Byzantine Machine Learning: A Primer

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Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based methods, are susceptible to altering sentence semantics, leading to invalid adv ...

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 ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

This work presents a novel hybrid (physics- and data -driven) model for short-term (intra-day and day-ahead, 3h -24h) wind power forecasting (STWPF). Traditionally, STWPF predictors admitted very few meteorological variables only from the grid points close ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train them.|S ...

This paper considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with ...

This study focuses on investigating Acoustic Emission (AE) monitoring in the Laser Powder Bed Fusion (LPBF) process, using premixed Ti6Al4V-(x wt%) Fe, where x = 0, 3, and 6. By employing a structure-borne AE sensor, we analyze AE data statistically, uncov ...

Byzantine Machine Learning: A Primer

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Revisiting Character-level Adversarial Attacks for Language Models

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

Partial discharge localization in power transformer tanks using machine learning methods

A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space

Machine Learning for Modeling Stock Returns

Machine learning-aided generative molecular design

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Meta-learning to address diverse Earth observation problems across resolutions

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Impact of Redundancy on Resilience in Distributed Optimization and Learning

Acoustic emission signature of martensitic transformation in laser powder bed fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction