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
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
arXiv:2606. 17120v1 Announce Type: new Abstract: Deep neural networks (DNNs) exhibit first order phase transitions under variations of the L2 regularization strength, with each transition marking the onset of a new learnable feature.
By Ibrahim Talha Ersoy, Karoline Wiesner