Equivariant-Aware Structured Pruning for Efficient Edge Deployment: A Comprehensive Framework with Adaptive Fine-Tuning

A new approach to probabilistic robustness in deep learning, termed non-parametric probabilistic robustness (NPPR), has been proposed, which learns optimized perturbation distributions directly from data rather than relying on fixed distributions. This method aims to enhance the evaluation of model robustness under distributional uncertainty, addressing a significant limitation in existing probabilistic robustness frameworks.

A new self-supervised learning framework called CurvSSL has been introduced, which incorporates curvature regularization to enhance the learning process by considering the local geometry of data manifolds. This method builds on existing architectures like Barlow Twins and employs a two-view encoder-projector setup, aiming to improve representation learning in machine learning models.

A new framework called Convolutional Nearest Neighbors (ConvNN) has been introduced, unifying convolutional neural networks and transformers within a k-nearest neighbor aggregation framework. This approach highlights that both convolution and self-attention can be viewed as methods of neighbor selection and aggregation, with ConvNN serving as a drop-in replacement for existing layers in neural networks.