Distributed Extra-Gradient With Optimal Complexity And Communication Guarantees

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Fuel performance codes are essential for predicting fuel rod behavior in nuclear reactors. These codes comprise a complex system of models with empirical constants that require calibration against experimental data. To support this calibration, this study ...

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

Machine learning is increasingly integrated with decision making by powering automated systems and informing human decision makers in applications such as CV screening, portfolio allocation, loan risk assessment, and insurance approval. In this thesis, I e ...

Noise in the data is one of the main cause of model performance drop. Denoising is therefore a critical step in most data pipelines. In this paper we propose to fuse the learning abilities of Neural Network with the well-proven efficiency of the wavelet pa ...

Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-perfor ...

The proliferation of microscopy methods for live-cell imaging offers many new possibilities for users but can also be challenging to navigate. The prevailing challenge in live-cell fluorescence microscopy is capturing intra-cellular dynamics while preservi ...

Adversarial Training using a strong first-order adversary (PGD) is the gold standard for training Deep Neural Networks that are robust to adversarial examples. We show that, contrary to the general understanding of the method, the gradient at an optimal ad ...

We propose Grab-UCB, a graph-kernelmulti-arms bandit algorithm to learn online the optimal source placement in large scale networks, such that the reward obtained from a priori unknown network processes is maximized. The uncertainty calls for online learni ...

Reed-Muller (RM) codes achieve the capacity of general binary-input memoryless symmetric channels and are conjectured to have a comparable performance to that of random codes in terms of scaling laws. However, such results are established assuming maximum- ...

Positron emission tomography is a nuclear imaging technique well known for its use in oncology for cancer diagnosis and staging. A PET scanner is a complex machine which comprises photodetectors placed in a ring configuration that detect gamma photons gen ...

The recent advance of large language models (LLMs) demonstrates that these large-scale foundation models achieve remarkable capabilities across a wide range of language tasks and domains. The success of the statistical learning approach challenges our unde ...

Neural tangent kernel (NTK) is a powerful tool to analyze training dynamics of neural networks and their generalization bounds. The study on NTK has been devoted to typical neural network architectures, but it is incomplete for neural networks with Hadamar ...

We develop a novel 2D functional learning framework that employs a sparsity-promoting regularization based on second-order derivatives. Motivated by the nature of the regularizer, we restrict the search space to the span of piecewise-linear box splines shi ...

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), a no ...

A fast hotspot detection technique has been implemented and patented at ecole Polytechnique Federale de Lausanne (EPFL) under the European Union project FastGrid. The optical fibre sensing based technique uses the Mach-Zehnder Interferometer (MZI) and can ...

At the end of Moore's law, new computing paradigms are required to prolong the battery life of wearable and IoT smart audio devices. Theoretical analysis and physical validation have shown that analog signal processing (ASP) can be more power-efficient tha ...

The Hubble constant (H-0) is one of the fundamental parameters in cosmology, but there is a heated debate around the > 4 sigma tension between the local Cepheid distance ladder and the early Universe measurements. Strongly lensed Type Ia supernovae (LSNe I ...

How does reliable computation emerge from networks of noisy neurons? While individual neurons are intrinsically noisy, the collective dynamics of populations of neurons taken as a whole can be almost deterministic, supporting the hypothesis that, in the br ...

Distributed Extra-Gradient With Optimal Complexity And Communication Guarantees

Chattez avec Graph Search

Preliminary Sensitivity Analysis of Time-Dependent Code Outputs in Fuel Performance Models Using OFFBEAT for Future Calibration

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

Robust time series denoising with learnable wavelet packet transform

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

Live-cell imaging powered by computation

Finding Actual Descent Directions For Adversarial Training

Online Network Source Optimization with Graph-Kernel MAB

Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes

Integrated electronics for time-of-flight positron emission tomography photodetectors

Monotonicity Reasoning in the Age of Neural Foundation Models

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

Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

Fast Quench Detection in SFCL Pancake Using Optical Fibre Sensing and Machine Learning

PEAF: Learnable Power Efficient Analog Acoustic Features for Audio Recognition

HOLISMOKES VII. Time-delay measurement of strongly lensed Type Ia supernovae using machine learning

Taming neuronal noise with large networks