A First Glimpse of Rigetti’s Quantum Computing Cloud

Quantum computing has been picking up the momentum, and there are many startups and scholars discussing quantum machine learning. A basic knowledge of quantum two-level computation ought to be acquired. Recently, Rigetti, a startup for quantum computing service in Bay Area, published that they opened to public their cloud server for users to simulate the … More A First Glimpse of Rigetti’s Quantum Computing Cloud

Word Mover’s Distance as a Linear Programming Problem

Much about the use of word-embedding models such as Word2Vec and GloVe have been covered. However, how to measure the similarity between phrases or documents? One natural choice is the cosine similarity, as I have toyed with in a previous post. However, it smoothed out the influence of each word. Two years ago, a group … More Word Mover’s Distance as a Linear Programming Problem

Short Text Mining using Advanced Keras Layers and Maxent: shorttext 0.4.1

On 07/28/2017, shorttext published its release 0.4.1, with a few important updates. To install it, type the following in the OS X / Linux command line: >>> pip install -U shorttext The documentation in PythonHosted.org has been abandoned. It has been migrated to readthedocs.org. (URL: http://shorttext.readthedocs.io/ or http:// shorttext.rtfd.io) Exploiting the Word-Embedding Layer This update is mainly due … More Short Text Mining using Advanced Keras Layers and Maxent: shorttext 0.4.1

Simulation of Presidential Election 2016

Today is the presidential election. Regardless of the dirty things, we can do some simple simulation about the election. With the electoral college data, and the poll results from various sources, simple simulation can be performed. Look at this sophisticated model in R: http://blog.yhat.com/posts/predicting-the-presidential-election.html (If I have time after the election, I will do the simulation … More Simulation of Presidential Election 2016

Sammon Embedding

Word embedding has been a frequent theme of this blog. But the original embedding has been algorithms that perform a non-linear mapping of higher dimensional data to the lower one. This entry I will talk about one of the most oldest and widely used one: Sammon Embedding, published in 1969. This is an embedding algorithm … More Sammon Embedding