour of models depending on the regime they belong to: under- or over-parameterized. The current theoretical understanding behin…
Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks
Antonio Orvieto, Jonas Kohler, Dario Pavllo +2
This paper revisits the so-called vanishing gradient phenomenon, which commonly occurs in deep randomly initialized neural networks. Leveraging an in-depth analysis of neural chain…
Generative Minimization Networks: Training GANs Without Competition
Paulina Grnarova, Yannic Kilcher, Kfir Y. Levy +2
Many applications in machine learning can be framed as minimization problems and solved efficiently using gradient-based techniques. However, recent applications of generative mode…
SwissDial: Parallel Multidialectal Corpus of Spoken Swiss German
Pelin Dogan-Schönberger, Julian Mäder, Thomas Hofmann
Swiss German is a dialect continuum whose natively acquired dialects significantly differ from the formal variety of the language. These dialects are mostly used for verbal communi…