Neural Network
Neural networks are computational models inspired by the structure and function of the brain, primarily aimed at approximating complex functions and solving diverse problems through learning from data. Current research emphasizes improving efficiency and robustness, exploring novel architectures like sinusoidal neural fields and hybrid models combining neural networks with radial basis functions, as well as developing methods for understanding and manipulating the internal representations learned by these networks, such as through hyper-representations of network weights. These advancements are driving progress in various fields, including computer vision, natural language processing, and scientific modeling, by enabling more accurate, efficient, and interpretable AI systems.
Papers
Low-Power Vibration-Based Predictive Maintenance for Industry 4.0 using Neural Networks: A Survey
Alexandru Vasilache, Sven Nitzsche, Daniel Floegel, Tobias Schuermann, Stefan von Dosky, Thomas Bierweiler, Marvin Mußler, Florian Kälber, Soeren Hohmann, Juergen Becker
Block-Operations: Using Modular Routing to Improve Compositional Generalization
Florian Dietz, Dietrich Klakow
TASI Lectures on Physics for Machine Learning
Jim Halverson
Universal Approximation Theory: Foundations for Parallelism in Neural Networks
Wei Wang, Qing Li
Comgra: A Tool for Analyzing and Debugging Neural Networks
Florian Dietz, Sophie Fellenz, Dietrich Klakow, Marius Kloft
SmileyNet -- Towards the Prediction of the Lottery by Reading Tea Leaves with AI
Andreas Birk
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product
Neil Mallinar, Daniel Beaglehole, Libin Zhu, Adityanarayanan Radhakrishnan, Parthe Pandit, Mikhail Belkin
rLLM: Relational Table Learning with LLMs
Weichen Li, Xiaotong Huang, Jianwu Zheng, Zheng Wang, Chaokun Wang, Li Pan, Jianhua Li
Application of Unsupervised Artificial Neural Network (ANN) Self_Organizing Map (SOM) in Identifying Main Car Sales Factors
Mazyar Taghavi
From Flat to Spatial: Comparison of 4 methods constructing 3D, 2 and 1/2D Models from 2D Plans with neural networks
Jacob Sam, Karan Patel, Mike Saad
AI-Powered Energy Algorithmic Trading: Integrating Hidden Markov Models with Neural Networks
Tiago Monteiro
Generalization bounds for regression and classification on adaptive covering input domains
Wen-Liang Hwang
Neural networks for bifurcation and linear stability analysis of steady states in partial differential equations
Muhammad Luthfi Shahab, Hadi Susanto
HENNC: Hardware Engine for Artificial Neural Network-based Chaotic Oscillators
Mobin Vaziri, Shervin Vakili, M. Mehdi Rahimifar, J. M. Pierre Langlois
Decomposing heterogeneous dynamical systems with graph neural networks
Cédric Allier, Magdalena C. Schneider, Michael Innerberger, Larissa Heinrich, John A. Bogovic, Stephan Saalfeld
Artificial neural networks on graded vector spaces
T. Shaska
Interpreting artificial neural networks to detect genome-wide association signals for complex traits
Burak Yelmen, Maris Alver, Estonian Biobank Research Team, Flora Jay, Lili Milani
Learning production functions for supply chains with graph neural networks
Serina Chang, Zhiyin Lin, Benjamin Yan, Swapnil Bembde, Qi Xiu, Chi Heem Wong, Yu Qin, Frank Kloster, Alex Luo, Raj Palleti, Jure Leskovec