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We propose an attention-enabled encoder-decoder model for the problem of grapheme-to-phoneme conversion. Most previous work has tackled the problem via joint sequence models that require explicit alignments for training. In contrast, the attention-enabled encoder-decoder model allows for jointly learning to align and convert characters to phonemes. We explore different types of attention models, including...
It's unclear how our brain's concepts emerge sequentially, and how the brain abstracts and generalizes each concept internally. The artificial intelligence field has seen the rise of model-based methods, where, starting from a predefined set of tasks, a programmer handcrafts a model for the set. Such a machine is incapable of generating and using any concept beyond the handcrafted model. Inspired...
In recent years, many principled probabilistic definitions for the determination of visual saliency have been proposed. Moreover, there has been increased focus on the role of context in the determination of visual salience. Prior efforts have shed some light on how context may help in predicting the location of, or presence of features associated with an object in the context of detection or recognition...
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