Bobby @bobby_he
Machine Learning postdoc @ETH. PhD from @UniofOxford and former research intern @DeepMind/@samsungresearch bobby-he.github.io Zürich Joined January 2012-
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Why in neural networks the learning rate can transfer from small to large models (both in width and depth)? It turns out that the sharpness dynamics can explain it. Check out our new work! arxiv.org/abs/2402.17457 w/ @alexmeterez (co-first), @orvieto_antonio and T. Hofmann
If you are looking for a PhD position in the intersection between Deep Learning and Optimization, it's not too late to apply to my group at @MPI_IS and @ELLISforEurope Institute Tübingen! Send a DM if you are interested :) institute-tue.ellis.eu/research-group…
Interested in large language models? Worried about impacts of climate change? Come join us @oxcsml @NatureRecovery @UniofOxford in pushing the frontiers in #LLMs and at the same time help #NatureRecovery and address the impacts of #ClimateChange! bit.ly/4750IBO
Really fun project led by the amazing @yuhui_ding 💫 simple and efficient long range graph learning!!!📈
Really fun project led by the amazing @yuhui_ding 💫 simple and efficient long range graph learning!!!📈
Hot off the presses: ResNet hyperparameter transfer across depth and width! Tl;dr transfer for LR+schedules, momentum, L2 reg., etc. for wide ResNets and ViTs, with and without Batch/LayerNorm w/ @lorenzo_noci @mufan_li @BorisHanin @CPehlevan arxiv.org/abs/2309.16620
How do you scale Transformers to infinite depth while ensuring numerical stability? In fact, LayerNorm is not enough. But *shaping* the attention mechanism works! arxiv.org/abs/2306.17759 w/ @ChuningLi @mufan_li @bobby_he @THofmann2017 @cjmaddison @roydanroy
Mysterious observation: re-initializing neural nets during training can improve generalization, despite *no* change to the model, data or compute. We asked: when do re-initialization methods work? Paper📄: arxiv.org/abs/2206.10011 Poster🖼️: bit.ly/3mdjJAF (1/6)
New TMLR paper (w/ Francisca Vasconcelos, @bobby_he, and @yeewhye) on uncertainty quantification for low-dose CT reconstruction with implicit neural representations:
Collapsed or whitened features in self-supervised learning?🤔Turns out you can improve generalisation (esp in low-labelled data settings) by bridging between the two. Check out our work @ #icml today! 📒icml.cc/virtual/2022/p…
Presenting our work "Feature Kernel Distillation" (w. Mete Ozay) at ICLR poster session 5 today. We study the relevance of NN feature learning for (ensemble) knowledge distillation😊 Paper: openreview.net/forum?id=tBIQE…
Presenting our work "Feature Kernel Distillation" (w. Mete Ozay) at ICLR poster session 5 today. We study the relevance of NN feature learning for (ensemble) knowledge distillation😊 Paper: openreview.net/forum?id=tBIQE… https://t.co/1wd2gKjH2x
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233 Followers 274 Following I am a PhD student @ETH Zürich working on deep learning. MLP-pilled 💊. https://t.co/yWdDEV6Z15Russ Cook @hardestgeezer
354K Followers 185 Following The first person ever to run the full length of Africa @huel @perfecttednrg @hoka CHARITY FUNDRAISER⬇️Thrilled to announce that (1) I've successfully defended my PhD thesis, "Improving Deep Learning with Probabilistic Approaches," which I'll share once corrections are done, and (2) Today I started my dream job as a research scientist @GoogleDeepMind! Excited for the future! 🥳
PhD students deserve better financial conditions.
While packing, I found this “vintage” OpenAI t-shirt from 2016 BC(-hatGPT). I feel old… #openai #neurips2016 #swag
Super excited to share that I successfully defended my PhD thesis "Understanding Generalization and Robustness in Modern Deep Learning" today 👨🎓 A huge thanks to the thesis examiners @SebastienBubeck, @zicokolter, and @KrzakalaF, jury president Rachid Guerraoui, and, of course,…
You are not prepared for how this guy pronounces Schweppes. 😭
why didn’t voldemort just run harry potter over with a car
I’m excited to announce that in July 2025 I will be joining @UWaterloo as an Assistant Professor in the Department of Statistics and Actuarial Science! Until then, I will continue at Princeton as a DataX Postdoc Fellow, working with Boris Hanin. I have many exciting projects…
Following our previous work, we are releasing RecurrentGemma - a fully open source 2B model based on our Griffin architecutre! Code + weights as everyone has wished for! Code on Github: github.com/google-deepmin… Weights on Kaggle: kaggle.com/models/google/…
Who did this? ‘Me and the lads are exactly the same’ 🤣 Genius ..
This was such a special experience. The MFO itself, the discussions there, the chance to talk about our work on online model selection, and ofc the mandatory group photo in front of the Boy's surface statue. Would recommend! 🫶
The Oberwolfach Research Institute for Mathematics (MFO) was also the location of the latest #ELLISProgram workshop on Interactive Learning and Interventional Representations. Check out this detailed recap by Giorgia Ramponi @gio_ramponi: ifi.uzh.ch/en/alpi/blog.h…
[1/7] Happy to release 🥕QuaRot, a post-training quantization scheme that enables 4-bit inference of LLMs by removing the outlier features. With @akmohtashami_a @max_croci @DAlistarh @thoefler @jameshensman and others Paper: arxiv.org/abs/2404.00456 Code: github.com/spcl/QuaRot
I successfully defended my PhD today (exciting!). I've struggled today balancing feelings of accomplishment+gratitude and anti-climatic disappointment. It's been more than worth it to hear my youngest daughter toddle around saying "Doc-tor Dad!" 🥰
transitioning from a Bay Area tech worker type of guy to a back problems type of guy
There is already a Switzerland of AI. It's called Switzerland
📢 I am looking for a student researcher to work with me and my colleagues at Google DeepMind London on understanding & building new neural network architectures. Please reach out to me ([email protected]) and apply below before Mar 22 if interested! google.com/about/careers/…
Just got back from vacation, and super excited to finally release Griffin - a new hybrid LLM mixing RNN layers with Local Attention - scaled up to 14B params! arxiv.org/abs/2402.19427 My co-authors have already posted about our amazing results, so here's a 🧵on how we got there!
i passed my phd viva today !!! i give thanks to all of the (countless) beings that supported me and contributed to this <3 thank you @yeewhye @tom_rainforth @eric_nalisnick and so many others <3
We build neural codecs from a *single* image or video, achieving compression performance close to SOTA models trained on large datasets, while requiring ~100x fewer FLOPs for decoding ⚡ #CVPR2024 c3-neural-compression.github.io
From stochastic parrot 🦜 to Clever Hans 🐴? In our work with @_vaishnavh we carefully analyse the debate surrounding next-token prediction and identify a new failure of LLMs due to teacher-forcing 👨🏻🎓! Check out our work arxiv.org/abs/2403.06963 and the linked thread!
🗣️ “Next-token predictors can’t plan!” ⚔️ “False! Every distribution is expressible as product of next-token probabilities!” 🗣️ In work w/ @GregorBachmann1 , we carefully flesh out this emerging, fragmented debate & articulate a key new failure. 🔴 arxiv.org/abs/2403.06963