Fabio De Sousa Ribeiro

Postdoc at Imperial College London. Working on causality, generative models, and Bayesian inference.

[John Carmack @ID_AA_Carmack]

I like em dashes, and I hate that some people now take them as evidence of text being AI generated.

[Christian A. Naesseth @canaesseth]

I like the resurgence of "old-school" NFs that is happening. Here to distill CNFs for free-energy estimation.

RL https://t.co/bVPe9Ta1WI

Diffusion models (NFDM)
https://t.co/V9B1T8e5AD

Latent SDEs (SDE Matching)
https://t.co/k8PNxYTYZP

#SDE #Diffusion #GenAI #ML #FlowMatching

[Tanishq Mathew Abraham, Ph.D. @iScienceLuvr]

Why work on medical AI?

It's one of the most impactful usecases of AI, it's as simple as that.

If you want to work on something meaningful instead of using AI to optimize for clicks/revenue/engagement/etc., consider working on medical AI!

[Fabio De Sousa Ribeiro @fabayesian]

@NandoDF Cool, maybe you'll like this too :) https://t.co/zeY5EGUOsA

[Dan Roy @roydanroy]

We REALLY REALLY need a "Findings" for NeurIPS, ICLR, and ICML. 25,000 submissions at this year's NeurIPS represents extreme excess pressure. It takes valuable time away from legitimate new research.

One question is how to administer it. I suggest that Findings go through a lightweight round focused on improving clarity, reeling in overselling, etc. An AC can then "sign off". Authors can always decline the opportunity, if they want to try for the next conference.

The NeurIPS's proceedings are called "Advances in Neural Information Processing Systems". We could have "Findings in Neural Information Processing Systems", or to not trample on their brand, perhaps "Contributions to Neural Information Processing Systems".

ICLR could have "Letters on Learning Representations."

ICML could have "Machinations on Machine Learning."

[Fabio De Sousa Ribeiro @fabayesian]

@liyzhen2 We've got a flavour of this coming out soon 😌

[François Chollet @fchollet]

If your learning algorithm is based on correlation rather than causation, it will struggle with overfitting. To understand something is to identify its minimal sufficient causal mechanisms. Parsimony isn't just elegance, it's generalization robustness.

[Fabio De Sousa Ribeiro @fabayesian]

@gabriberton GANs came out in June 2014 and diffusion models in March 2015, not a huge gap between them

[Lucas Beyer (bl16) @giffmana]

Everything old is new again.

Mamba/ssm folks should really google their "new idea + lstm" please. About a decade ago, people have tried a shitton of things with lstms. Nothing wrong with retrying with modern tools, but ack the past.

This is not the first such case I see btw.

[Andrea Dittadi @andrea_dittadi]

Why do NNs often learn similar representations? Existing identifiability results offer theoretical insights, but applying them in practice poses challenges.

We’ll present our new work exploring these challenges next week at @unireps #NeurIPS2024 🇨🇦🎉

https://t.co/lCKgRhh43m

1/

[Ruiqi Gao @RuiqiGao]

A common question nowadays: Which is better, diffusion or flow matching? 🤔

Our answer: They’re two sides of the same coin. We wrote a blog post to show how diffusion models and Gaussian flow matching are equivalent. That’s great: It means you can use them interchangeably.

[Thomas Melistas (@tmelistas.bsky.social) @ThomasMelistas]

🎉Thrilled to share our paper "Benchmarking Counterfactual Image Generation" is accepted to @NeurIPSConf 2024!🌟

A big shoutout to my co-first authors Nikos Spyrou, @nefeligk_ and our amazing team: @SnchzPedro_, @vlontzos, Yannis Panagakis, @GiorgosPapanas & @STsaftaris! 🙌

[Fabio De Sousa Ribeiro @fabayesian]

@dvruette Correct. For those interested in more details check this out ✌️: https://t.co/zeY5EGVmi8

[Harry Anthony @HarryEJAnthony]

I’m thrilled to announce that my paper has been accepted at #UNSURE2024 at #MICCAI2024! Check out the video below where I give a summary of my findings! Keep an eye out next week for a full-length post where I’ll dive deeper into the work and what’s next!

[Athanasios (Thanos) Vlontzos, PhD @vlontzos]

Grateful for the warm reception at the #CausalAI Conference ! Honoured to share how #CausalSystemDesign draws parallels between Aristotle’s 4 Causes, NASA TRLs & Pearls causal modelling to transform AI. #AIInnovation #AIforBusiness

[Fabio De Sousa Ribeiro @fabayesian]

@SnchzPedro_ Possibly confounded by the absolute number of publications increasing?

[Andrea Dittadi @andrea_dittadi]

📢 New preprint on finding good representations for Visual Question Answering!

We focus in particular on object-centric (OC) learning – since its role in downstream tasks is still partially underexplored – and on pretrained foundation models.

📄 https://t.co/1tgsCZ7oDX

🧵👇

[Chen (Cherise) Chen @cherise_go]

Looking for PhD candidates interested in exploring multi-modal AI for healthcare! If you or someone you know is curious about this opportunity, feel free to reach out. More info about me and the team: https://t.co/lUwYusdqrE

[Fabio De Sousa Ribeiro @fabayesian]

@fhuszar We love our L40S'