Phase-Associative Memory: Sequence Modeling in Complex Hilbert Space
📰 ArXiv cs.AI
Phase-Associative Memory (PAM) is a recurrent sequence model that uses complex-valued representations and accumulates associations in a matrix state
Action Steps
- Understand the concept of complex-valued representations in sequence modeling
- Implement the Phase-Associative Memory (PAM) model using outer products and conjugate inner product
- Evaluate the performance of PAM on benchmark datasets such as WikiText-103
- Compare the results with other sequence models like transformers
Who Needs to Know This
ML researchers and engineers on a team can benefit from PAM as it provides a new approach to sequence modeling, and software engineers can implement and optimize the model
Key Insight
💡 PAM achieves competitive performance with transformers on sequence modeling tasks
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🤖 Introducing Phase-Associative Memory (PAM), a new recurrent sequence model that uses complex-valued representations! 🚀
Key Takeaways
Phase-Associative Memory (PAM) is a recurrent sequence model that uses complex-valued representations and accumulates associations in a matrix state
Full Article
Title: Phase-Associative Memory: Sequence Modeling in Complex Hilbert Space
Abstract:
arXiv:2604.05030v1 Announce Type: cross Abstract: We present Phase-Associative Memory (PAM), a recurrent sequence model in which all representations are complex-valued, associations accumulate in a matrix state $S_{t}$ $\in$ $\mathbb{C}^{d \times d}$ via outer products, and retrieval operates through the conjugate inner product $K_t^* \cdot Q_t / \sqrt{d}$. At $\sim$100M parameters on WikiText-103, PAM reaches validation perplexity 30.0, within $\sim$10\% of a matched transformer (27.1) trained
Abstract:
arXiv:2604.05030v1 Announce Type: cross Abstract: We present Phase-Associative Memory (PAM), a recurrent sequence model in which all representations are complex-valued, associations accumulate in a matrix state $S_{t}$ $\in$ $\mathbb{C}^{d \times d}$ via outer products, and retrieval operates through the conjugate inner product $K_t^* \cdot Q_t / \sqrt{d}$. At $\sim$100M parameters on WikiText-103, PAM reaches validation perplexity 30.0, within $\sim$10\% of a matched transformer (27.1) trained
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