by instadatahelp | Sep 4, 2023 | AI Blogs
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...
![Unveiling Imperceptible Adversarial Perturbations for Top-$k$ Multi-Label Learning in the Presence of Unreliable Measures. (arXiv:2309.00007v1 [cs.CV])](https://instadatanews.com/wp-content/uploads/2023/08/arxiv-logo-fb-1080x675.png)
by instadatahelp | Sep 4, 2023 | AI Blogs
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...
by instadatahelp | Sep 4, 2023 | AI Blogs
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,...
by instadatahelp | Sep 4, 2023 | AI Blogs
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,...
by instadatahelp | Sep 4, 2023 | AI Blogs
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...
