I had a good weekend. Got to ride in the mountains. Actually finished my chores, to I didn’t get to paying bills. Saw my sister – outside, 8′ apart, much more careful than last time. Went on a date.
- We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose, TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.
- Back to draft zero – grinding along
- Check out Vadim’s rwheel results today.
- Work on calculating the contributions from the rwheels to rotation around an arbitrary vector
- Write up second review – done!
- Started on third paper