Publications related to Machine learning

OASIS: An integrated optimisation framework for activity scheduling

Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...

EPFL2024

How to Support Students to Cultivate Psychological Safety in their Teams

Francesco Mondada, Yousef Jalali

The importance of psychological safety in teams has been demonstrated in different settings. Team performance improves when team climate allows team members to be their authentic selves, to ask questions and raise concerns comfortably. However, it is less ...

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

Solvation Free Energies from Machine Learning Molecular Dynamics

Nicola Marzari, Nicéphore Arthur François Bonnet

The present work proposes an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approa ...

Amer Chemical Soc2024

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

Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Wenlong Liao, Qi Liu, Zhe Yang

Reactive power optimization of distribution networks is traditionally addressed by physical model based methods, which often lead to locally optimal solutions and require heavy online inference time consumption. To improve the quality of the solution and r ...

State Grid Electric Power Research Inst2024

NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection

Lijing Xin, Yubo Zhao, Yan Lin, Wei Ye

Metabolic changes precede malignant histology. However, it remains unclear whether detectable characteristic metabolome exists in esophageal squamous cell carcinoma (ESCC) tissues and biofluids for early diagnosis. Here, we conduct NMR- and MS-based metabo ...

Nature Portfolio2024

Context-Aware Monitoring of Hand Motor Primitives in Daily Life Activities: A Pathway to Objectively Assess Rehabilitation of Patients with Upper-Limb Neurological Impairment

The hands, our silent performers in daily life, face overwhelming challenges when neurological impairments disrupt the simple tasks that compose our daily symphony. This thesis unveils a comprehensive framework for the objective monitoring of upper-limb re ...

EPFL2024

Towards practical reinforcement learning for tokamak magnetic control

Federico Alberto Alfredo Felici, Cristian Galperti, Jonas Buchli, Brendan Tracey

Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbacks compared to traditional feedback control approaches for magnetic confinem ...

Lausanne2024

Ambulatory seizure detection

Jonathan Dan, Adriano Bernini

Purpose of review: To review recent advances in the field of seizure detection in ambulatory patients with epilepsy. Recent findings: Recent studies have shown that wrist or arm wearable sensors, using 3D-accelerometry, electrodermal activity or photopleth ...

2024

Machine learning

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OASIS: An integrated optimisation framework for activity scheduling

How to Support Students to Cultivate Psychological Safety in their Teams

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Solvation Free Energies from Machine Learning Molecular Dynamics

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection

Context-Aware Monitoring of Hand Motor Primitives in Daily Life Activities: A Pathway to Objectively Assess Rehabilitation of Patients with Upper-Limb Neurological Impairment

Towards practical reinforcement learning for tokamak magnetic control

Ambulatory seizure detection

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Context-Aware Monitoring of Hand Motor Primitives in Daily Life Activities: A Pathway to Objectively Assess Rehabilitation of Patients with Upper-Limb Neurological Impairment

Towards practical reinforcement learning for tokamak magnetic control

Ambulatory seizure detection

OASIS: An integrated optimisation framework for activity scheduling

How to Support Students to Cultivate Psychological Safety in their Teams

Solvation Free Energies from Machine Learning Molecular Dynamics

Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection