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
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 ConvRNN: 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
Graph neural networks and non-commuting operators
Mauricio Velasco, Kaiying O'Hare, Bernardo Rychtenberg, Soledad Villar
Weighted Sobolev Approximation Rates for Neural Networks on Unbounded Domains
Ahmed Abdeljawad, Thomas Dittrich
Problem Space Transformations for Generalisation in Behavioural Cloning
Kiran Doshi, Marco Bagatella, Stelian Coros
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset
Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
Flexible task abstractions emerge in linear networks with fast and bounded units
Kai Sandbrink, Jan P. Bauer, Alexandra M. Proca, Andrew M. Saxe, Christopher Summerfield, Ali Hummos
A Subsampling Based Neural Network for Spatial Data
Debjoy Thakur
Designing a Linearized Potential Function in Neural Network Optimization Using Csiszár Type of Tsallis Entropy
Keito Akiyama