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Jakub Tomczak
@jmtomczak
Group Leader & Principal Scientist | @ChanZuckerberg | @TUeindhoven | @natinlab1 | Before: @Qualcomm @VUamsterdam @UvA_Amsterdam (@wellingmax) | Opinions my own
Joined October 2016
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    🎊 It has arrived 🎊, the 2nd edition of my "Deep Generative Modeling" book. It has 100 new pages, 3 new chapters (incl. #LLMs) and new sections. It covers all deep generative models that constitute the core of all #GenerativeAI techs! Check it out: πŸ’»tinyurl.com/mwj9dw83
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    I am excited to announce that my book, "Deep Generative Modeling", is available online and in print (@SpringerNature): link.springer.com/book/10.1007/9… Code used in the book is freely available online: github.com/jmtomczak/intr… (1/4)
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    New year, and new challenges! I've decided to do a series of blog posts on deep generative modeling. The first post is out: jmtomczak.github.io/blog/1_introdu… I hope you'll enjoy it! The next one in ~2 weeks πŸ€“
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    The fourth blog post on deep generative modeling! This time about one of my favorite topics: Variational Auto-Encoders ▢️◀️. Proposed concurrently by @dpkingma @wellingmax and @DaniloJRezende @shakir_za D. Wierstra. πŸ‘‡ Post: jmtomczak.github.io/blog/4/4_VAE.h… Code: github.com/jmtomczak/intr…
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    I am extremely happy to announce that yesterday, after ~12mo of hard work, I handed in the second edition of my book "Deep Generative Modeling" (link.springer.com/book/10.1007/9…) to the publisher. There are ~100 pages of new content! ✨ πŸ’‘I will keep you updated!!πŸ’‘
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    A new blog post is out! This time about Diffusion-based Deep Generative Modeling! Read about this fascinating research line and check out the code: Post πŸ“œ: tinyurl.com/c4mhz6jn Code πŸ’»: tinyurl.com/3h3zmx2f
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    The 7th post in the series on deep generative modeling. This time about priors ⏺️ in VAEs ▢️◀️, a very important topic that is often neglected. Join me in the journey on standard Gaussians, VampPriors, flows and GTMs. πŸ‘‡ Post: jmtomczak.github.io/blog/7/7_prior…
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    Yesterday, in our deep learning course (dlvu.github.io) at @VUamsterdam, we had an amazing guest lecture on "Geometric Deep Learning" provided by @TacoCohen. More on this topic could be found at geometricdeeplearning.com (@mmbronstein @joanbruna @TacoCohen @PetarV_93 )
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    Can we efficiently learn one model that generates and classifies (i.e., a joint model)? Yes, we can! In our recent paper, we proposed a new joint model that combines a diffusion model with a classifier by sharing the UNet architecture. Link : arxiv.org/abs/2301.13622 [1/5]
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    After the summer break, the deep generative modeling series is back! πŸ’ͺ This time about hierarchical latent variables models (hierarchical VAEs). Post πŸ“œ: tinyurl.com/ntmkdx45 Code πŸ’»: tinyurl.com/d5kxr22a
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    After a long time, the 8th blog post is out! This time I discuss neural compression with deep generative modeling. Post πŸ€“: jmtomczak.github.io/blog/8/8_neura… Code πŸ’»: github.com/jmtomczak/intr…
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    The end of February brings the last blog post in the short series on score-based generative models. After score matching and SDEs, it is time for flow matching! Link to the post: jmtomczak.github.io/blog/18/18_fm.… Link to the code: github.com/jmtomczak/intr…
    GIF
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    Exactly one year ago, after writing it for 9mo (yep, it's my baby!), my book on #GenerativeAI was out! It was downloaded over 26k, which is over 70 times per day! It was an amazing year for #GeneartiveAI, and if you'd like to catch up with this topic, just get this book πŸ€“
    I am excited to announce that my book, "Deep Generative Modeling", is available online and in print (@SpringerNature): link.springer.com/book/10.1007/9… Code used in the book is freely available online: github.com/jmtomczak/intr… (1/4)
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    Today is a new day and a new blog post on score-based generative models. How do we use score matching to learn generative models defined by a stochastic/ordinary differential equation? Check this out! Post: jmtomczak.github.io/blog/17/17_sbg… Code: github.com/jmtomczak/intr…