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Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and important for a wide range of NLP tasks. We present a model that uses a mix of unsuper-vised and supervised techniques to learn word vectors capturing semantic term–document(More)
In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. However, to our knowledge, these deep learning approaches have not been extensively studied for auditory data. In this paper, we apply convolutional deep belief networks to audio data and empirically evaluate them(More)
Although recent advances in AI have allowed Go playing programs to become moderately strong even on full 19x19 boards, Go programs still cannot approach the highest levels of play. One of the strongest Go playing programs (strongest in 2007) utilizes the UCT algorithm, which is a Monte‐Carlo search algorithm paired with a value function [1]. The value(More)
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