Paper

bde: A Python Package for Bayesian Deep Ensembles via MILE

Unreadunread

AI summary

arXiv:2605. 14146v1 Announce Type: new Abstract: bde is a user-friendly Python package for Bayesian Deep Ensembles with a particular focus on tabular data.

Reading status

Priority

About this paper

Authors: Vyron Arvanitis, Angelos Aslanidis, Emanuel Sommer, David R\"ugamer
Released: 2026-05-15 (3mo ago)
Topic: Related to current project
Relation to my work: Read next because bde: A Python Package for Bayesian Deep Ensembles via MILE overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: class, implement, chain. Source: arxiv cs.LG (Machine Learning).
arXiv: 2605.14146

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to bde: A Python Package for Bayesian Deep Ensembles via MILE. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.