This post proves that the combination of softmax and cross-entropy loss ensures significant gradients by making the gradient the difference between predicted probabilities and actual labels, which aids effective learning.

This post introduces essence of the Einstein summation convention (Einsum).

This article discusses the method of the paper "Query2Box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings" by Ren et al [1] and explains the intution behind the defined box operations and distance metrics.

This article summarizes major researches that applied graph neural networks (GNN) to analyzing electronic health records (EHR).

This post summarizes three papers at the center of the debate on whether attention is interpretable.

• Attention is not explanation [1]
• Is attention interpretable? [4]
• Attention is not not explanation [5]

The paper (Sundararajan, Taly et al. 2017) identifies two axioms – sensitivity and implementation invariance that attribution methods ought to satisfy. Based on the axioms, the authors designed a new attribution method called integrated gradients. The method requires no modification to the original model and needs only a few calls to the standard gradient operator.