Summary of Next State Prediction Gives Rise to Entangled, Yet Compositional Representations Of Objects, by Tankred Saanum et al.
Next state prediction gives rise to entangled, yet compositional representations of objects
by Tankred Saanum, Luca M. Schulze Buschoff, Peter Dayan, Eric Schulz
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Compositional representations are essential for human-like generalization in vast state spaces. Models with learnable object slots have shown promise but rely on strong architectural priors. Distributed representation models, using overlapping neural codes, can support compositional generalization, but their ability to develop linearly separable object representations remains unexplored. This paper examines whether distributed models can match or outperform object-slot models in downstream prediction tasks through unsupervised video training. The results show that distributed models often perform as well or better than object-slot models and reveal that linearly separable object representations can emerge without object-centric priors, with next-state prediction playing a key role. However, the object representations are never fully disentangled, suggesting that maintaining partially shared codes enhances generalization by compressing object dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to understand and learn new things even when they’re combined in different ways. This paper looks at how computers can do this better. They compare two kinds of computer models: one that separates objects into distinct “slots” and another that uses overlapping neural codes. Surprisingly, the model using overlapping codes performs just as well or better than the other one. The researchers also found that even when the model doesn’t fully separate objects, it can still learn to recognize them in different combinations. |
Keywords
* Artificial intelligence * Generalization * Unsupervised
