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Donato Crisostomi ✈️ NeurIPS

@crisostomi.bsky.social

🧑‍🎓 PhD stud. @ Sapienza, Rome 🥷stealth-mode CEO 🔬prev visiting @ Cambridge | RS intern @ Amazon Search | RS intern @ Alexa. 🆓 time 🎭improv theater, 🤿scuba diving, ⛰️hiking

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Donato Crisostomi ✈️ NeurIPS @crisostomi.bsky.social · 11/03/2025

Will present this at #CVPR ✈️ See you in Nashville 🇺🇸! Kudos to the team 👏 Antonio A. Gargiulo, @mariasofiab.bsky.social, @sscardapane.bsky.social, Fabrizio Silvestri, Emanuele Rodolà.

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Donato Crisostomi ✈️ NeurIPS @crisostomi.bsky.social · 08/01/2025

📢Prepend “Singular” to “Task Vectors” and get +15% average accuracy for free! 1. Perform a low-rank approximation of layer-wise task vectors. 2. Minimize task interference by orthogonalizing inter-task singular vectors. 🧵(1/6)

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Donato Crisostomi ✈️ NeurIPS @crisostomi.bsky.social · 09/12/2024

📣 Come check it this Friday at #NeurIPS!

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Donato Crisostomi ✈️ NeurIPS @crisostomi.bsky.social · 05/12/2024

I know you're probably thinking, "Yeah, these neuron-permutation-based model merging methods are cool.. but are they cycle-consistent (CC)?" Say no more! It just so happens that our new #NeurIPS24 paper covers exactly this! Huh? No idea what I am talking about? Read on (1/6)

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Donato Crisostomi ✈️ NeurIPS @crisostomi.bsky.social · 05/12/2024

First blue post (still have to figure out how tweets are called here) 💡idea: we consider task vectors at the layer level and reduce task interference by decorrelating the task-specific singular vectors of any matrix-structured layer 🔬results: large-margin improvements across all vision benchmarks

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