Hello X community! let's delve into a fascinating statistical concept known as Survival Analysis using @mcmc_stan in #rstats The blogpost covers a survival workflow in bayes (model+splines,priors,visualization,validation and deployment in Shiny) https://t.co/5CJWZC562A

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A leap forward, yet I hope the FDA does not forget "Why I am only a half-Bayesian": https://t.co/gqeQb2lfLJ. @DrMakaryFDA @NIHDirector_Jay, @eliasbareinboim @soboleffspaces @f2harrell

hot take: scientific geniuses were more common in the past because science was less professionalized. science used to attract weirdos and polymaths. now it’s dominated by the same resume-tweaking hyper-optimizers who end up in medicine, law, and mba programs.

Help! ─ 𝗔𝗽𝗽𝗹𝗶𝗲𝗱 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 open to work ─ Remote/Austin → rexdouglass .com With new tools, I've been cooking: ▸ Machine-vision pipelines — RIOS Intelligent Machines ▸ Information-extraction pipelines — Microsoft ▸ Interactive SMS surveys — Pantheon Insights

List of free Machine Learning books:

🚨New @JAMAHealthForum: We describe persistent performance drift, or post-deployment changes in performance, in a population #health algorithm widely-used in the @DeptVetAffairs, stemming in part from #COVID-related changes in utilization and outcomes -> https://t.co/mnegu7fW7L

Our didactic review on machine learning for causal inference, now open access We explain • identifiability (theory of when the data can answer a causal question) • machine-learning estimators • study design (asking well-framed questions, and loopholes, eg with time-wise data)

At Tilde, we believe mechanistic understanding of models is key to enabling entirely new architectures and capabilities. We’ve put together a position piece on what interpretability means to us. A thread 🧵

I'm observing a mini Moravec's paradox within robotics: gymnastics that are difficult for humans are much easier for robots than "unsexy" tasks like cooking, cleaning, and assembling. It leads to a cognitive dissonance for people outside the field, "so, robots can parkour &

NEW PAPER in @bmj_latest "Dealing with continuous variables and modelling non-linear associations in healthcare data: practical guide" --> https://t.co/4YGRasQVrx #methodologymatters

Can an AI model predict perfectly and still have a terrible world model? What would that even mean? Our new ICML paper formalizes these questions One result tells the story: A transformer trained on 10M solar systems nails planetary orbits. But it botches gravitational laws 🧵

Research papers from 14 institutions contained hidden prompts directing AI tools to give them good reviews. https://t.co/pXQ2rGlrxX

Wrapped up Stanford CS336 (Language Models from Scratch), taught with an amazing team @tatsu_hashimoto @marcelroed @neilbband @rckpudi. Researchers are becoming detached from the technical details of how LMs work. In CS336, we try to fix that by having students build everything:

🚨What is SOTA on tabular data, really? We are excited to announce 𝗧𝗮𝗯𝗔𝗿𝗲𝗻𝗮, a living benchmark for machine learning on IID tabular data with: 📊 an online leaderboard (submit!) 📑 carefully curated datasets 📈 strong tree-based, deep learning, and foundation models 🧵

@LangChainAI @llama_index @vercel @DSPyOSS @msdev @jxnlco @crewAIInc @guardrails_ai @Haystack_AI @dottxtai @latentspacepod @swyx @AndrewYNg @AlphaSignalAI @interconnectsai @dylan522p @SemiAnalysis_ @bentossell @jackclarkSF @theneurondaily And top podcasts:

I wrote a new piece on how much progress has been made in treating childhood leukemia. The answer is: quite a lot! Before the 1970s, fewer than 10% of children diagnosed survived 5 years after diagnosis. Now most are cured and around 85% survive that long.

Excited to announce MIRIAD — a large-scale dataset of 5,821,948 medical question-answer pairs, each rephrased from passages in the medical literature. Great collab with @QueyJ, @salmanabdullah_, @samarthrawal, @cyrilzakka, @SophieOstmeier, Maximilian Purk, @edreisMD, @EricTopol &

Do we already agree that to benefit from an LM for coding, you need to be a coder; to benefit from it as a scientist, you need to be a scientist yourself; to benefit from it as a writer, you need to be a writer. Or not yet?

Introducing soarXiv ✈️, the most beautiful way to explore human knowledge Take any paper's URL and replace arxiv with soarxiv (show in video) to teleport to its place in the universe I've embedded all 2.8M papers up until April 2025 Try it at: soarxiv dot org

BayesFlow 2.0, a Python package for amortized Bayesian inference, is now powered by Keras 3, with support for JAX, PyTorch, and TF. Great tool for statistical inference fueled by recent advances in generative AI and Bayesian inference. (Links in next tweet)