arXiv:2606.11860v1 Announce Type: new Abstract: In this paper, we introduce Representation Prediction via Autoencoding using Iterative Refinement (RePAIR) - a novel self-supervised representation learning architecture that synthesizes Masked Autoencoders (MAE), Joint Embedding Predictive Architectures (JEPA), and Bidirectional Encoder Representations from Transformers (BERT). We demonstrate how it can be used to encode objects in sequential data like consecutive chess positions into compact yet meaningful representations. The basic principle of the architecture is to mask large portions of a s
The continuous evolution of self-supervised learning techniques and the demand for more efficient and robust AI models underpins the development of architectures like RePAIR.
This development indicates progress in creating more efficient and interpretable AI models for sequential data, expanding the capabilities of AI to understand complex patterns beyond just static images or text.
The ability to encode sequential data like chess positions into compact, meaningful representations allows for more sophisticated AI analysis and prediction in varied domains.
- · AI/ML Research
- · Gaming AI
- · Robotics
- · Pattern Recognition Software Providers
- · Traditional supervised learning models
- · AI systems requiring extensive labeled datasets
More efficient and interpretable AI models emerge for sequential data analysis.
Improved AI performance in complex strategic games and potentially in motion planning for robotics.
These advancements could contribute to the development of more general artificial intelligence capable of learning from raw, unlabeled sequential interaction data.
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Read at arXiv cs.LG