International audienceWe describe the recent developments in using machine learning techniques to compute Hodge numbers of complete intersection Calabi-Yau (CICY) 3- and 4-folds. The main motivation is to understand how to study data from algebraic geometry and solve problems relevant for string theory with machine learning. We describe the state-of-the art methods which reach near-perfect accuracy for several Hodge numbers, and discuss extrapolating from low to high Hodge numbers, and conversely
We employ machine learning techniques to investigate the volume minimum of Sasaki-Einstein base mani...
We introduce neural networks (NNs) to compute numerical Ricci-flat Calabi-Yau (CY) metrics for compl...
We employ machine learning techniques to investigate the volume minimum of Sasaki-Einstein base mani...
International audienceWe describe the recent developments in using machine learning techniques to co...
International audienceWe review advancements in deep learning techniques for complete intersection C...
International audienceWe continue earlier efforts in computing the dimensions of tangent space cohom...
International audienceWe continue earlier efforts in computing the dimensions of tangent space cohom...
International audienceWe introduce a neural network inspired by Google's Inception model to compute ...
In these lecture notes, we survey the landscape of Calabi-Yau threefolds, and the use of machine lea...
The goal of this thesis is to review and investigate recent applications of machine learning to prob...
The latest techniques from Neural Networks and Support Vector Machines (SVM) are used to investigate...
I will describe a large scale study of Calabi-Yau hypersurfaces in toric varieties. We construct lar...
We use machine learning to approximate Calabi-Yau and SU(3)-structure metrics, including for the fir...
Supervised machine learning can be used to predict properties of string geometries with previously u...
We revisit the classic database of weighted-P4s which admit Calabi-Yau 3-fold hypersurfaces equipped...
We employ machine learning techniques to investigate the volume minimum of Sasaki-Einstein base mani...
We introduce neural networks (NNs) to compute numerical Ricci-flat Calabi-Yau (CY) metrics for compl...
We employ machine learning techniques to investigate the volume minimum of Sasaki-Einstein base mani...
International audienceWe describe the recent developments in using machine learning techniques to co...
International audienceWe review advancements in deep learning techniques for complete intersection C...
International audienceWe continue earlier efforts in computing the dimensions of tangent space cohom...
International audienceWe continue earlier efforts in computing the dimensions of tangent space cohom...
International audienceWe introduce a neural network inspired by Google's Inception model to compute ...
In these lecture notes, we survey the landscape of Calabi-Yau threefolds, and the use of machine lea...
The goal of this thesis is to review and investigate recent applications of machine learning to prob...
The latest techniques from Neural Networks and Support Vector Machines (SVM) are used to investigate...
I will describe a large scale study of Calabi-Yau hypersurfaces in toric varieties. We construct lar...
We use machine learning to approximate Calabi-Yau and SU(3)-structure metrics, including for the fir...
Supervised machine learning can be used to predict properties of string geometries with previously u...
We revisit the classic database of weighted-P4s which admit Calabi-Yau 3-fold hypersurfaces equipped...
We employ machine learning techniques to investigate the volume minimum of Sasaki-Einstein base mani...
We introduce neural networks (NNs) to compute numerical Ricci-flat Calabi-Yau (CY) metrics for compl...
We employ machine learning techniques to investigate the volume minimum of Sasaki-Einstein base mani...