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:2408. 04619v2 Announce Type: replace-cross Abstract: The Transformer architecture underpins modern large language models powering state-of-the-art text generation and AI applications.
By Aeree Cho, Grace C. Kim, Alexander Karpekov, Seongmin Lee, Alec Helbling, Benjamin Hoover, Zijie J. Wang, Minsuk Kahng, Duen Horng Chau
arXiv:2606. 06624v1 Announce Type: new Abstract: In the current era of deep learning and especially generative models, there is significant investment in training very large generative models.
By San Buchanan, Druv Pai, Peng Wang, Yi Ma
arXiv:2608. 00029v1 Announce Type: cross Abstract: The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physical hardware.
By Adwaid Suresh, Aparna A, Harshini V M, Jona Delcy C A, Killi Uma Maheswara Rao, Ram Charan Golla, Surendra Vendra
arXiv:2604. 07242v3 Announce Type: replace Abstract: Despite deep learning models running well-defined mathematical functions, we lack a formal mathematical framework for describing model architectures.
By Vincent Abbott, Gioele Zardini
arXiv:2607. 00969v1 Announce Type: cross Abstract: Visual analytics (VA) plays an increasingly important role in supporting machine learning (ML) workflows.
By Yiwen Xing, Philip Beaucamp, Joyraj Chakraborty, Afrah Farea, Yuanzhe Jin, Saiful Khan, Gennady Andrienko, Natalia Andrienko, Min Chen