arXiv:2607. 16813v1 Announce Type: new Abstract: Sparse-support uncertainty is usually quantified by treating the dictionary as known, an assumption that can produce overconfident, label-dependent conclusions when the dictionary is learned from latent sparse mixtures.
By Guan-Ju Peng
arXiv:2609.07061v2 Announce Type: replace
Abstract: Physics-Informed Neural Networks (PINNs) embed PDE residuals into neural network training, but their internal representations remain opaque: it is...
By Nandita N. Patil, Eshwar R. A., Gajanan V. Honnavar
arXiv:2606. 02385v1 Announce Type: cross Abstract: Sparse Autoencoders (SAEs) have found success parsing neural representations into interpretable concepts, providing a basis for understanding and control.
By William Dorrell
arXiv:2609.07061v1 Announce Type: new
Abstract: Physics-Informed Neural Networks (PINNs) embed PDE residuals into neural network training, but their internal representations remain opaque: it is unkn...
By Nandita N. Patil, Eshwar R. A., Gajanan V. Honnavar
arXiv:2606. 06333v1 Announce Type: new Abstract: Sparse Autoencoders (SAEs) are widely used for mechanistic interpretability in large language models, yet their formulation assigns each latent feature a single decoder direction, implicitly assuming features to be one-dimensional.
By Seyed Arshan Dalili, Mehrdad Mahdavi
arXiv:2607. 11273v1 Announce Type: cross Abstract: Quantum state tomography is sample-starved, and the states one prepares live on a narrow, learnable manifold.
By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao