Paper · arXiv
Stochastic approximation in non-markovian environments revisited
Based on some recent work of the author on stochastic approximation in non-markovian environments, the situation when the driving random process is non-ergodic in addition to being non-markovian is considered. Using this, we propose an analytic framework for understanding transformer based learning, specifically, the `attention' mechanism, and continual learning, both of which depend on the entire past in principle.
| Authors | Vivek Shripad Borkar |
|---|---|
| Primary category | stat.ML |
| Published | 22 March 2026 |
| Last updated at source | 22 March 2026 |
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