591 research outputs found
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
Most recent approaches use the sequence-to-sequence model for paraphrase
generation. The existing sequence-to-sequence model tends to memorize the words
and the patterns in the training dataset instead of learning the meaning of the
words. Therefore, the generated sentences are often grammatically correct but
semantically improper. In this work, we introduce a novel model based on the
encoder-decoder framework, called Word Embedding Attention Network (WEAN). Our
proposed model generates the words by querying distributed word representations
(i.e. neural word embeddings), hoping to capturing the meaning of the according
words. Following previous work, we evaluate our model on two
paraphrase-oriented tasks, namely text simplification and short text
abstractive summarization. Experimental results show that our model outperforms
the sequence-to-sequence baseline by the BLEU score of 6.3 and 5.5 on two
English text simplification datasets, and the ROUGE-2 F1 score of 5.7 on a
Chinese summarization dataset. Moreover, our model achieves state-of-the-art
performances on these three benchmark datasets.Comment: arXiv admin note: text overlap with arXiv:1710.0231
Ultrafast quantum state tomography with feed-forward neural networks
Reconstructing the state of many-body quantum systems is of fundamental
importance in quantum information tasks, but extremely challenging due to the
curse of dimensionality. In this work, we present a quantum tomography approach
based on neural networks to achieve the ultrafast reconstruction of multi-qubit
states. Particularly, we propose a simple 3-layer feed-forward network to
process the experimental data generated from measuring each qubit with a
positive operator-valued measure, which is able to reduce the storage cost and
computational complexity. Moreover, the techniques of state decomposition and
-order absolute projection are jointly introduced to ensure the positivity
of state matrices learned in the maximum likelihood function and to improve the
convergence speed and robustness of the above network. Finally, it is tested on
a large number of states with a wide range of purity to show that we can
faithfully tomography 11-qubit states on a laptop within 2 minutes under noise.
Our numerical results also demonstrate that more state samples are required to
achieve the given tomography fidelity for the low-purity states, and the
increased depolarizing noise induces a linear decrease in the tomography
fidelity
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