arXiv:2605. 08193v3 Announce Type: replace-cross Abstract: Normalization Equivariance (NE) is a structural prior that improves robustness to distribution shift in image-to-image tasks.
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arXiv:2606. 16214v1 Announce Type: cross Abstract: Modern deep learning models remain notoriously prone to overconfidence, limiting their reliability in high-stakes applications.
The paper investigates why adaptive optimizers like Adam outperform SGD when fine‑tuning Transformers. It introduces gradient heterogeneity—the variation in gradient norms across parameter blocks—and shows, both theoretically and experimentally, that this heterogeneity, together with Hessian heterogeneity, hampers SGD convergence while sign‑based methods such as SignSGD are less affected. The study links the source of gradient heterogeneity to layer‑normalization placement, finding that Post‑LN architectures exhibit the strongest effect, and uses SignSGD as a tractable proxy to analyze Adam‑like behavior and learning‑rate scaling.
arXiv:2607. 10593v1 Announce Type: new Abstract: Normalization is a critical component for stabilizing Transformer training, yet the choice between static strategies such as Layer Normalization (LN) and adaptive alternatives remains largely task-dependent.
arXiv:2601. 22580v2 Announce Type: replace-cross Abstract: The success of Large Language Models (LLMs) hinges on the stable training of deep Transformer architectures.
arXiv:2509. 25136v3 Announce Type: replace Abstract: Activation-aware low-rank factorization techniques yield strong compression results but are generally confined to linear layers, while existing whitening-based theory typically makes an implicit full-rank assumption on activations.
The paper introduces Anon, an optimizer that extends adaptivity beyond the traditional bounds of SGD and Adam by allowing extrapolation across the entire real-number spectrum. It addresses the limitations of prior tunable optimizers that only interpolate between 0 and 1 adaptivity, showing that optimal adaptivity can require negative values for CNNs or values greater than one for Transformers. Anon incorporates Incremental Delay Update (IDU) to maintain provable stability and demonstrates competitive performance on image classification, diffusion, and large language modeling tasks.