Do you want to train massive deep learning models with ease? Our 10 new tutorial notebooks of our popular UvA DL course show you how, implementing data, pipeline and tensor parallelism (and more) from scratch in JAX+Flax! 🚀🚀 .Check them out here: 🧵 1/11

Was a lot of fun going from a simple research idea to reclaiming the top spot and driving the effort with the team. Much more to come! 🚀.

RT @lmarena_ai: BREAKING: Gemini 2.5 Pro is now #1 on the Arena leaderboard - the largest score jump ever (+40 pts vs Grok-3/GPT-4.5)! 🏆. T….

RT @UncertaintyInAI: to all reviewers who kindly accepted the invitation to review for #UAI2025 🙏,. you can do it! one more push! 🦾. and if….

RT @maxmbeck: Yesterday, we shared the details on our xLSTM 7B architecture. Now, let's go one level deeper🧑‍🔧. We introduce. ⚡️Tiled Flash….

RT @maxmbeck: 📢🔔I am excited to share the details on our optimized xLSTM architecture for our xLSTM 7B model!🚨. We optimized the architectu….

RT @mentzer_f: 🚀 🚀 🚀 Seeing SHIPS everywhere!!! Model is out, check out at

RT @robertriachi: some cool examples with Gemini 2.0 native image output 🧵

RT @19kaushiks: Super excited to ship Gemini's native image generation into public experimental today :) We've made a lot of progress and s….

RT @OriolVinyalsML: Gemini 2.0 Flash debuts native image gen! Create contextually relevant images, edit conversationally, and generate long….

RT @a7b2_3: Excited to share that the image generation capability from Gemini is publicly available now :).We have been working tirelessly….

RT @m__dehghani: Anyone who has been in this room knows that it’s never just another day in here! This space has seen the extremes of chaos….

RT @OfficialLoganK: Native image generation with Gemini 2.0 Flash is now available to all developers via an experimental release in the Gem….

I will miss the unique, thriving research environment at @UvA_IvI, the VISLab and @quvalab, but similarly looking forward to continuing my newest journey at @GoogleDeepMind.

My thesis "Learning Causal Representations in Spatio-Temporal Systems" summarizes our main contributions on causality and causal representation learning:

Thank you to my committee @FrancescoLocat8, Tom Heskes, Patrick Forré, @cgmsnoek, Evangelos Kanoulas, and chair @PaolaGrossoWork for making the defence very enjoyable, and special thanks to @samuelepapa_ and Mahsa Mojtahedi for supporting me as paranymphs!.

With a successful PhD defense, my 4-year PhD journey in Amsterdam has finally come to a close. It's been a truly amazing experience to have @egavves @y_m_asano @TacoCohen @saramagliacane supervising and supporting me all the way, and I will miss our fun collaborations together!

RT @y_m_asano: Congrats to Dr. Lippe, @phillip_lippe! ✨🎉🎉and congrats to the very rare distinction of receiving a 'cum laude' PhD💪 . It was….

RT @egavves: Wholehearted congratulations to the legendary @phillip_lippe graduating his PhD cum laude (full honors for an outstanding thes….

Personal update: Excited to have joined @GoogleDeepMind in Mountain View, working with the amazing @m__dehghani and team on training the next-gen Gemini models. Looking forward to the incredible work ahead! 🚀🤖.

RT @jo_brandstetter: Super hyped to share NeuralDEM -- the first real-time simulation of industrial particulate flows. NeuralDEM replaces D….