A Geometric Perspective on Robustness in Vision Transformers [R]

<!-- SC_OFF --><div class="md"><p>A neat blog post with excellent visuals introducing ViTs from the ground up. The post covers: </p> <ul> <li>Patch embedding</li> <li>Positional

<!-- SC_OFF --><div class="md"><p>We've been exploring an alternative framing of positional encoding where instead of additively injecting position signals into token embeddings,

<!-- SC_OFF --><div class="md"><p><strong>Paper:</strong> <a href="https://arxiv.org/abs/2604.21395v2"><strong>https://arxiv.org/abs/2604.21395v2</strong></a></p> <p>For years,

<!-- SC_OFF --><div class="md"><p>I’ve been reading about Geometric Deep Learning lately (the whole grids, graphs, groups, manifolds idea), and something clicked that i wanted to

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1texa4d/made_and_published_a_paper_comparing_analysis_of/"> <img alt="Made and Published a Paper

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1ukzdnj/hamiltonian_neural_networks_from_a_differential/"> <img alt="Hamiltonian Neural Networks from a

<!-- SC_OFF --><div class="md"><p>Paper:</p> <p><a href="https://arxiv.org/abs/2603.21676">https://arxiv.org/abs/2603.21676</a></p> <p>I found this interesting as another