Все публикации

1:31:06

Mark Bishop, 'Deep Stupidity: A Provocation on the Things LLMs Can and Cannot Do.'

0:49:51

Linfeng Wang: Measure transport with kernel mean embeddings

0:59:27

Liv Vage: Graph neural nets and reinforcement learning for particle tracking at the LHC

0:57:13

Florian Schaefer: Statistical Inference and PDEs: From operator learning to shock capturing

0:59:35

Boya Hou: Nonparametric Compressed Learning of Dynamical Systems

1:22:22

Matthew Colbrook: The Hitchhiker's Guide to the DMD Multiverse

1:03:14

Lorenz Richter: An Optimal Control Perspective on Diffusion-Based Generative Modeling

1:46:22

Nathan Doumèche: Physics-Informed Machine Learning as a Kernel Method

0:57:52

Nicolas Boulle: Elliptic PDE learning is provably data-efficient

1:15:39

Yuanzhao Zhang: Catch-22s of reservoir computing

1:11:58

Petar Veličković: Graph Deep Learning: Monoids and time, Embracing asynchrony in (G)NNs

0:46:28

B. Hamzi: On Bridging ML, Dynamical Systems, & Algorithmic Info. Th. Via SKFs and PDE Simplification

0:47:44

Tobias Schröder: Energy Discrepancy: Training of Energy-Based Models without Scores or MCMC

1:20:01

Shi Jin: Dimension Lifting for Quantum Computation of Partial Differential Eqns and Related Problems

1:04:31

Cristina Cipriani: A mean-field optimal control approach for the training of NeurODEs & AutoencODEs.

1:37:27

Islem Rekik: The landscape of generative GNNs in network neuroscience

0:48:41

Yoshito Hirata: Unified time series analysis for nonlinear deterministic/stochastic systems

0:53:04

Stefan Klus: Kernel based approximation of the Koopman generator and Schrödinger operator

0:57:48

Daniel Wilczak: Recent advances in rigorous computation of Poincaré maps

1:13:19

P.-C. Aubin: The reproducing kernels underlying LQ control and Kalman filtering, and their duality

0:53:52

Mengjia Xu: TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings

0:52:41

F. Ferrini: A hands-on introduction to Graph Deep Learning, with examples in PyTorch Geometric (4/4)

1:06:55

S. Azzolin: A hands-on introduction to Graph Deep Learning, with examples in PyTorch Geometric (3/4)

1:50:46

A. Longa: A hands-on introduction to Graph Deep Learning, with examples in PyTorch Geometric (2/4)