Hybrid Models

Switch language: 混合模型

This page covers the two mixed-architecture models: M11 and M12.

Why hybrids exist

Hybrid models are designed around a practical intuition: different architectural families are good at different things.

  • CNNs are good at local motif extraction.

  • BiLSTMs are good at sequential context accumulation.

  • Transformers are good at global interaction modeling.

Hybrid models try to combine those strengths rather than forcing a single family to do everything.

M11: CNN + BiLSTM

M11 uses a CNN frontend followed by a bidirectional LSTM.

  • CNN role: compress the raw sequence and detect local motif-like patterns

  • BiLSTM role: model dependencies over the compressed feature sequence

This is attractive when the raw sequence is too long for direct recurrent modeling but ordered context still matters.

M12: CNN + Transformer

M12 uses the same CNN-style idea as a frontend, but hands the compressed sequence to a Transformer encoder.

  • CNN role: reduce length and expose local features

  • Transformer role: perform global interaction modeling on a shorter sequence

This is a pragmatic compromise between raw-sequence Transformers and purely convolutional baselines.

Why hybrids matter for the benchmark

They test whether architectural composition is better than architectural purity for this task. In many practical sequence problems, the answer is often yes:

  • local detectors handle motif discovery efficiently;

  • later modules reason over the more abstract feature stream.

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