Category AI Blogs

AI Blogs

AI Blogs

Deep Reinforcement Learning for Cellular-Connected UAV Trajectory Design in Rainy Environments: A Physics-Based Approach (arXiv:2309.00017v1 [eess.SY])

The focus on cellular-connected unmanned aerial vehicles (UAVs) has been growing due to their ability to enhance conventional UAV capabilities by utilizing existing cellular infrastructure for reliable communication with base stations. These UAVs have been utilized in various applications such…

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September 4, 2023

AI Blogs

Estimating Determinants of Kernel Matrices using Stopped Cholesky Decomposition

Estimating Kernel-Matrix Determinants using Stopped Cholesky Decomposition Authors: Simon Bartels, Wouter Boomsma, Jes Frellsen, Damien Garreau; Published in Journal of Machine Learning Research, 24(71):1−57, 2023. Abstract Many algorithms involving Gaussian processes or determinantal point processes require the computation of the…

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September 4, 2023

AI Blogs

Improvement in Differentially Private Image Generation Quality with Large-Scale Public Data

The content below has been rewritten: arXivLabs is a platform where collaborators can collaborate and create new features for arXiv directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of transparency,…

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September 4, 2023

AI Blogs

Regression: A Comprehensive Framework and Benchmark for Deep Batch Active Learning

A Framework and Benchmark for Deep Batch Active Learning for Regression David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart; 24(164):1−81, 2023. Abstract This study focuses on active learning methods for improving the sample efficiency of neural network regression by adaptively…

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September 4, 2023

AI Blogs

Unveiling Imperceptible Adversarial Perturbations for Top-$k$ Multi-Label Learning in the Presence of Unreliable Measures. (arXiv:2309.00007v1 [cs.CV])

Adversarial learning has gained significant attention in various studies due to the success of deep neural networks. However, existing adversarial attacks in multi-label learning only focus on visual imperceptibility and overlook the perceptible issue related to measures such as Precision@$k$…

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September 4, 2023

AI Blogs

Inferencing for an Extensive Directed Acyclic Graph with Unspecified Interventions

Inference for a Large Directed Acyclic Graph with Unspecified Interventions Chunlin Li, Xiaotong Shen, Wei Pan; 24(73):1−48, 2023. Abstract The statistical inference of directed relations, given unspecified interventions where the intervention targets are unknown, presents a significant challenge. In this…

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September 4, 2023

AI Blogs

A Synthetic HyperSpectral Dataset with High Spectral Spatial Resolution Achieved through Multi-Source Fusion. (arXiv:2309.00005v1 [cs.CV])

This research paper introduces a novel synthetic hyperspectral dataset that overcomes the limitations of relying on a single camera for high spectral and spatial resolution imaging. The dataset combines three modalities: RGB, push-broom visible hyperspectral camera, and snapshot infrared hyperspectral…

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September 4, 2023

AI Blogs

Effective Approaches for Linear Learning in High-Dimensional Spaces

Robust Methods for High-Dimensional Linear Learning Ibrahim Merad, Stéphane Gaïffas; 24(165):1−44, 2023. Abstract This paper presents statistically robust and computationally efficient linear learning methods for high-dimensional batch settings, where the number of features (d) may exceed the sample size (n).…

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September 4, 2023

AI Blogs

Density-based Metric Learning for Intrinsic Persistent Homology

Intrinsic Persistent Homology via Density-based Metric Learning Authors: Ximena Fernández, Eugenio Borghini, Gabriel Mindlin, Pablo Groisman; Published in: Journal of Machine Learning Research, Volume 24, Pages 1-42, 2023. Abstract This study focuses on estimating topological features from data in high…

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September 4, 2023

AI Blogs

Decentralized Weakly Convex Optimization with Moreau Envelope ADMM

This paper introduces a modified version of the alternating direction method of multipliers (ADMM) for distributed optimization. While the current ADMM algorithms have shown promising results in finding near-optimal solutions for various convex and non-convex optimization problems, it is still…

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September 3, 2023

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