Researchers at Tsinghua University have posted their preprint on arXiv addressing one of AI interpretability's most persistent scaling problems: the inability to label millions of sparse autoencoder ...

Untethered thin-film neurostimulator wrapped around tiny nerve trunks for wireless neuromodulation In the human brain, dendrites exhibit nonlinear integration and sparse parallel processing ...

ABSTRACT: Sparse identification of nonlinear dynamical systems is an important project, directly addressing the physics community’s long-standing goal of data-driven discovery. Although many effective ...

Non-negative matrix factorization (NMF) is a workhorse for biomedical data: it produces interpretable parts-based decompositions of count or abundance matrices (gene-association, phenotype, ...

Large language models are remarkably capable, yet frustratingly opaque. When a model misbehaves — generating responses in the wrong language, repeating itself endlessly, or refusing safe requests — AI ...

Abstract: The rapid growth in the size of deep learning models strains the capabilities of dense computation paradigms. Leveraging sparse computation has become increasingly popular for training and ...

Google's TorchTPU aims to enhance TPU compatibility with PyTorch Google seeks to help AI developers reduce reliance on Nvidia's CUDA ecosystem TorchTPU initiative is part of Google's plan to attract ...

Important Note: This repository implements SVG-T2I, a text-to-image diffusion framework that performs visual generation directly in Visual Foundation Model (VFM) representation space, rather than ...

Large language models store knowledge in complex ways that make it difficult to understand or manipulate, but researchers are now developing methods to bring greater clarity to this process. Minglai ...

Abstract: Epilepsy is a medical condition characterized by sudden and frequent sensory disruptions which is commonly detected by electroencephalogram (EEG) analysis. However, analyzing these signals ...