When designing deep neural networks we have to decide on its depth/width. These so-called hyper-parameters need to be tuned based on the dataset, available resources, etc. Different studies have compared the performance
In this article, I will be summarising the methods and findings of a paper titled ANU-Net: Attention-based nested U-Net to exploit full resolution features for medical image segmentation, published in May 2020 by
In this article, I'm going to summarise a paper with the above title that was published in October 2020. This paper focuses on a common problem in machine learning, overconfident classifiers. What do
In this post I am going to discuss a very interesting paper on the fusion of graph transformer networks and reinforcement learning (RL). This paper proposes a graph-aided transformer agent (GATA) that can
I was reading a paper on a combination of brain-computer interfaces (BCI) and generative adversarial networks (GAN) a few days ago and thought I should write an introductory article on what BCIs are