Wojciech Zarzecki (@w_zarzecki) 's Twitter Profile
Wojciech Zarzecki

@w_zarzecki

Researcher at @ewa_szczurek's lab at UW | ML Engineer at DMSI Software | CS student at WUT

ID: 1829517688609107968

calendar_today30-08-2024 13:53:25

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Ewa Szczurek (@ewa_szczurek) 's Twitter Profile Photo

11 HOURS / 06 MINUTES to the ML in PL conference in Warsaw! I'm a big fan of this event and proud of our BATTLE-AMP ⚔️ by Paulina Szymczak, Wojciech Zarzecki, Adriana Bukała et al. presented at the Student Research Workshop tomorrow, and of posters from our lab at the poster session :).

11 HOURS / 06 MINUTES to the <a href="/MLinPL/">ML in PL</a> conference in Warsaw! I'm a big fan of this event and proud of our BATTLE-AMP ⚔️ by <a href="/szymczak_pau/">Paulina Szymczak</a>,  <a href="/w_zarzecki/">Wojciech Zarzecki</a>, <a href="/ada_bukala/">Adriana Bukała</a> et al. presented at the Student Research Workshop tomorrow, and of posters from our lab at the poster session :).
Paulina Szymczak (@szymczak_pau) 's Twitter Profile Photo

BATTLE-AMP ⚔️ framework for benchmarking AMP prediction models at ML in PL! 💻🦠 Cool talks, superb conversations and a lot of fun! Many thanks to Wojciech Zarzecki, Adriana Bukała and Ewa Szczurek for making this happen! And to collaborators from Helmholtz Munich | @HelmholtzMunich and Uniwersytet Warszawski!

BATTLE-AMP ⚔️ framework for benchmarking AMP prediction models at <a href="/MLinPL/">ML in PL</a>! 💻🦠 Cool talks, superb conversations and a lot of fun! Many thanks to <a href="/w_zarzecki/">Wojciech Zarzecki</a>, Adriana Bukała and <a href="/ewa_szczurek/">Ewa Szczurek</a> for making this happen! And to collaborators from <a href="/HelmholtzMunich/">Helmholtz Munich | @HelmholtzMunich</a> and <a href="/UniWarszawski/">Uniwersytet Warszawski</a>!
Bartosz Cywiński (@bartoszcyw) 's Twitter Profile Photo

🔥 New Paper! How can sparse autoencoders (SAEs) applied to diffusion models help us solve real-world challenges? 🚀 Introducing 𝗦𝗔𝗲𝗨𝗿𝗼𝗻: We use SAEs for unlearning in diffusion models and outperform existing baselines! Here's how it works: 🧵 1/