New Machine
Research on "new machines" broadly encompasses the development and application of machine learning across diverse fields, aiming to improve efficiency, accuracy, and decision-making. Current efforts focus on refining model architectures like convolutional neural networks, gradient boosting machines, and transformers for tasks ranging from image and signal processing to complex prediction and control problems. This research is significant because it drives advancements in various sectors, including healthcare, energy, manufacturing, and transportation, by enabling automated processes, improved diagnostics, and more efficient resource allocation.
Papers
Measuring machine learning harms from stereotypes: requires understanding who is being harmed by which errors in what ways
Angelina Wang, Xuechunzi Bai, Solon Barocas, Su Lin Blodgett
Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification
Soumya Sanyal, Tianyi Xiao, Jiacheng Liu, Wenya Wang, Xiang Ren
Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them
Chao Liu, Boxi Chen, Wei Shao, Chris Zhang, Kelvin Wong, Yi Zhang
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction
Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin, Khondokar Fida Hasan, Selina Sharmin, Salem A. Alyami, Mohammad Ali Moni
The Cadaver in the Machine: The Social Practices of Measurement and Validation in Motion Capture Technology
Emma Harvey, Hauke Sandhaus, Abigail Z. Jacobs, Emanuel Moss, Mona Sloane
Estimation of AMOC transition probabilities using a machine learning based rare-event algorithm
Valérian Jacques-Dumas, René M. van Westen, Henk A. Dijkstra
NN-VVC: Versatile Video Coding boosted by self-supervisedly learned image coding for machines
Jukka I. Ahonen, Nam Le, Honglei Zhang, Antti Hallapuro, Francesco Cricri, Hamed Rezazadegan Tavakoli, Miska M. Hannuksela, Esa Rahtu
Bridging the gap between image coding for machines and humans
Nam Le, Honglei Zhang, Francesco Cricri, Ramin G. Youvalari, Hamed Rezazadegan Tavakoli, Emre Aksu, Miska M. Hannuksela, Esa Rahtu