One of the fundamental tasks in quantum information theory is quantum data compression, which can be realized via quantum autoencoders that first compress quantum states to low-dimensional ones and then recover to the original ones with a reference state. When taking a pure reference state, there...
Deep learning methods learn the input data's hierarchical features, leading to better performance than other standard machine learning methods. In deep learning-based DTI prediction, a drug-target pair has taken as input, and then the affinity of interaction is predicted as output. Wen et al....
1). Incidentally, there exist baseline models in the literature which combine two features but not the last one. In particular, the grammar variational autoencoder (Kusner et al., 2017, GVAE) represents strings as a sequence of context-free grammar rules and is trained via deep learning, ...
The classifiers such as adaptive neuro-fuzzy inference system and SVM are utilized along with DWT-based feature extraction to detect HIFs and to discriminate them from other transients in medium-voltage distribution systems [25]. The neural networks are sensitive to frequency changes, whereas the WT...
A generative deep learning model was developed to extract characteristics representing the inverse field of the permeability of a fluid in a porous medium, which in turn represents the runner system geometry of a thermoplastic injection mold. The model comprises a variational autoencoder network and ...
Such a method is beneficial for creating fast and easy-to-use deep learning models and if we only want to have a high-level/abstract view of what's happening within the layers. However, to have more flexibility in the interaction between layers and/or exploiting certain blocks of the model...
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One-hot encodings are commonly used in machine learning, but I hope to have shown you an interesting alternative for it in CVAE models. If you are also interested in the applications of such an approach (e.g. in biology), I recommend the publication"Population-level integration of single-c...
Representing crystal structures of materials to facilitate determining them via neural networks is crucial for enabling machine-learning applications involving crystal structure estimation. Among these applications, the inverse design of materials can co
30 December 2023 / Published online: 8 February 2024 © The Author(s) 2024 Abstract Variational autoencoders (VAEs) play an important role in high-dimensional data gener- ation based on their ability to fuse the stochastic data representation with the power of recent deep learning techniques....