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
Investigating the Synergistic Effects of Dropout and Residual Connections on Language Model Training
Qingyang Li, Weimao Ke
Advancing Spatio-temporal Storm Surge Prediction with Hierarchical Deep Neural Networks
Saeed Saviz Naeini, Reda Snaiki, Teng Wu
Deep activity propagation via weight initialization in spiking neural networks
Aurora Micheli, Olaf Booij, Jan van Gemert, Nergis Tömen
GARCH-Informed Neural Networks for Volatility Prediction in Financial Markets
Zeda Xu, John Liechty, Sebastian Benthall, Nicholas Skar-Gislinge, Christopher McComb
(Almost) Smooth Sailing: Towards Numerical Stability of Neural Networks Through Differentiable Regularization of the Condition Number
Rossen Nenov, Daniel Haider, Peter Balazs
Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in $L^p$-sense
Ariel Neufeld, Tuan Anh Nguyen
Old Optimizer, New Norm: An Anthology
Jeremy Bernstein, Laker Newhouse
First Order System Least Squares Neural Networks
Joost A. A. Opschoor, Philipp C. Petersen, Christoph Schwab
Constraint Guided Model Quantization of Neural Networks
Quinten Van Baelen, Peter Karsmakers
Aggressive Post-Training Compression on Extremely Large Language Models
Zining Zhang, Yao Chen, Bingsheng He, Zhenjie Zhang
Counter-Current Learning: A Biologically Plausible Dual Network Approach for Deep Learning
Chia-Hsiang Kao, Bharath Hariharan
Automatic debiasing of neural networks via moment-constrained learning
Christian L. Hines, Oliver J. Hines
Nonideality-aware training makes memristive networks more robust to adversarial attacks
Dovydas Joksas, Luis Muñoz-González, Emil Lupu, Adnan Mehonic
Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation
Shiming Fang, Kaiyan Yu
Sequencing the Neurome: Towards Scalable Exact Parameter Reconstruction of Black-Box Neural Networks
Judah Goldfeder, Quinten Roets, Gabe Guo, John Wright, Hod Lipson
Harmonizing knowledge Transfer in Neural Network with Unified Distillation
Yaomin Huang, Zaomin Yan, Chaomin Shen, Faming Fang, Guixu Zhang
Treating Brain-inspired Memories as Priors for Diffusion Model to Forecast Multivariate Time Series
Muyao Wang, Wenchao Chen, Zhibin Duan, Bo Chen