Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

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Noise detrimentally affects quantum computations so that they not only become less accurate but also easier to simulate classically as systems scale up. We construct a classical simulation algorithm, lowesa (low weight efficient simulation algorithm), for ...

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Biomolecular condensates are cellular organelles without a membrane, which have come into the scientific focus about 15 years ago. They are made from a mix of proteins and RNAs. It is hypothesized that they maintain their shape and function through liquid- ...

For high-dimensional Gaussian data, we investigate theoretically how the features of a two-layer neural network adapt to the structure of the target function through a few large batch gradient descent steps, leading to an improvement in the approximation c ...

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have ...

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This paper systematically analyzes the security risks associated with artificial intelligence (AI) components in autonomous vehicles (AVs). Given the increasing reliance on AI for various AV functions, from perception to control, the potential for security ...

Learning from Demonstration (LfD) stands as an efficient framework for imparting human-like skills to robots. Nevertheless, designing an LfD framework capable of seamlessly imitating, generalizing, and reacting to disturbances for long-horizon manipulation ...

The social learning paradigm for distributed hypothesis testing involves a collection of agents interacting over a graph, each observing streaming data that provide evidence about the state of the environment. These agents perform a series of local Bayesia ...

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, int ...

In the field of model predictive control, Dataenabled Predictive Control (DeePC) offers direct predictive control, bypassing traditional modeling. However, challenges emerge with increased computational demand due to recursive data updates. This paper intr ...

The framework of approximate differential privacy is considered, and augmented by leveraging the notion of "the total variation of a (privacy-preserving) mechanism" (denoted by eta-TV). With this refinement, an exact composition result is derived, and show ...

Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

Chattez avec Graph Search

Classical simulations of noisy variational quantum circuits

Ion Sieving in Two-Dimensional Membranes from First Principles

Surface distributions and vertical profiles of trace gases (CO, O3, NO, NO2) in the Arctic wintertime boundary layer using low-cost sensors during ALPACA-2022

Combinatorial Aspects of Continuous Optimization: Graph Polynomials and Neural Networks

Learning Hand State Estimation for a Light Exoskeleton

Optimization frameworks for bespoke sensory encoding in neuroprosthetics

Non-equilibrium control of biomolecular condensates

How Two-Layer Neural Networks Learn, One (Giant) Step at a Time

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Simultaneous Quantification of Multiple Drugs by Machine Learning on Electrochemical Sensors

Aircraft Trajectory Planning for Climate Hotspot Avoidance Considering Air Traffic Complexity: A Constrained Multi-Agent Reinforcement Learning Approach

A PAC-Bayesian Framework for Optimal Control with Stability Guarantees

Euclid preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy

A qualitative AI security risk assessment of autonomous vehicles

Logic Learning From Demonstrations for Multi-Step Manipulation Tasks in Dynamic Environments

Inverse Graph Learning from Social Interactions

Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons

Efficient Recursive Data-enabled Predictive Control

Total Variation Meets Differential Privacy