In past blog posts, we discussed different models, objective functions, and hyperparameter choices that allow us to learn accurate word embeddings. However, these models are generally restricted to capture representations of words in the language they were trained on. The availability of resources, training data, and benchmarks in English... Read more
Today’s businesses run on data. It’s essential for any corporation to look for insights about their customers based on the data they collect. That collected information drives everything from business strategy to customer service. In order to retrieve insights from the massive amounts of data they collect, companies are... Read more
Superficially different in goals and approach, two recent algorithmic advances, Bayesian Program Learning and Galileo, are examples of one of the most interesting and powerful new trends in data analysis. It also happens to be the oldest one. Bayesian Program Learning (BPL) is deservedly one of the most discussed... Read more
She pined in thought, And with a green and yellow melancholy She sat like Patience on a monument, Smiling at grief. Was not this love indeed? In King Lear, Shakespeare stirs a sense of self-consciousness by invoking Patience, sitting up high; isolated in her... Read more
Table of contents: What is Transfer Learning? Why Transfer Learning Now? A Definition of Transfer Learning Transfer Learning Scenarios Applications of Transfer Learning Learning from simulations Adapting to new domains Transferring knowledge across languages Transfer Learning Methods Using pre-trained CNN features Learning domain-invariant representations Making representations more similar Confusing... Read more
Above is an image of the Zaamenkomst panel: one of the best remaining exemplars of rock art from the San people of Southern Africa. As soon as you see it, you are inevitably herded, like the eland in the scene, through a series of thoughts. Does it have a meaning? Why are the eland running?... Read more
