arXiv:2606. 19850v1 Announce Type: cross Abstract: Deep neural networks (DNNs) exhibit attractive performance in various fields but often suffer from low interpretability.
By Yasutoshi Kishimoto, Kota Yamanishi, Takuya Matsuda, Shinichi Shirakawa
arXiv:2606. 18326v1 Announce Type: new Abstract: The application of machine learning models in practical tasks faces challenges such as class imbalance and multidimensional noise.
By Evgeny Nikulchev, Dmitry Ilin
arXiv:2607. 09235v1 Announce Type: cross Abstract: The current state of the art in AI/ML rests on deep neural architectures, which, in general, suffer from a lack of interpretability.
By Jayadeva, Madhur Aswani
arXiv:2606. 00157v1 Announce Type: cross Abstract: We consider establishing the interpretability theory of deep learning through constructing a corresponding relationship between the renormalization group (RG) method in statistical physics and the training process of deep neural networks (DNNs).
By Fuzhou Gong, Zigeng Xia
arXiv:2601. 06441v2 Announce Type: replace Abstract: Learning activation functions has emerged as a promising direction in deep learning, allowing networks to adapt activation mechanisms to task-specific demands.
By Ramnath Kumar, Kyle Ritscher, Junmin Judy, Lawrence Liu, Cho-Jui Hsieh
arXiv:2606. 06772v1 Announce Type: cross Abstract: Understanding the generalization performance of over-parameterized neural networks has become a central topic in deep learning theory.
By Junyu Zhou, Puyu Wang, Yunwen Lei, Marius Kloft, Yiming Ying