Transformer components arise as the natural solution to precision-weighted directional state estimation on the hypersphere.
2206.00826 , archivePrefix=
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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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RT-Transformer: The Transformer Block as a Spherical State Estimator
Transformer components arise as the natural solution to precision-weighted directional state estimation on the hypersphere.
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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.