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Traditional tools fragment your thinking—papers in one place, annotations in another, writing somewhere else, and your ideas scattered everywhere.
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The dominant sequence transduction models are based on complex recurrent or convolutional neural networks. The best performing models also connect the encoder and decoder through an attention mechanism.
Recurrent neural networks, long short-term memory and gated recurrent neural networks in particular, have been firmly established as state of the art approaches.
"The best performing models also connect the encoder and decoder through an attention mechanism."
Page 1, Abstract
Key insight: Attention replaces recurrence entirely → enables parallelization
Linked to [[Transformers]]
How does this compare to BERT's bidirectional approach?
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Get answers grounded in your papers, highlights, and notes—not generic responses. Ada understands your research context and responds accordingly.
Lay out papers visually, connect themes across your library, and watch insights emerge. Some discoveries only happen when you can see the whole picture.
Your research stays grouped, searchable, and easy to revisit. Ada turns scattered reading into a coherent research system—so writing isn't a restart, it's the next step.
Ada is for the way research actually happens — nonlinear, iterative, and driven by curiosity.
Whether you're writing a thesis, synthesizing literature, building a new argument, or exploring an unfamiliar field, Ada keeps everything connected so your work stays coherent over time.
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