The paper casts the standard Transformer block with RoPE as a first-order approximation of a radial–tangential state estimator and introduces a Polar Transformer variant that retains the discarded geometric corrections.
BayesFormer: A trustworthy bayesian inference framework for large language models
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
representative citing papers
BayesLoRA applies diagonal rank-wise variational inference to break LoRA gauge symmetry and learn adapter rank with O(r) parameters.
Bayesian visual transformers with ensemble and sampling methods achieve a 7.4 percentage point gain on weighted F-beta score for affordance instance segmentation on the IIT-Aff dataset while providing calibrated epistemic and aleatoric uncertainty maps.
A variational language model achieves minimal agentic control by treating internal uncertainty as an operational signal for regulation, checkpoint retention, and inference intervention.
citing papers explorer
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The Transformer as a Polar State Estimator
The paper casts the standard Transformer block with RoPE as a first-order approximation of a radial–tangential state estimator and introduces a Polar Transformer variant that retains the discarded geometric corrections.
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Learning Adapter Rank via Symmetry Breaking
BayesLoRA applies diagonal rank-wise variational inference to break LoRA gauge symmetry and learn adapter rank with O(r) parameters.
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Uncertainty Estimation in Instance Segmentation of Affordances via Bayesian Visual Transformers
Bayesian visual transformers with ensemble and sampling methods achieve a 7.4 percentage point gain on weighted F-beta score for affordance instance segmentation on the IIT-Aff dataset while providing calibrated epistemic and aleatoric uncertainty maps.
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Agentic Control in Variational Language Models
A variational language model achieves minimal agentic control by treating internal uncertainty as an operational signal for regulation, checkpoint retention, and inference intervention.