Sam Bond-Taylor @sambondtaylor
Senior Researcher in Health Futures at @MSFTResearch. Previously PhD in deep generative models @comp_sci_durham. He/him. samb-t.github.io Cambridge, England Joined July 2020-
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Is low-to-high frequency generation in diffusion models aka. 'approximate spectral autoregression' a necessity for generation performance? 📃 Blog: fabianfalck.com/posts/spectral… 📜 Paper: arxiv.org/abs/2505.11278
Is pixel diffusion passé? In 'Simpler Diffusion' (arxiv.org/abs/2410.19324) , we achieve 1.5 FID on ImageNet512, and SOTA on 128x128 and 256x256. We ablated out a lot of complexity, making it truly 'simpler'. w/ @tejmensink @JonathanHeek @KayLamerigts @RuiqiGao @TimSalimans
Discrete generative models use denoisers for generation, but they can slip up. What if generation *isn’t only* about denoising?🤔 Introducing DDPD: Discrete Diffusion with Planned Denoising🤗🧵(1/11) w/ @junonam_ @AndrewC_ML @HannesStaerk @xuyilun2 Tommi Jaakkola @RGBLabMIT
RadEdit stress-tests biomedical vision models by simulating dataset shifts through precise image editing. It uses diffusion models to create realistic, synthetic datasets, helping to identify model weaknesses and evaluate robustness: msft.it/6011mlGU3
This morning we released RadEdit's weights on @huggingface 🤗. RadEdit is a state-of-the-art latent diffusion model to generate chest X-rays conditioned on text inputs, and an editing pipeline designed for model stress-testing. huggingface.co/microsoft/rade… (Cc @cyrilzakka) 🧵2/9
👀A sneak-peek at our recent work accepted at #ECCV2024 @eccvconf ! 📜: arxiv.org/abs/2407.12753… Want a scalable and robust foundational model that also saves computational costs? Say hello to LookupViT, our information compression idea leading to sub-quadratic attention! (1/10)
👀A sneak-peek at our recent work accepted at #ECCV2024 @eccvconf ! 📜: arxiv.org/abs/2407.12753… Want a scalable and robust foundational model that also saves computational costs? Say hello to LookupViT, our information compression idea leading to sub-quadratic attention! (1/10) https://t.co/QLdxlDIGaB
Releasing Hydra, our "official" extension of Mamba (and general state space models) to be bidirectional! Hydra is motivated from first principles by increasing expressivity through the framework of "matrix mixer" sequence models
Releasing Hydra, our "official" extension of Mamba (and general state space models) to be bidirectional! Hydra is motivated from first principles by increasing expressivity through the framework of "matrix mixer" sequence models
Congratulations to @sambondtaylor (Sam Bond-Taylor) for successfully defending his thesis on "Modelling High-Dimensional Data with Likelihood-Based Generative Models" @comp_sci_durham @durham_uni Thanks to the examiners @haipinglu @AmirAtapour.
🔥 Our new preprint on biomedical self-supervised learning with images only is out! 🔥 Rᴀᴅ-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision by @MSFTResearch We show that: 0/3 🧵
🔥 Our new preprint on biomedical self-supervised learning with images only is out! 🔥 Rᴀᴅ-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision by @MSFTResearch We show that: 0/3 🧵 https://t.co/1kcoff6Wqf
Getting the most out of your hardware when training transformers requires thinking about your model as a sequence of GPU kernel calls. This mindset, common in HPC, is rare in ML and leads to inefficiencies in LLM training. Learn more in our paper arxiv.org/abs/2401.14489
[Job Alert] Associate/Assistant Professor in Artificial Intelligence for Space-Enabled Technologies at @durham_uni Perks: 1️⃣fully 'tenured' in US terminology at a top 100 Uni 2⃣reduced teaching 3⃣a funded PhD 4⃣travel budget 5⃣chance for a funded PostDoc shorturl.at/otwHQ
Tim doesn't stop to amaze me, head here to read where @Tim_Dettmers describes the working of the paged AdamW github.com/TimDettmers/bi… He basically built a linux cpu memory paging system but for GPU/CUDA. So when you run out of GPU memory instead of OOM, the unused memory blocks…
If you are passionate about AI in healthcare and seeking an internship opportunity to work in our team, please get in touch with us. jobs.careers.microsoft.com/global/en/job/… #MachineLearning #AIinHealthcare #medicalimaging #deeplearning #InternshipOpportunities
Rᴀᴅ-DINO is now on arXiv!🦖 We show that medical image encoders trained only on images perform similar or better than text supervised models on various benchmarks, demonstrating that reliance on text may not be necessary, and can become a potential limitation. @MSFTResearch
Rᴀᴅ-DINO is now on arXiv!🦖 We show that medical image encoders trained only on images perform similar or better than text supervised models on various benchmarks, demonstrating that reliance on text may not be necessary, and can become a potential limitation. @MSFTResearch https://t.co/AklawLeBvk
Experiment: Triton-Autodiff (github.com/srush/triton-a…) Source-to-source autodiff of Triton GPU code. Uses tangent to produce working backward code you can edit. (Been writing Mamba in Triton and hate having to do this part manually)
We are looking for interns & visiting researchers in our Visual Computing & AI Lab at TU Munich! Topics focus on Generative AI, NeRFs, Neural Scene Representations, Diffusion Models, LLMs, etc. Please share & apply by Feb 29th :) application.vc.in.tum.de Details: - We focus on…
Our paper ∞-Diff: Infinite Resolution Diffusion... was accepted at #iclr2024 Its a diffusion model defined in an ∞-dimensional Hilbert space. Sample higher res than the training set (non-blurry). Paper: openreview.net/pdf?id=OUeIBFh… with @sambondtaylor @comp_sci_durham #ai #research
We know "all models are wrong", partially because "all datasets are biased", but how wrong and how biased are they? Check out our latest @MSFTResearch preprint on stress-testing with diffusion models to learn more. "Causality matters in medical imaging"!
Introducing RadEdit! We use diffusion image editing to stress-test biomedical vision models: diagnosing failures and helping avoid extra data collection. This is my first paper since joining @MSFTResearch and started as an internship project with @SnchzPedro_ & @BorisvanBreugel
RadEdit: stress-testing biomedical vision models via diffusion image editing paper page: huggingface.co/papers/2312.12… Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from…
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