Machine Learning with a Honk
Massimiliano ViolaWeekly insights into machine learning research, focusing on computer vision and generative models.
| Platform |  | Pricing | Only free issues | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 66 | Founded | 2 years ago | Last Issue | 5 days ago |
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66. V-JEPA 2 and V-JEPA 2.1
From the V-JEPA 2.1 paper [2].
Introduction
Last time, we saw how V-JEPA transformed video self-supervised learning by replacing pixel reconstruction with feature prediction in latent space. While that was a new way to train a video enc...
65. VideoMAE V2 and V-JEPA
Introduction
In the previous (and only) post we ever did on video SSL models, we looked into VideoMAE [3] as an example of extending an image paradigm to the temporal domain with just a few modifications.
That said, video data is a whol...
64. PMAE: Principal Masked Autoencoder
Introduction
In the previous episodes of our series on SSL (read them here), we saw how masking parts of an image or video and reconstructing the missing content is a working pre-training recipe that scales with no labels.
Masked Autoen...
63. Steerable Visual Representations
Introduction
Pretrained Vision Transformers (ViTs) are static by design. If we have an architecture and a fixed set of weights, feeding an image through the model yields an identical output every single time.
Still, anyone deploying the...
62. PartField
Introduction
General 3D encoders are becoming popular as we adopt more scalable architectures and expand the size and diversity of training datasets. For instance, models like Sonata [2], Concerto [3], and Utonia [4], which I covered in...
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Authors
The writers behind this newsletter.
Perception & Safety at Bedrock Robotics. Previously at Cerrion (YC S22), Stanford, and ETH Zurich.
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