I don't know. I could just now...

I think I may switch back to Twitter/X. Somehow I feel this site didn't take off and I really don't want to be looking at two feeds all the time...

Image matching and ChatGPT - new post in the wide baseline stereo blog.

tl;dr: it is good, even feels like human, but not perfect.
ducha-aiki.github.io/wide-baselin...

Many of the greatest papers, now canonical works, have a story of resistance, tension, and, finally, a crucial advocate. It's shockingly common. Why is there a bias against excellence? And what happens to those papers, those people, when no one has the courage to advocate?

Intern - Machine Learning for Audio The Music Technology team at Sony AI Barcelona is looking for Research Interns who are passionate about machine learning for audio signal processing. Our mission is to research and develop technologie...

Apply here: sonyglobal.wd1.myworkdayjobs.com/Sony_Europe_...

Preferred qualifications:
- PhD candidate or Postdoc.
- Experience with representation/contrastive learning or generative music models.
- Strong programming skills.
- Strong mathematical background.
- Python, github, pytorch, ...
- EU residence permit.
👇

Topics: representation learning for music matching or generative models for music copyright.
Location: Barcelona, on-site (two days a week at least).
Duration: 4-6 months.
Start date: April-November 2025.
Dedication: full-time (part-time also an option).
👇

Views from the office window. Photo taken just now.

Do you want to work with me for some months? Two internship positions available at the Music Team of Sony AI in Barcelona!
👇

Haha, me maybe not, but someone should go...

Thanks.

Congrats to my colleagues, many of whom are not on this website!

I'm happy to have two papers accepted at #ICASSP2025!

1) Contrastive learning for audio-video sequences, exploiting the fact that they are *sequences*: arxiv.org/abs/2407.05782

2) Knowledge distillation at *pre-training* time to help generative speech enhancement: arxiv.org/abs/2409.09357

Flow matching mapping text to image directly (instead of noise to image): cross-flow.github.io

With some delay, JetFormer's *prequel* paper is finally out on arXiv: a radically simple ViT-based normalizing flow (NF) model that achieves SOTA results in its class.

Jet is one of the key components of JetFormer, deserving a standalone report. Let's unpack: 🧵⬇️

Did you miss any of the talks of the Deep Learning Barcelona Symposyum 2024 ? Play them now from the recorded stream:

www.youtube.com/live/yPc-Un3...

I'll get straight to the point.

We trained 2 new models. Like BERT, but modern. ModernBERT.

Not some hypey GenAI thing, but a proper workhorse model, for retrieval, classification, etc. Real practical stuff.

It's much faster, more accurate, longer context, and more useful. 🧵

On pre-acrivation norm, learnable residuals, etc.

Two great tokenizer blog posts that helped me over the years: sjmielke.com/papers/token...

sjmielke.com/comparing-pe...

People have mostly standardized on certain tokenizations right now, but there are huge performance gaps between locales with high agglomeration (e.g. common en-us) and ...

No.

Don't be like Reviewer 2.

Did Gauss invent the Gaussian?

- Laplace wrote down the integral first in 1783
- Gauss then described it in 1809 in the context of least-sq. for astronomical measurements
- Pearson & Fisher framed it as ‘normal’ density only in 1910

* Best part is: Gauss gave Laplace credit!

I already signed up (as a mentor) for this year!

Thrilled to present our work on Constrained Parameter Regularization (CPR) at #NeurIPS2024!
Our novel deep learning regularization outperforms weight decay across various tasks. neurips.cc/virtual/2024...
This is joint work with Michael Hefenbrock, Gregor Köhler, and Frank Hutter
🧵👇

Entropy is one of those formulas that many of us learn, swallow whole, and even use regularly without really understanding.

(E.g., where does that “log” come from? Are there other possible formulas?)

Yet there's an intuitive & almost inevitable way to arrive at this expression.

Inventors of flow matching have released a comprehensive guide going over the math & code of flow matching!

Also covers variants like non-Euclidean & discrete flow matching.

A PyTorch library is also released with this guide!

This looks like a very good read! 🔥

arxiv: arxiv.org/abs/2412.06264

Normalizing Flows are Capable Generative Models Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have demonstrated promising results on both density estimation and generative modeling tasks, but have received relati...

Normalizing Flows are Capable Generative Models

Apple introduces TarFlow, a new Transformer-based variant of Masked Autoregressive Flows.

SOTA on likelihood estimation for images, quality and diversity comparable to diffusion models.

arxiv.org/abs/2412.06329

That was fast: #DLBCN 2024 was sold out in less than two hours !

New requests will be added to a waiting list. Read the instructions for same day event registration:

sites.google.com/view/dlbcn20...

Past work has characterized the functions learned by neural networks: arxiv.org/pdf/1910.01635, arxiv.org/abs/1902.05040, arxiv.org/abs/2109.12960, arxiv.org/abs/2105.03361. But it turns out multi-task training produces strikingly different solutions! Adding tasks produces “kernel-like” solutions.

Can language models transcend the limitations of training data?

We train LMs on a formal grammar, then prompt them OUTSIDE of this grammar. We find that LMs often extrapolate logical rules and apply them OOD, too. Proof of a useful inductive bias.

Check it out at NeurIPS:

nips.cc/virtual/2024...

Of course, Jürgen invented it before.