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 they give us insight into the dynamics of neural networks during training and into their generalization features
This thesis aims to study recent theoretical work in machine learning research that seeks to better ...
Graph neural networks (GNNs) achieve remarkable performance in graph machine learning tasks but can ...
A soft tree is an actively studied variant of a decision tree that updates splitting rules using the...
In the recent years, Deep Neural Networks (DNNs) have managed to succeed at tasks that previously ap...
International audienceState-of-the-art neural networks are heavily over-parameterized, making the op...
Recently people showed that wide neural networks can be approximated by linear models under gradient...
Recently people showed that wide neural networks can be approximated by linear models under gradient...
Deep learning has become an important toolkit for data science and artificial intelligence. In contr...
Recently, several studies have proven the global convergence and generalization abilities of the gra...
Deep learning has become an important toolkit for data science and artificial intelligence. In contr...
A rising trend in theoretical deep learning is to understand why deep learning works through Neural ...
Recent work by Jacot et al. (2018) has shown that training a neural network using gradient descent i...
International audienceState-of-the-art neural networks are heavily over-parameterized, making the op...
Neural Tangent Kernel (NTK) is widely used to analyze overparametrized neural networks due to the fa...
The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide memorization, optimization...
This thesis aims to study recent theoretical work in machine learning research that seeks to better ...
Graph neural networks (GNNs) achieve remarkable performance in graph machine learning tasks but can ...
A soft tree is an actively studied variant of a decision tree that updates splitting rules using the...
In the recent years, Deep Neural Networks (DNNs) have managed to succeed at tasks that previously ap...
International audienceState-of-the-art neural networks are heavily over-parameterized, making the op...
Recently people showed that wide neural networks can be approximated by linear models under gradient...
Recently people showed that wide neural networks can be approximated by linear models under gradient...
Deep learning has become an important toolkit for data science and artificial intelligence. In contr...
Recently, several studies have proven the global convergence and generalization abilities of the gra...
Deep learning has become an important toolkit for data science and artificial intelligence. In contr...
A rising trend in theoretical deep learning is to understand why deep learning works through Neural ...
Recent work by Jacot et al. (2018) has shown that training a neural network using gradient descent i...
International audienceState-of-the-art neural networks are heavily over-parameterized, making the op...
Neural Tangent Kernel (NTK) is widely used to analyze overparametrized neural networks due to the fa...
The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide memorization, optimization...
This thesis aims to study recent theoretical work in machine learning research that seeks to better ...
Graph neural networks (GNNs) achieve remarkable performance in graph machine learning tasks but can ...
A soft tree is an actively studied variant of a decision tree that updates splitting rules using the...