TEMPER: Tensorized Efficient Manifold-constrained Parameterization for Expressive Residual Routing
Residual connections rely on a static residual pathway, and are essential for training deep neural networks. Hyper-connections (HC) increase the expressivity of residual routing by incorporating multiple residual streams and learning dynamic information flow, while manifold-constrained (mHC) variants stabilize training through doubly stochastic residual mixing. However, a generator-level bottleneck remains in existing methods: they use dense, unstructured generators for pre-branch aggregation, residual mixing, and post-branch redistribution, which results in parameter count growing rapidly with the number of streams. To address this issue, we propose \underline{\textbf{T}}ensorized \under...
| Authors | Yuxuan Gu, Wuyang Zhou, Huijun Xing et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
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