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Finite Bit Quantization For Decentralized Learning Under Subspace Constraints

Publications associées (31)

Timing and spatial selection bias in rapid extreme event attribution

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Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

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A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

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Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been most ...

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A Multimodal Dataset for Automatic Edge-AI Cough Detection

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Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. Automatic cough counting tools must provide accurate information, while runnin ...

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Modeling local thermal responses of individuals: Validation of advanced human thermo-physiology models

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Human thermo-physiology models (HTPM) are useful tools to assess dynamic and non-uniform human thermal states. However, they are developed based on the physiological data of an average person. In this paper, we present a detailed evaluation of two sophisti ...

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Spatially multiplexed single-molecule translocations through a nanopore at controlled speeds

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In current nanopore-based label-free single-molecule sensing technologies, stochastic processes influence the selection of translocating molecule, translocation rate and translocation velocity. As a result, single-molecule translocations are challenging to ...

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An Integrated Approach to Designing Robust Turbocompressors on Gas Bearings Through Surrogate Modeling and Constrained Multi-Objective Optimization

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Arbitrary Decisions are a Hidden Cost of Differentially Private Training

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Mechanisms used in privacy-preserving machine learning often aim to guarantee differential privacy (DP) during model training. Practical DP-ensuring training methods use randomization when fitting model parameters to privacy-sensitive data (e.g., adding Ga ...

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Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

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We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...

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Improving time-resolution and sensitivity of in situ X-ray photoelectron spectroscopy of a powder catalyst by modulated excitation

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Ambient pressure X-ray photoelectron spectroscopy (APXPS) is a powerful tool to characterize the surface structure of heterogeneous catalysts in situ. In order to improve the time resolution and the signal-to-noise (S/N) ratio of photoemission spectra, we ...

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Optimizing Sales Forecasting, Inventory, Pricing and Sourcing Decisions

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In this thesis we address various factors that contribute both theoretically and practically to mitigating supply demand mismatches. The thesis is composed of three chapters, where each chapter is an independent scientific paper. In the first paper, we dev ...

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State-Based Versus Time-Based Estimation of the Gait Phase for Hip Exoskeletons in Steady and Transient Walking

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The growing demand for online gait phase (GP) estimation, driven by advancements in exoskeletons and prostheses, has prompted numerous approaches in the literature. Some approaches explicitly use time, while others rely on state variables to estimate the G ...

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