Skip-gram neural networks were the base of the language models and ensemble tree-based algorithms were the machine learning algorithms for prediction models. We trained the prediction model on Rosaceae genome d
cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding modelAuthor(s): Shahid Akbar , Maqsood Hayat , Muhammad Tahir , Salman Khan , Fawaz Khaled Alarfaj Publication date Created: September 2022 ...
we propose a stable dynamic embedding framework with high efficiency. It is an extension for the Skip-gram based network embedding methods, which can keep the optimality of the objective in the Skip-gram based methods in theory. Our model can not only generalize to...
We propose a novel framework VASG (Visually Aware Skip-Gram) for learning user and product representations in a common latent space using product image features. Our model is an amalgamation of the Skip-Gram architecture and a deep neural network based Decoder. Here the Skip-Gram attempts to ...
Skip-Gram architecture and a deep neural network based Decoder. Here the Skip-Gram attempts to capture user preference by optimizing user-product co-occurrence in a Heterogeneous Information Network while the Decoder simultaneously learns a mapping to transform product image features to the Skip-Gram ...
Word2Vec Skip-Gram model implementation using TensorFlow 2.0 to learn word embeddings from a small Wikipedia dataset (text8). Includes training, evaluation, and cosine similarity-based nearest neighbors - sminerport/Word2VecSkipgramTensorFlow
Devi, G.R., Veena, P.V., Kumar, M.A., & Soman, K.P. (2016). Entity extraction for Malayalam social media text using structured skip-gram based embedding features from unlabeled data. Procedia Computer Science, 93(2016), 547-553....
Performance evaluation on various datasets demonstrates that embodiments of the skip-gram network are powerful for general text classification task set. The skip-gram models are robust and may be generalized well on different datasets, even without tuning the hyper-parameters for specific dataset....
Experimental results indicate that CNN with the skip-gram model performs more efficiently than CNN-based one-hot method.doi:10.3837/tiis.2019.12.016Xu, WenhuaHuang, HaoZhang, JieGu, HaoYang, JieGui, GuanKsii Transactions On Internet And Information Systems...
Performance evaluation on various datasets demonstrates that embodiments of the skip-gram network are powerful for general text classification task set. The skip-gram models are robust and may be generalized well on different datasets, even without tuning the hyper-parameters for specific dataset....