Alan Baade

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@BaadeAlan

CS PhD Student at Stanford

US Joined April 2016

79 Following 908 Followers 63 Posts

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Alan Baade @BaadeAlan Oct 19

1/ The tokenizer is a new scaling axis!

We introduce an approach for diffusable representation learning that's principled, scalable, and works when DINO/LeJEPA collapse.

We get joint tokenizer+diffusion scaling laws and it's compute-optimal vs raw diffusion

w/ @drfeifei @GordonWetzstein @jcjohnss @ericryanchan @vincesitzmann @KyleSargentAI

Thread, Paper, and Blogpost below

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Alan Baade @BaadeAlan Jul 5

I'll be at ICML to present Latent Forcing!

Come stop by the poster on Tuesday at 2:00 PM KST, Hall A #3507

Reach out if you want to chat about the future of generative models!

Alan Baade Verified account @BaadeAlan Feb 17

What's the right space to diffuse in: Raw Data or Latents?

Why not both!

In Latent Forcing, we order a joint diffusion trajectory to reveal Latents before Pixels, leading to improved convergence while being lossless at encoding and end-to-end at inference.

w/ @drfeifei+...
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Alan Baade @BaadeAlan Apr 9

I love this result

It shows that a robot foundation model ought to be a model trained to understand the world that happens to be able to perform robot tasks. Not just action behavior cloning from teleop/umi data to fit a demo.

For this, the DVA approach is the clear direction.

Rhoda AI @RhodaAI Apr 9

Here’s something we’ve never seen done before.

Real-world tasks are long and ambiguous. Solving them requires visual memory and state tracking. Most robot policies only see the last few frames. Ours doesn't.

We put our DVA, FutureVision, to the perfect testbed: the shell game 🐚. The DVA nails it.

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Alan Baade @BaadeAlan Feb 17

What's the right space to diffuse in: Raw Data or Latents?

Why not both!

In Latent Forcing, we order a joint diffusion trajectory to reveal Latents before Pixels, leading to improved convergence while being lossless at encoding and end-to-end at inference.

w/ @drfeifei+...
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Alan Baade @BaadeAlan Oct 9, 2024

Q: Why can't we get GPT-level understanding from language models on speech?
A: We need better speech tokens!

In SyllableLM, *we beat
@kyutai_labs Moshi on semantic understanding in 70 hours of training* by making speech tokens at 5 frames/s
With @PuyuanPeng, David Harwath

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