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
Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling
Nikolaos Evangelou, Tianqi Cui, Juan M. Bello-Rivas, Alexei Makeev, Ioannis G. Kevrekidis
Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
Jung-Woo Chang, Ke Sun, Nasimeh Heydaribeni, Seira Hidano, Xinyu Zhang, Farinaz Koushanfar
Machine learning for accuracy in density functional approximations
Johannes Voss
A Machine Learning-Based Framework for Clustering Residential Electricity Load Profiles to Enhance Demand Response Programs
Vasilis Michalakopoulos, Elissaios Sarmas, Ioannis Papias, Panagiotis Skaloumpakas, Vangelis Marinakis, Haris Doukas
MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Peikun Zheng, Yuxinxin Chen, Mario Barbatti, Olexandr Isayev, Cheng Wang, Bao-Xin Xue, Max Pinheiro, Yuming Su, Yiheng Dai, Yangtao Chen, Lina Zhang, Shuang Zhang, Arif Ullah, Quanhao Zhang, Yanchi Ou
Exploring a new machine learning based probabilistic model for high-resolution indoor radon mapping, using the German indoor radon survey data
Eric Petermann, Peter Bossew, Joachim Kemski, Valeria Gruber, Nils Suhr, Bernd Hoffmann
Spatially-resolved hyperlocal weather prediction and anomaly detection using IoT sensor networks and machine learning techniques
Anita B. Agarwal, Rohit Rajesh, Nitin Arul