Neural-Network Representation of Quantum Many-Body States

There are many embeddings algorithm for representations. Sammon embedding is the oldest one, and we have Word2Vec, GloVe, FastText etc. for word-embedding algorithms. Embeddings are useful for dimensionality reduction. Traditionally, quantum many-body states are represented by Fock states, which is useful when the excitations of quasi-particles are the concern. But to capture the quantum entanglement … More Neural-Network Representation of Quantum Many-Body States

“selu” Activation Function and 93 Pages of Appendix

A preprint on arXiv recently caught a lot of attentions. While deep learning is successful in various types of neural networks, it had not been so for feed-forward neural networks. The authors of this paper proposed normalizing the network with a new activation function, called “selu” (scaled exponential linear units): . which is an improvement … More “selu” Activation Function and 93 Pages of Appendix

ConvNet Seq2seq for Machine Translation

In these few days, Facebook published a new research paper, regarding the use of sequence to sequence (seq2seq) model for machine translation. What is special about this seq2seq model is that it uses convolutional neural networks (ConvNet, or CNN), instead of recurrent neural networks (RNN). The original seq2seq model is implemented with Long Short-Term Memory … More ConvNet Seq2seq for Machine Translation

Tensor Networks and Density Matrix Renormalization Group

A while ago, Mehta and Schwab drew a connection between Restricted Boltzmann Machine (RBM), a type of deep learning algorithm, and renormalization group (RG), a theoretical tool in physics applied on critical phenomena. [Mehta & Schwab, 2014; see previous entry] Can RG be able to relate to other deep leaning algorithms? Schwab wrote a paper … More Tensor Networks and Density Matrix Renormalization Group