1 code implementation • 28 Dec 2023 • Tycho F. A. van der Ouderaa, Markus Nagel, Mart van Baalen, Yuki M. Asano, Tijmen Blankevoort
Experimentally, our method can prune rows and columns from a range of OPT models and Llamav2-7B by 20%-30%, with a negligible loss in performance, and achieve state-of-the-art results in unstructured and semi-structured pruning of large language models.
1 code implementation • 6 Jun 2023 • Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Rätsch, Bernhard Schölkopf
Recent works show that Bayesian model selection with Laplace approximations can allow to optimize such hyperparameters just like standard neural network parameters using gradients and on the training data.
no code implementations • 14 Apr 2022 • Tycho F. A. van der Ouderaa, David W. Romero, Mark van der Wilk
Equivariances provide useful inductive biases in neural network modeling, with the translation equivariance of convolutional neural networks being a canonical example.
no code implementations • 25 Feb 2022 • Tycho F. A. van der Ouderaa, Mark van der Wilk
Assumptions about invariances or symmetries in data can significantly increase the predictive power of statistical models.
1 code implementation • 22 Feb 2022 • Alexander Immer, Tycho F. A. van der Ouderaa, Gunnar Rätsch, Vincent Fortuin, Mark van der Wilk
We develop a convenient gradient-based method for selecting the data augmentation without validation data during training of a deep neural network.
no code implementations • 1 Oct 2020 • Tycho F. A. van der Ouderaa, Ivana Išgum, Wouter B. Veldhuis, Bob D. de Vos
Deep neural networks are increasingly used for pair-wise image registration.
3 code implementations • CVPR 2019 • Tycho F. A. van der Ouderaa, Daniel E. Worrall
The Pix2pix and CycleGAN losses have vastly improved the qualitative and quantitative visual quality of results in image-to-image translation tasks.