Person
Clément Hongler
2006: B.Sc. Math, EPFL2008: M.Sc. Math, EPFL2010: Ph.D. Math, Université de Genève2010-2014: Ritt Assistant Professor, Columbia University2014-2018 Tenure-Track Assistant Professor, EPFL2019-present: Associate Professor, EPFL
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Related units (1)
Courses taught by this person (3)
MATH-200: Analysis III - complex analysis and vector fields
Apprendre les bases de l'analyse vectorielle et de l'analyse complexe.
MATH-642: Artificial Life
We will give an overview of the field of Artificial Life (Alife). We study questions such as emergence of complexity, self-reproduction, evolution, both through concrete models and through mathematica
MATH-434: Lattice models
Lattice models consist of (typically random) objects living on a periodic graph. We will study some models that are mathematically interesting and representative of physical phenomena seen in the real
Related publications (19)
Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.
Conformal Field Theory at the Lattice Level: Discrete Complex Analysis and Virasoro Structure
Critical statistical mechanics and Conformal Field Theory (CFT) are conjecturally connected since the seminal work of Beliavin et al. (Nucl Phys B 241(2):333-380, 1984). Both exhibit exactly solvable structures in two dimensions. A long-standing question ( ...
SPRINGER2022Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances
Wulfram Gerstner, Clément Hongler, Johanni Michael Brea, Francesco Spadaro, Berfin Simsek, Arthur Jacot
We study how permutation symmetries in overparameterized multi-layer neural networks generate `symmetry-induced' critical points. Assuming a network with layers of minimal widths reaches a zero-loss minimum at $ r_1^*! \c ...
2021Neural Tangent Kernel: Convergence and Generalization in Neural Networks (Invited Paper)
Clément Hongler, Franck Raymond Gabriel, Arthur Jacot
The Neural Tangent Kernel is a new way to understand the gradient descent in deep neural networks, connecting them with kernel methods. In this talk, I'll introduce this formalism and give a number of results on the Neural Tangent Kernel and explain how th ...
ASSOC COMPUTING MACHINERY2021