Digests » 98
In this work, we answer this question in the negative (strong reject, high confidence) and propose instead State-Of-the-Art Review (SOAR), a neoteric reviewing pipeline that serves as a 'plug-and-play' replacement for peer review. At the heart of our approach is an interpretation of the review process as a multi-objective, massively distributed and extremely-high-latency optimisation, which we scalarise and solve efficiently for PAC and CMT-optimal solutions.
In practical settings, labeling data is a time consuming and expensive process. Though, you have a lot of images, only a small portion of them can be labeled due to resource constraints. In such settings, how can we leverage the remaining unlabeled images along with the labeled images to improve the performance of our model? The answer is semi-supervised learning.
I was very fortunate to be given the opportunity by my employer to change role from a full-stack software developer to data scientist with the in-house Data Team. I've now spent over a year in this new data science role and thoroughly enjoy the different challenges it brings over software development.
Here, I cover the basic intuitions and mechanisms of Graph Neural Networks. Using colourful diagrams, I try to condense the essential steps needed to learn over structured graph data.
Coca-Cola is a well-known soft drink manufacturing company which has been rapidly adopting artificial intelligence. In the TensorFlow Dev Summit 2018, Patrick Brandt, the IT Director and Solutions Strategist at the Coca-Cola company presented a talk on how Coca-Cola has been applying AI in their loyalty campaigns.