3 citations · 4 across the 7 of their papers we have counts for
3 papers · 1 filter
Privacy from Symmetry: Orthogonally Equivariant Transformers for LLM Inference
Alexander Yukhimchuk, Andrey Shulga, Mladen Kolar +1
Running large language models locally is often impractical, pushing inference on sensitive text to third-party providers. Split inference partially mitigates this by keeping tokens…
Gradient Clipping Beyond Vector Norms: A Spectral Approach for Matrix-Valued Parameters
Alexander Yukhimchuk, Mladen Kolar, Martin Takáč +1
Gradient clipping is a standard safeguard for training neural networks under noisy, heavy-tailed stochastic gradients; yet, most clipping rules treat all parameters as vectors and…
Muon with Nesterov Momentum: Heavy-Tailed Noise and (Randomized) Inexact Polar Decomposition
Sayantan Choudhury, Xiaoran Cheng, Martin Takáč +2
Most first-order optimizers treat matrix-valued parameters as vectors, ignoring the intrinsic geometry of hidden-layer weights in neural networks. Muon addresses this mismatch by u…