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cognitive_synergy [2018/04/05 02:12]
cognitive_synergy [2018/05/23 11:39] (current)
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 the mappings of a generator and use it to generate data of different modality. In addition, the proposed model can achieve the mappings of a generator and use it to generate data of different modality. In addition, the proposed model can achieve
 semi-supervised learning, which makes our model more flexible for practical applications. semi-supervised learning, which makes our model more flexible for practical applications.
 +Cross-domain GANs adopt several special mechanisms such as cycle-consistency and weight-sharing to extract the common
 +structure of cross-domain data automatically. However, the common structure does not exist between most cross-modal data
 +due to the heterogeneous gap. Therefore, the model need paired information to relate the different structures between data
 +of various modalities which are of the same concept.
 +https://​arxiv.org/​abs/​1703.04368v1 Symbol Grounding via Chaining of Morphisms
 +https://​arxiv.org/​abs/​1805.04174 Joint Embedding of Words and Labels for Text Classification
 +Word embeddings are effective intermediate representations for capturing semantic regularities between words, when learning the representations of text sequences. We propose to view text classification as a label-word joint embedding problem: each label is embedded in the same space with the word vectors. We introduce an attention framework that measures the compatibility of embeddings between text sequences and labels. The attention is learned on a training set of labeled samples to ensure that, given a text sequence, the relevant words are weighted higher than the irrelevant ones. Our method maintains the interpretability of word embeddings, and enjoys a built-in ability to leverage alternative sources of information,​ in addition to input text sequences. Extensive results on the several large text datasets show that the proposed framework outperforms the state-of-the-art methods by a large margin, in terms of both accuracy and speed.
 +https://​arxiv.org/​abs/​1805.08720 Adversarial Training of Word2Vec for Basket Completion
 +In recent years, the Word2Vec model trained with the Negative Sampling loss function has shown state-of-the-art results in a number of machine learning tasks, including language modeling tasks, such as word analogy and word similarity, and in recommendation tasks, through Prod2Vec, an extension that applies to modeling user shopping activity and user preferences. Several methods that aim to improve upon the standard Negative Sampling loss have been proposed. In our paper we pursue more sophisticated Negative Sampling, by leveraging ideas from the field of Generative Adversarial Networks (GANs), and propose Adversarial Negative Sampling. We build upon the recent progress made in stabilizing the training objective of GANs in the discrete data setting, and introduce a new GAN-Word2Vec model.We evaluate our model on the task of basket completion, and show significant improvements in performance over Word2Vec trained using standard loss functions, including Noise Contrastive Estimation and Negative Sampling.