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07/05/2023
From NeurODEs to AutoencODEs: a mean-field control framework for width-varying Neural Networks
In our work, we build upon the established connection between Residual N...
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06/16/2023
Gradient is All You Need?
In this paper we provide a novel analytical perspective on the theoretic...
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11/08/2022
Finite Sample Identification of Wide Shallow Neural Networks with Biases
Artificial neural networks are functions depending on a finite number of...
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11/15/2021
Convergence of Anisotropic Consensus-Based Optimization in Mean-Field Law
In this paper we study anisotropic consensus-based optimization (CBO), a...
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03/28/2021
Consensus-based optimization methods converge globally in mean-field law
In this paper we study consensus-based optimization (CBO), which is a mu...
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01/18/2021
Stable Recovery of Entangled Weights: Towards Robust Identification of Deep Neural Networks from Minimal Samples
In this paper we approach the problem of unique and stable identifiabili...
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01/31/2020
Consensus-Based Optimization on the Sphere I: Well-Posedness and Mean-Field Limit
We introduce a new stochastic Kuramoto-Vicsek-type model for global opti...
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01/31/2020
Consensus-based Optimization on the Sphere II: Convergence to Global Minimizers and Machine Learning
We present the implementation of a new stochastic Kuramoto-Vicsek-type m...
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11/01/2019
Data-driven Evolutions of Critical Points
In this paper we are concerned with the learnability of energies from da...
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06/30/2019
Robust and Resource Efficient Identification of Two Hidden Layer Neural Networks
We address the structure identification and the uniform approximation of...
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04/04/2018
Identification of Shallow Neural Networks by Fewest Samples
We address the uniform approximation of sums of ridge functions ∑_i=1^m ...
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08/18/2010