Patching in Order: Efficient On-Device Model Fine-Tuning for Multi-DNN Vision Applications

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Macroeconomic Conditioned Synthetic Financial Markets

Alexander Michael Rusnak

The creation of high fidelity synthetic data has long been an important goal in machine learning, particularly in fields like finance where the scarcity of available training and test data make it difficult to utilize many of the deep learning techniques w ...

ACM2024

Using Motion Cues to Supervise Single-Frame Body Pose and Shape Estimation in Low Data Regimes

Pascal Fua, Mathieu Salzmann, Andrey Davydov

When enough annotated training data is available, supervised deep-learning. algorithms excel at estimating human body pose and shape using a single camera. The effects of too little such data being available can be mitigated by using other information sour ...

Journal of Machine Learning Research Inc.2024

Understanding generalization and robustness in modern deep learning

Maksym Andriushchenko

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

Optimal distributed control with stability guarantees by training a network of neural closed-loop maps

Giancarlo Ferrari Trecate, Luca Furieri, Danilo Saccani, Leonardo Massai

This paper proposes a novel approach to improve the performance of distributed nonlinear control systems while preserving stability by leveraging Deep Neural Networks (DNNs). We build upon the Neural System Level Synthesis (Neur-SLS) framework and introduc ...

Institute of Electrical and Electronics Engineers Inc.2024

A generalized machine learning framework to estimate fatigue life across materials with minimal data

Anastasios Vassilopoulos, Dharun Vadugappatty Srinivasan

In this research, a generalized machine learning (ML) framework is proposed to estimate the fatigue life of epoxy polymers and additively manufactured AlSi10Mg alloy materials, leveraging their failure surface void characteristics. An extreme gradient boos ...

2024

Electronic Excited States from Physically Constrained Machine Learning

Michele Ceriotti, Jigyasa Nigam, Divya Suman

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly w ...

2024

Learning-based Hand Gesture Classification using Channel Impulse Response with UWB

Clement Weihao Samanos, Han Miao, Andreas Peter Burg, Sitian Li

The channel impulse response (CIR) of the wireless propagation channel is influenced by the surrounding environment and thus can be used to retrieve environmental information such as location, the presence of objects, and the speed of objects. In this work ...

Institute of Electrical and Electronics Engineers Inc.2024

A two-phase approach for leak detection and localization in water distribution systems using wavelet decomposition and machine learning

Jérôme Chenal

Water is a crucial resource for all forms of life, yet it is becoming increasingly scarce. A significant portion of water loss in urban and industrial areas is attributed to leaks. Addressing this issue is critical for enhancing efficiency, sustainability, ...

Elsevier BV2024