Masked Autoencoder for Distribution Estimation (MADE)
There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder’s parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering. Constrained this way, the autoencoder outputs can be interpreted as a set of conditional probabilities, and their product, the full joint probability. We can also train a single network that can decompose the joint probability in multiple different orderings. Our simple framework can be applied to multiple architectures, including deep ones. Vectorized implementations, such as on GPUs, are simple and fast. Experiments demonstrate that this approach is competitive with stateof- the-art tractable distribution estimators. At test time, the method is significantly faster and scales better than other autoregressive estimators.
GitXiv …

Skill2vec
Un-supervise learned word embeddings have seen tremendous success in numerous Natural Language Processing (NLP) tasks in recent years. The main contribution of this paper is to develop a technique called Skill2vec, which applies machine learning techniques in recruitment to enhance the search strategy to find the candidates who possess the right skills. Skill2vec is a neural network architecture which inspired by Word2vec, developed by Mikolov et al. in 2013, to transform a skill to a new vector space. This vector space has the characteristics of calculation and present their relationship. We conducted an experiment using AB testing in a recruitment company to demonstrate the effectiveness of our approach. …

Quantum Neural Network (QNN)
Quantum neural networks (QNNs) are neural network models which are based on the principles of quantum mechanics. There are two different approaches to QNN research, one exploiting quantum information processing to improve existing neural network models (sometimes also vice versa), and the other one searching for potential quantum effects in the brain. …