Top 100 AI · Netherlands
Sander Dieleman
Google DeepMind 
- Score
- 7,3330 today
- Rank
- 70/198 ↓6
- over 90 days
- +309+4.1%
Sander Dieleman is a Research Scientist at Google DeepMind, specializing in deep learning, generative models, and neural networks. He shares technical insights, research findings, and developments in machine learning, often referencing his work on WaveNet, Imagen, and Veo. His content includes analyses of diffusion models, autoencoders, and other advanced ML techniques, alongside occasional mentions of music information retrieval and recommender systems.
Data as of Oct 25, 2026.
The raw numbers · last 90 days
- Median views
- 45.2K
- per post
- Median engagements
- 568
- per post
- Posts
- 31
- over 90 days
- Followers
- 78.6K
- all scored platforms
What drives the score
Percentiles 0–100 against all tracked creators.
- Reach · 30%86
Average views per post over 90 days
- Engagement · 30%74
Average reactions per post
- Audience · 30%66
Total followers, relative to population
- Consistency · 10%100
Posting frequency (25 posts / 90 days = 100)
Platforms
78.6K total followers
| Platform | Followers | Score |
|---|---|---|
| X@sedielem | 72K | 7,821t2 |
| LinkedIn@sanderdieleman | 6.5K | 7,512t2 |
Last 30 days
Dieleman's top posts
X · 14 days ago#1SDSander DielemanThis is a beautiful analysis of the order in which diffusion models resolve uncertainty about information at different abstraction levels in different representation spaces, with significant implications for latent di…
25.2K views553 engagements
- X · 14 days ago#2
Turns out you can use distribution matching distillation (DMD) to turn diffusion/flow models into looped models that iteratively refine samples directly in data space🤯 Nice bit of out-of-the-box thinking here! Strong…
SDSander Dieleman19.9K views367 engagements
- X · 8 days ago#3
More diffusion with nested looping! Looping at a given noise level fixes errors which would otherwise accumulate. Also reduces reliance on CFG. Related in spirit to data-space iteration (https://t.co/zhZPyHj87K) and s…
SDSander Dieleman15.5K views273 engagements
- X · 21 days ago#4
As if we needed any more evidence that 2026 is the year of continuous diffusion for language🤭 (I recently wrote a blog post about my thoughts on this topic, in case you missed it: https://t.co/IxtdWvfa6B)
SDSander Dieleman15.4K views239 engagements
- X · 17 days ago#5
I've been doing some thinking about multimodality lately, specifically the semantic gap between common representations of language and perceptual signals. In that context, this is an exciting result! (These thoughts a…
SDSander Dieleman12.9K views223 engagements
- X · 2 days ago#6
The magic of flow map language models is that they capture dependencies between tokens in just 1-2 model passes. Using this as a drafter for speculative decoding makes a ton of sense, and it's exciting to see this wor…
SDSander Dieleman10.2K views215 engagements
Head-to-head
