Xin Wen @_xwen_
PhD @HKUniversity | Research Scientist Intern @Meta FAIR wen-xin.info New York Joined November 2017-
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Xin Wen retweeted
love these "what does the red arrow see" google maps transforms with nano-banana
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Xin Wen retweeted
We ran more experiments to better understand “why” diffusion models do better in data-constrained settings than autoregressive. Our findings support the hypothesis that diffusion models benefit from learning over multiple token orderings, which contributes to their robustness and…
We ran more experiments to better understand “why” diffusion models do better in data-constrained settings than autoregressive. Our findings support the hypothesis that diffusion models benefit from learning over multiple token orderings, which contributes to their robustness and… https://t.co/lXEPNn3mrV
Xin Wen retweeted
No Diet Coke if you work on pretraining
Xin Wen retweeted
🚨 The era of infinite internet data is ending, So we ask: 👉 What’s the right generative modelling objective when data—not compute—is the bottleneck? TL;DR: ▶️Compute-constrained? Train Autoregressive models ▶️Data-constrained? Train Diffusion models Get ready for 🤿 1/n
Xin Wen retweeted
I converted one of my favorite talks I've given over the past year into a blog post. "On the Tradeoffs of SSMs and Transformers" (or: tokens are bullshit) In a few days, we'll release what I believe is the next major advance for architectures.
Great work! Looking forward to seeing what you do next :)
Great work! Looking forward to seeing what you do next :)
Xin Wen retweeted
📢 New Paper Alert! 📢 "Principal Components" Enable A New Language of Images ✨ arxiv.org/abs/2503.08685 We introduce Semanticist, a PCA-guided tokenizer that revolutionizes visual tokenization for generative models! 🧵 Thread below! 👇
Xin Wen retweeted
🎉 New Pre-print! 🎉 Do CLIP models truly generalize to new, out-of-domain (OOD) images, or are they only doing well because they’ve been exposed to these domains in training? Our latest study reveals that CLIP’s ability to “generalize OOD” may be more limited than previously…
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