DEEPK 2024 : International Workshop on Deep Learning and Kernel Machines

DEEPK 2024
International Workshop on Deep Learning and Kernel Machines

March 7-8, 2024, Leuven, Arenberg Castle, Belgium
https://www.esat.kuleuven.be/stadius/E/DEEPK2024

- Main scope -

Major progress and impact has been achieved through deep learning architectures with many exciting applications such as by generative models and transformers. At the same time it triggers new questions on the fundamental possibilities and limitations of the models, with respect to representations, scalability, learning and generalization aspects. Through kernel-based methods often a deeper understanding and solid foundations have been obtained, complementary to the powerful and flexible deep learning architectures. Recent examples are understanding generalization of over-parameterized models in the double descent phenomenon and conceiving attention mechanisms in transformers as kernel machines. The aim of DEEPK 2024 is to provide a multi-disciplinary forum where researchers of different communities can meet, to find new synergies between deep learning and kernel machines, both at the level of theory and applications.

- Topics -

Topics include but are not limited to:

Deep learning and generalization
Double descent phenomenon and over-parameterized models
Transformers and asymmetric kernels
Attention mechanisms, kernel singular value decomposition
Learning with asymmetric kernels
Duality and deep learning
Regularization schemes, normalization
Neural tangent kernel
Deep learning and Gaussian processes
Transformers, support vector machines and least squares support vector machines
Autoencoders, neural networks and kernel methods
Kernel methods in GANs, variational autoencoders, diffusion models, Generative Flow Networks
Generative kernel machines
Deep Kernel PCA, deep kernel machines, deep eigenvalues, deep eigenvectors
Restricted Boltzmann machines, Restricted kernel machines, deep learning, energy based models
Disentanglement and explainability
Tensors, kernels and deep learning
Convolutional kernels
Sparsity, robustness, low-rank representations, compression
Nystrom method, Nystromformer
Efficient training methods
Lagrange duality, Fenchel duality, estimation in Hilbert spaces, reproducing kernel Hilbert spaces, vector-valued reproducing kernel Hilbert spaces, Krein spaces, Banach spaces, RKHS and C*-algebra
Applications


- Invited Speakers -

Mikhail Belkin (University of California San Diego)
Volkan Cevher (EPFL)
Florence d'Alche-Buc (Telecom Paris, Institut Polytechnique de Paris)
Julien Mairal (INRIA)
Massimiliano Pontil (IIT and University College London)
Dingxuan Zhou (University of Sydney)


- Call for abstracts -

The DEEPK 2024 program will include oral and poster sessions. Interested participants are cordially invited to submit an extended abstract (max. 2 pages) for their contribution. Please prepare your extended abstract submission in LaTeX, according to the provided stylefile and submit it in pdf format (max. 2 pages). Further extended abstract information will be given at https://www.esat.kuleuven.be/stadius/E/DEEPK2024/call_for_abstracts.php .

- Schedule -

Deadline extended abstract submission:
Feb 8, 2024
Notification of acceptance and presentation format (oral/poster):
Feb 22, 2024
Deadline for registration:
Feb 29, 2024
International Workshop DEEPK 2024:
March 7-8, 2024

- Organizing committee -

Johan Suykens (Chair), Alex Lambert, Panos Patrinos, Qinghua Tao, Francesco Tonin

- Other info -

Please consult the DEEPK 2024 website https://www.esat.kuleuven.be/stadius/E/DEEPK2024 for info on program, registration, location and venue. The event is co-sponsored by ERC Advanced Grant E-DUALITY and KU Leuven.