arXiv:2607. 04262v1 Announce Type: new Abstract: Convolutional Neural Network (CNN) and Vision Transformer (ViT) for image classification exploit a dense grid of pixels containing redundant information.
By Sarabeshwar Balaji, Shubham Mohanty, Akash Anil
arXiv:2610.20326v1 Announce Type: new
Abstract: Integrated Sensing and Communication (ISAC) systems rely on accurate Direction-of-Arrival (DoA) estimation for effective beamforming and spectral-effic...
By Anjali K. S., Manoj Kumar Panda, Chandramani Singh
arXiv:2607. 22872v1 Announce Type: new Abstract: Post-training quantization (PTQ) has become a practical solution for deploying deep learning models on resource-constrained edge devices by compressing high-precision floating-point weights into low-precision representations without requiring retraining.
By Kazi Kamruzzaman Rabbi, Md. Zami Al Zunaed Farabe, M. Sohel Rahman
arXiv:2606. 00081v1 Announce Type: cross Abstract: Distributed Acoustic Sensing (DAS) enables large-scale monitoring through optical fibers, but its high dimensionality and complex spatio-temporal patterns make event classification demanding.
By Michel Dione (CERI SN - IMT Nord Europe), Jerry Lonlac (CERI SN - IMT Nord Europe), H\'el\`ene Louis (CERI SN - IMT Nord Europe), Anthony Fleury (CERI SN - IMT Nord Europe), Stephane Lecoeuche
arXiv:2606. 28654v1 Announce Type: cross Abstract: Deep Neural Network (DNN) classifiers suffer from poor calibration when their softmax outputs (predictive confidence) deviate from the empirical likelihoods.
By Thiru Thillai Nadarasar Bahavan, Sachith Seneviratne, Saman Halgamuge
Post-training quantization (PTQ) has become a practical solution for deploying deep learning models on resource-constrained edge devices by compressing high-precision floating-point weights into low-precision representations without requiring retraining. Past research has demonstrated that quantization largely preserves classification accuracy; however, whether it also preserves the model's internal reasoning remains an open question.