Danushka Bollegala’s Research Blog

Here is a list of common errors encountered when revising paper drafts. Please check these when you write papers (before you send them for me to check). Write the abstract first. Think of the keywords that you want to include in the title. Then think of a few candidate titles for your paper. Discuss these with your co-authors. Introduction: List up the things that you want to mention in the intro (same goes for all sections)....

Danushka Bollegala¹ Norikazu Tokusue² ¹University of Liverpool, United Kingdom. ²Cognite, AS, Tokyo, Japan. Abstract The proposal of two-systems to model human cognition has received a wide popularity in the Artificial Intelligence (AI) community. The co-existence of an intuition-driven fast-acting System One and a reasoning-based slower System Two exclusively decouples the different cognitive tasks humans carryout on a daily basis. However, several important questions quickly surface when one attempts to implement a working prototype based on the two-system model such as (a) the distribution of external sensory inputs between the two systems, (b) feedback of the rewards for the actions for learning parameters related to each system and (c) providing justifications to decisions made by the System One using System Two, to name a few....

Word embeddings attempt to represent the meaning of words using lower-dimensional real-valued vectors such that words that have similar meaning are embedded close to each other in the embedding space. Counting-based approaches that create word embeddings in a top-down fashion have been overtaken by prediction-based methods, which (randomly) initialise the embeddings and subsequently update them such that the words in their co-occurrence contexts can be accurately predicted. Arguably word2vec¹ is the most popular suite of algorithms for learning word embeddings but the history of neural embeddings dates back to the work by Bengio (2008)....

The problem of domain adaptation concerns with adapting a model (e.g. a classifier) on one particular domain (source domain) to be applicable in a different domain (target domain). The main difficulty in doing so is that the distributions from which data points $(x, y)$ are sampled differ between the source and the target domains. This can be a severe problem in many practical applications such as developing systems to diagnose diseases where it is relatively easy to obtain samples from patients diagnosed with that particular disease but difficult to obtain samples from healthy individuals....