Publications related to Learning ground states of gapped quantum Hamiltonians with Kernel Methods

Residual-based attention in physics-informed neural networks

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Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

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Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

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Recently, remarkable progress has been made in the application of machine learning (ML) techniques (e.g., neural networks) to transformer fault diagnosis. However, the diagnostic processes employed by these techniques often suffer from a lack of interpreta ...

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Incorporating Projective Geometry into Deep Learning

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In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

EPFL2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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EPFL2024

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

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In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI

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Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

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Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

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Quantum Error Correction and Simulation in Open Bosonic Systems

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Quantum computers have the potential to surpass conventional computing, but they are hindered by noise which induces errors that ultimately lead to the loss of quantum information. This necessitates the development of quantum error correction strategies fo ...

EPFL2024

On the Generalization of Stochastic Gradient Descent with Momentum

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While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

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Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

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In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Learning ground states of gapped quantum Hamiltonians with Kernel Methods

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Residual-based attention in physics-informed neural networks

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Incorporating Projective Geometry into Deep Learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Quantum Error Correction and Simulation in Open Bosonic Systems

On the Generalization of Stochastic Gradient Descent with Momentum

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Residual-based attention in physics-informed neural networks

Explainable Fault Diagnosis of Oil-Immersed Transformers: A Glass-Box Model

Incorporating Projective Geometry into Deep Learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

On the Generalization of Stochastic Gradient Descent with Momentum

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Quantum Error Correction and Simulation in Open Bosonic Systems