Summary of Disentangling Representations Through Multi-task Learning, by Pantelis Vafidis et al.
Disentangling Representations through Multi-task Learning
by Pantelis Vafidis, Aman Bhargava, Antonio Rangel
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC); Machine Learning (stat.ML)
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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 The proposed framework focuses on internal representations that capture the underlying structure of the world, enabling intelligent perception and interaction. By optimally solving multi-task evidence accumulation classification tasks, agents can implicitly represent disentangled latent factors, which facilitate feature-based generalization. Theoretical results guarantee the emergence of these representations in terms of noise, number of tasks, and evidence accumulation time. Experimental validation is demonstrated in recurrent neural networks (RNNs) trained to multi-task, which learn continuous attractors representing disentangled representations. This framework establishes a link between competence at multiple tasks and the formation of interpretable world models in biological and artificial systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence can understand the world by creating internal representations that show its structure. These “disentangled” representations are like maps that help AI make sense of things. Researchers found that if AI is good at doing many tasks, it will automatically create these maps. This helps AI generalize to new situations without needing training data. The study shows that transformers, a type of AI model, are particularly good at creating these maps and understanding the world. |
Keywords
* Artificial intelligence * Classification * Generalization * Multi task
