Can Geometric Deep Learning lead eliminate the need of "Brute Force" pre-trainin

<!-- SC_OFF --><div class="md"><p>This wasn't always the case, but now almost any research topic in machine learning that you can imagine is now being done MUCH BETTER in industry

<!-- SC_OFF --><div class="md"><p>There was a period in the development of machine learning where application seemed to be informed by theory. Some of the best known theories

<!-- 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 thinking about the current AGI discussions, especially the debate between scaling language models and building embodied systems.</p>

<!-- SC_OFF --><div class="md"><p>Hi everyone! I'm sharing a paper I've been working on that investigates how different positional encoding schemes (learned absolute, sinusoidal,

<!-- SC_OFF --><div class="md"><p>The BDH (Dragon Hatchling) paper (arXiv:2509.26507) describes a Hebbian synaptic plasticity mechanism where model weights update during

<!-- 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,

&#32; submitted by &#32; <a href="https://www.reddit.com/user/preyneyv"> /u/preyneyv </a> <br /> <span><a href="https://pleasedontcite.me/learning-backwards/">[link]</a></span>