iiinova — where your digital self keeps evolving

Dilyara Bareeva @dilya.bsky.social · 29/11/2025

Huge thanks to my fantastic co-authors Marina MC Höhne, Alexander Warnecke, @lpirch.bsky.social, Klaus-Robert Müller, @rieck.mlsec.org, @slapuschkin.bsky.social, @kirillbykov.bsky.social, and to the UMI Lab, @aifraunhoferhhi.bsky.social, @xai-berlin.bsky.social and @bifold.berlin for the support!

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Dilyara Bareeva @dilya.bsky.social · 29/11/2025

Our lightweight adversarial fine-tuning attack lets you bend a feature to visualize any arbitrary concept. Off-manifold, we impose a hyperbolic activation landscape with its optimum at the target, while preserving on-distribution activations through a weighted two-term loss. 🕵️‍♀️

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Dilyara Bareeva @dilya.bsky.social · 29/11/2025

✈️🇲🇽 Next Wednesday (Dec 3), 1–4 p.m. CST, I’ll be presenting Manipulating Feature Visualizations with Gradient Slingshots at NeurIPS 2025 in Mexico City! Feature Visualization has long been a staple interpretability tool. Our work shows it’s far from reliable! 🚨

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Dilyara Bareeva @dilya.bsky.social · 13/12/2024

Sadly, I wasn’t able to make it to NeurIPS this year. For anyone attending, check out our quanda poster at the ATTRIB workshop tomorrow (Saturday) from 3 to 4:30 pm, presented by Galip Ümit Yolcu and Anna Hedström! GitHub: github.com/dilyabareeva... Paper: arxiv.org/abs/2410.07158

github.com

GitHub - dilyabareeva/quanda: A toolkit for quantitative evaluation of data attribution methods.

A toolkit for quantitative evaluation of data attribution methods. - dilyabareeva/quanda

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