Sid

@sidgairo18.bsky.social

🏔️📍🇩🇪 🇪🇺 PhD student at the Max Planck Institute for Informatics, and Institute of Science & Technology - Austria. 💻🏃🏻‍♂️🚴🏻🏋🏻🏊⛷️🎸🎹📚 Webpage: https://sidgairo18.github.io/

🚀New preprint: DAVE — Distribution-aware Attribution via ViT Gradient DEcomposition. 1/11 🔍 What’s new: We fix a persistent issue in ViT explainability: unstable, artifact-heavy pixel attributions. DAVE yields fine-grained pixel-level maps without patch-grid saliency.

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🌟Here are some resources and notes about doing research that I’ve been compiling since I started my PhD.🌟 I rely on these often, and after sharing them with a few folks in my group who found them useful, I thought it could be of use to the broader community 🧵👇 (1/n)

⏳Still need to wait for your last experiment results? 📣 We're pleased to announce that the deadline for non-proceeding track #CV4DC at @iccv.bsky.social has been extended to August 15, 2025 Looking forward to your submissions! cv4dc.github.io/2025/

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Sid@sidgairo18.bsky.social · last yr.

📣 Proceeding track's results are out. 🎉 Congratulations to all the authors whose papers were accepted. We can't wait to meet you at @iccv.bsky.social in Hawaii on Oct 19th. ⏰ Our non-proceeding track is still accepting submissions until July 20th! Details in the comments

📣 Proceeding track's results are out. 🎉 Congratulations to all the authors whose papers were accepted. We can't wait to meet you at @iccv.bsky.social in Hawaii on Oct 19th. ⏰ Our non-proceeding track is still accepting submissions until July 20th! Details in the comments

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🚨More tiny experiments that seem benign: linear probing on frozen features (but again not explicitly making BatchNorm params to .eval) can have big implications. Best test accuracy: 71.20% at Epoch 10, for Learning Rate = 0.01 Makes a hell of a lot of difference! Almost 10% 🚨

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Sid@sidgairo18.bsky.social · last yr.

Seemingly innocuous error that I see students making time and again: model.eval() or param.eval() for models with norm layers (like batch-norm) is critical. Especially when freezing ❄️ certain layers and training rest.

Seemingly innocuous error that I see students making time and again: model.eval() or param.eval() for models with norm layers (like batch-norm) is critical. Especially when freezing ❄️ certain layers and training rest.

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Had my older MTB catching dust in the bike room, took 2 hours to replace both tyres and tubes with brand new ones, new brakes, and chain cleaned + greased. 💥 Pretty relaxing and a nice rejuvinating break from research✨ P.S.: Once you are used to driving road bikes, MTBs seem too slow 🤪

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Had a great time presenting our work at ICLR 2025 in Singapore 🇸🇬. Lot of great interactions, meeting amazing people and a very well organised conference 🌟. It was great to catch up with old colleagues and meet new folks. For more detail see: arxiv.org/abs/2503.00641

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Sid@sidgairo18.bsky.social · last yr.

🚀 I will be @iclr_conf in Singapore next week presenting our recent work. We answer how exactly does "What you explain depend on how you train". 🤓 We will be @ Hall 3 + Hall 2B #492 (26th April, 3PM - 5:30 PM) Please reach out via dm if interestd in a chat or ☕️ #ICLR2025

🚀 I will be @iclr_conf in Singapore next week presenting our recent work. We answer how exactly does "What you explain depend on how you train". 🤓 We will be @ Hall 3 + Hall 2B #492 (26th April, 3PM - 5:30 PM) Please reach out via dm if interestd in a chat or ☕️ #ICLR2025

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With systems like PaperBench -the reproducibility tracks at conferences will now also have AI participants 😃😉 Infact, (soonish) the reviewing process could be supported with pipelines that can authenticate claims and results made in papers. 🤓 openai.com/index/paperb...

PaperBench: Evaluating AI’s Ability to Replicate AI Research

We introduce PaperBench, a benchmark evaluating the ability of AI agents to replicate state-of-the-art AI research.

openai.com