Posts about different aspects of transfer learning.
natural language processing
This post discusses highlights of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018). It focuses on understanding representations and evaluating in more challenging scenarios.
natural language processing
Big changes are underway in the world of NLP. The long reign of word vectors as NLP's core representation technique has seen an exciting new line of challengers emerge. These approaches demonstrated that pretrained language models can achieve state-of-the-art results and herald a watershed moment.
natural language processing
This post discusses highlights of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018). It focuses on Generalization, the Test-of-Time awards, and Dialogue Systems.
semi-supervised learning
While unsupervised learning is still elusive, researchers have made a lot of progress in semi-supervised learning. This post focuses on a particular promising category of semi-supervised learning methods that assign proxy labels to unlabelled data, which are used as targets for learning.
transfer learning
It can be hard to find compelling topics to work on and know what questions to ask when you are just starting as a researcher. This post aims to provide inspiration and ideas for research directions to junior researchers and those trying to get into research.
domain adaptation
Domain adaptation methods typically seek to identify features that are shared between the domains or learn representations that are general enough to be useful for both domains. This post discusses a complementary approach to domain adaptation that selects data that is useful for training the model.
multi-task learning
Multi-task learning is becoming more and more popular. This post gives a general overview of the current state of multi-task learning. In particular, it provides context for current neural network-based methods by discussing the extensive multi-task learning literature.
transfer learning
Deep learning models excel at learning from a large number of labeled examples, but typically do not generalize to conditions not seen during training. This post gives an overview of transfer learning, motivates why it warrants our application, and discusses practical applications and methods.