Efficient Distributed Decision Trees for Robust Regression

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A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

Poisoning attacks compromise the training data utilized to train machine learning (ML) models, diminishing their overall performance, manipulating predictions on specific test samples, and implanting backdoors. This article thoughtfully explores these atta ...

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, ...

This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “3D printing of customizable transient bioelectronics and sensors”. This work aims to study and demonstrate ...

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 ...

Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

This document contains all the data and the details of the analysis used in the manuscript titled " Control of Ge island coalescence for the formation of nanowires on silicon." https://doi.org/10.1039/D3NH00573A ...

Utilization of small molecules as passivation materials for perovskite solar cells (PSCs) has gained significant attention recently, with hundreds of small molecules demonstrating passivation effects. In this study, a high-accuracy machine learning model i ...

Discovering new materials is essential but challenging, time-consuming, and expensive. In many cases, simulations can be useful for estimating material properties. For many of the most interesting properties, however, simulations are infeasible because of ...

Efficient Distributed Decision Trees for Robust Regression

Chattez avec Graph Search

Spectral Estimators for High-Dimensional Matrix Inference

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Machine Learning Security Against Data Poisoning: Are We There Yet?

Machine Learning for Modeling Stock Returns

Dataset for '3D printing of customizable transient bioelectronics and sensors'

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Reducing uncertainties in response predictions of earthquake-damaged masonry buildings using data from image-based inspection

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Data set for control of Ge island coalescence for the formation of nanowires on silicon

Machine Learning for Screening Small Molecules as Passivation Materials for Enhanced Perovskite Solar Cells

Toward data-driven materials design: From atoms to pilot plants