Publications related to Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

Lausanne2024

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Volkan Cevher, Grigorios Chrysos, Fanghui Liu

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

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A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

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The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

San Diego2024

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

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We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Lausanne2024

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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-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Robust machine learning for neuroscientific inference

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Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

EPFL2024

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

Residual-based attention in physics-informed neural networks

Nikolaos Stergiopoulos, Sokratis Anagnostopoulos

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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Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

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The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...

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InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Martin Jaggi, Vinitra Swamy, Jibril Albachir Frej, Julian Thomas Blackwell

Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i.e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance. Most exist ...

2024

Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo

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Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Generalization of Scaled Deep ResNets in the Mean-Field Regime

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Robust machine learning for neuroscientific inference

Incorporating Projective Geometry into Deep Learning

Residual-based attention in physics-informed neural networks

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Robust machine learning for neuroscientific inference

Incorporating Projective Geometry into Deep Learning

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Residual-based attention in physics-informed neural networks

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts