Publications related to Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

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 ...

EPFL Infoscience2024

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

Marco Picasso, Alexandre Caboussat, Maude Girardin

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

Machine Learning for Modeling Stock Returns

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Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

EPFL2024

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 ...

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Sparse autoregressive neural networks for classical spin systems

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Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

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ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

Daniel Gatica-Perez, Sina Sajadmanesh

Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to p ...

Assoc Computing Machinery2024

Deep Learning Generalization with Limited and Noisy Labels

Mahsa Forouzesh

Deep neural networks have become ubiquitous in today's technological landscape, finding their way in a vast array of applications. Deep supervised learning, which relies on large labeled datasets, has been particularly successful in areas such as image cla ...

EPFL2023

Benign Overfitting in Deep Neural Networks under Lazy Training

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Zhenyu Zhu

This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve Bayesoptimal test error for classification while obtaining (nearly) zero-trai ...

2023

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

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

Machine Learning for Modeling Stock Returns

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

Sparse autoregressive neural networks for classical spin systems

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

Deep Learning Generalization with Limited and Noisy Labels

Benign Overfitting in Deep Neural Networks under Lazy Training

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Machine Learning for Modeling Stock Returns

Deep Learning Generalization with Limited and Noisy Labels

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

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

Sparse autoregressive neural networks for classical spin systems

Benign Overfitting in Deep Neural Networks under Lazy Training

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees