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
Evolving Efficient Genetic Encoding for Deep Spiking Neural Networks
Wenxuan Pan, Feifei Zhao, Bing Han, Haibo Tong, Yi Zeng
MP-PINN: A Multi-Phase Physics-Informed Neural Network for Epidemic Forecasting
Thang Nguyen, Dung Nguyen, Kha Pham, Truyen Tran
A Text Classification Model Combining Adversarial Training with Pre-trained Language Model and neural networks: A Case Study on Telecom Fraud Incident Texts
Liu Zhuoxian, Shi Tuo, Hu Xiaofeng
Precision Glass Thermoforming Assisted by Neural Networks
Yuzhou Zhang, Mohan Hua, Haihui Ruan
On the Principles of ReLU Networks with One Hidden Layer
Changcun Huang
Multi-Dimensional Reconfigurable, Physically Composable Hybrid Diffractive Optical Neural Network
Ziang Yin, Yu Yao, Jeff Zhang, Jiaqi Gu
Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
G.J.E. van Otterdijk, S. Moradi, S. Weiland, R. Tóth, N.O. Jaensson, M. Schoukens
On the Role of Noise in AudioVisual Integration: Evidence from Artificial Neural Networks that Exhibit the McGurk Effect
Lukas Grasse, Matthew S. Tata
Controlling Grokking with Nonlinearity and Data Symmetry
Ahmed Salah, David Yevick
Learn to Solve Vehicle Routing Problems ASAP: A Neural Optimization Approach for Time-Constrained Vehicle Routing Problems with Finite Vehicle Fleet
Elija Deineko, Carina Kehrt
From CNN to CNN + RNN: Adapting Visualization Techniques for Time-Series Anomaly Detection
Fabien Poirier
Finding Strong Lottery Ticket Networks with Genetic Algorithms
Philipp Altmann, Julian Schönberger, Maximilian Zorn, Thomas Gabor
Verification of Neural Networks against Convolutional Perturbations via Parameterised Kernels
Benedikt Brückner, Alessio Lomuscio
Uncertainty Prediction Neural Network (UpNet): Embedding Artificial Neural Network in Bayesian Inversion Framework to Quantify the Uncertainty of Remote Sensing Retrieval
Dasheng Fan, Xihan Mu, Yongkang Lai, Donghui Xie, Guangjian Yan
Normalized Space Alignment: A Versatile Metric for Representation Analysis
Danish Ebadulla, Aditya Gulati, Ambuj Singh
Saliency Assisted Quantization for Neural Networks
Elmira Mousa Rezabeyk, Salar Beigzad, Yasin Hamzavi, Mohsen Bagheritabar, Seyedeh Sogol Mirikhoozani
Repairing Neural Networks for Safety in Robotic Systems using Predictive Models
Keyvan Majd, Geoffrey Clark, Georgios Fainekos, Heni Ben Amor
Impact of white noise in artificial neural networks trained for classification: performance and noise mitigation strategies
Nadezhda Semenova, Daniel Brunner