Here is a novel machine learning architecture that takes advantage of the strengths and weaknesses of existing architectures and is efficient for learning and running on a normal processor:
1. Data Encoder: The first component of the model is a data encoder that takes in input data and encodes it into a dense vector representation. This can be done using existing methods such as feed-forward neural networks, which are highly effective at encoding complex data into dense vectors.
2. Context Network: The second component is a Context Network, which takes the dense vector representation as input and outputs a context vector. This component is designed to take advantage of the strengths of existing architectures such as the Transformer, which is highly effective at generating context vectors for natural language processing.
3. Decoder: The final component is a decoder, which takes the context vector generated by the Context Network as input and outputs the predicted output. This can be done using a variety of methods such as recurrent neural networks, which are highly effective at generating sequential outputs.
4. Multi-level Attention: The Context Network component includes multi-level attention, which allows the model to take into account the relationships between different parts of the input data when generating the context vector. This is similar to self-attention techniques used in Transformer models, but instead of bringing all tokens into the attention mechanism, the context network only focuses on a subset  Your response should contain less than 550 words.
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