Publications related to Predictive Analysis of Errors During Robot-Mediated Gamified Training

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

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 ...

2024

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

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The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Oxford Univ Press2024

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

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The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

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

Topics in statistical physics of high-dimensional machine learning

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In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

EPFL2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

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

2

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

EPFL2024

Accelerometry as a tool for measuring the effects of transcranial magnetic stimulation

Pierre Theopistos Vassiliadis, Julie Duqué

Objective: We predicted that accelerometry would be a viable alternative to electromyography (EMG) for assessing fundamental Transcranial Magnetic Stimulation (TMS) measurements (e.g. Resting Motor Threshold (RMT), recruitment curves, latencies). New Metho ...

Elsevier2024

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 ...

2024

Predictive Analysis of Errors During Robot-Mediated Gamified Training

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

Machine Learning for Modeling Stock Returns

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

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Robust machine learning for neuroscientific inference

Topics in statistical physics of high-dimensional machine learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Accelerometry as a tool for measuring the effects of transcranial magnetic stimulation

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

Machine Learning for Modeling Stock Returns

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Robust machine learning for neuroscientific inference

Topics in statistical physics of high-dimensional machine learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Accelerometry as a tool for measuring the effects of transcranial magnetic stimulation