arXiv AI By Can Jin, Yan Kang, Yifan Gong, Chongjian Ge, Mingze Xu, Yuanjun Xiong, Dimitris N. Metaxas, Hongwu Peng

ShellElastic: Scalable Elastic Pretraining with Simultaneous Maximal Updates and Hyperparameter Transfer

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arXiv Machine Learning
Sep 10

Dense Structural Compression of Transformers via Gauge-Correct Channel Removal

The paper introduces GaugeLasso, a method that applies symmetric group‑lasso penalties to transformer channels during training, enabling entire tensor slices to be zeroed out while maintaining dense tensors for GPU efficiency. By calibrating channel penalties based on inference utility per compute, the network self‑organizes into depth‑dependent structural profiles that can be dramatically smaller than the original architecture, achieving up to 255‑fold compression on a polynomial division task and outperforming hand‑designed baselines on language modeling and autoencoding benchmarks. The approach also accelerates training and reveals over‑provisioned axes that guide subsequent design iterations.

By Jed A. Duersch, Na\"im Es-Sebbani, Nathana\"el Haas, Zied Bouraoui
arXiv Statistics ML
Oct 2

Transferable Graph Metanetworks

Transferable Graph Metanetworks extend the graph metanetwork paradigm to predict properties of neural networks of varying widths. By enforcing invariance to width-dependent representations and continuity across similar functions, the proposed modifications enable robust size generalization, especially for networks trained under the maximal‑update parameterization (μP) up to 42× the training width. The authors provide empirical evidence across multiple tasks and theoretical guarantees in the infinite‑width limit, explaining both successes and limitations under different parameterizations.

By Yuxin Ma, Adir Dayan, Yam Eitan, Haggai Maron, Soledad Villar