Publications related to Theory of representation learning in cortical neural networks

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...

AMER PHYSICAL SOC2023

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Rongrong Li, Qiaoli Wang, Yu Xu

Water quality prediction in the spatially heterogeneous environment is challenging as the importance of water quality parameters (WQPs) and the performance of prediction models may vary across space. Thus, this study proposed spatially adaptive machine lea ...

ELSEVIER2023

Modeling Visual Impairments with Artificial Neural Networks: a Review

Martin Schrimpf

We present an approach to bridge the gap between the computational models of human vision and the clinical practice on visual impairments (VI). In a nutshell, we propose to connect advances in neuroscience and machine learning to study the impact of VI on ...

Los Alamitos2023

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Vinitra Swamy, Jibril Albachir Frej

Explainable Artificial Intelligence (XAI) plays a crucial role in enabling human understanding and trust in deep learning systems, often defined as determining which features are most important to a model's prediction. As models get larger, more ubiquitous ...

2023

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Martin Jaggi, Thijs Vogels, Hadrien Hendrikx

In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. In the decentralized setting, in which workers commu ...

Brookline2023

PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling

Rachid Guerraoui, Alexandre David Olivier Maurer, Omid Ardakanian

Nowadays, the integration of home automation systems with smart thermostats is a common trend, designed to enhance resident comfort and conserve energy. The introduction of smart thermostats that can run machine learning algorithms has opened the door for ...

Basel2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Emilio Maddalena

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

EPFL2023

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

Christian Leinenbach, Sergey Shevchik, Rafal Wróbel, Marc Leparoux

This study presents a self-supervised Bayesian Neural Network (BNN) framework using air-borne Acoustic Emission (AE) to identify different Laser Powder Bed Fusion (LPBF) process regimes such as Lack of Fusion, conduction mode, and keyhole without ground-tr ...

London2023

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar

There is a strong incentive to develop computational pathology models to i) ease the burden of tissue typology annotation from whole slide histological images; ii) transfer knowledge, e.g., tissue class separability from the withheld source domain to the d ...

2023

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Theory of representation learning in cortical neural networks

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Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Modeling Visual Impairments with Artificial Neural Networks: a Review

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

Modeling Visual Impairments with Artificial Neural Networks: a Review

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Beyond Spectral Gap: The Role of the Topology in Decentralized Learning

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation