arXiv:2506. 08764v3 Announce Type: replace Abstract: Deep neural networks are known to suffer from exploding or vanishing gradients as depth increases, a phenomenon closely tied to the spectral behavior of the input-output Jacobian.
By Benjamin Dadoun, Soufiane Hayou, Hanan Salam, Mohamed El Amine Seddik, Pierre Youssef
arXiv:2602. 10949v2 Announce Type: replace-cross Abstract: Effective initialization in deep networks requires an understanding of random neural networks.
By Constantin Kogler, Tassilo Schwarz, Samuel Kittle
arXiv:2608. 14638v1 Announce Type: new Abstract: In this paper we study autoencoders, a special class of deep neural nets (DNNs) whose performance can be characterized via their fixed points.
By Leonid Berlyand, Roman Sarapin, Yitzchak Shmalo, Victor Slavin, Sasha Sodin
arXiv:2608. 04442v1 Announce Type: new Abstract: Robustness to natural corruptions remains a fundamental challenge for deep neural networks.
By Jiangang Yang, Wenhui Shi, Lu Hu, Jing Xing, Jian Liu
The paper introduces a new method called uniform‑phase initialization for deep neural networks with sine activations, eliminating the need for the Central Limit Theorem and fully decoupling layers. This approach avoids distributional approximation errors and coupling between layers, leading to stable weight initialization. Experiments show that models using this initialization outperform state‑of‑the‑art methods on image and audio fitting tasks and remain competitive without tuning, while also supporting μP width scaling.
By Simon Kuang, Kyle Chickering, Xinfan Lin
arXiv:2605. 29547v2 Announce Type: replace-cross Abstract: Deep learning optimization relies heavily on the assumption of smooth loss landscapes, a condition systematically violated by modern architectures due to non-smooth components such as ReLU activations and quantization operators.
By Ruoran Xu, Borong She, Xiaobo Jin, Qiufeng Wang