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How AI Chooses What Information Matters Most
- The selection mechanism discussed in the article is inspired by concepts like gating, hypernetworks, and data-dependence.
- The concept of gating in neural networks has evolved to include any multiplicative interaction, not just limited to RNN mechanisms like LSTM or GRU.
- Hypernetworks involve neural networks whose parameters are generated by smaller networks, leading to more complex architectures.
- Data-dependence, like hypernetworks, involves model parameters that depend on the data being processed.
- Selection mechanisms are considered distinct concepts from ideas like gating or hypernetworks, despite some similarities.
- Related work includes structured SSM models like S4, S5, and quasi-RNNs, and end-to-end architectures such as H3, RetNet, and RWKV.
- S4 introduced structured SSMs with diagonal structures and focused on efficient convolutional algorithms for these models.
- S5 independently discovered the diagonal SSM approximation and computed recurrently with a parallel scan, differing from S6 with a selection mechanism.
- Mega simplified S4 models to real-valued forms, showing effectiveness in certain settings when combined with different architectural components.
- Various methods like Liquid S4, SGConv, Hyena, and others focus on different parameterizations of convolutional representations in SSMs.
- Most structured SSMs known are non-selective and usually strictly LTI (linear time invariant) in their operations.
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