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Latest Blog
Why bidirectional RNN is better than unidirectional RNN: A theoretical proof
Ever wondered how Machine Learning research papers come up with their own mathematical proofs? In this article, we will provide you with a step-by-step, theoretical proof of why a bidirectional Recurrent Neural Network (RNN) performs empirically better than a unidirectional RNN. Our proof is novel and does not exist in Deep Learning literature. By going through this article, you will learn how to write your own proofs!
Latest Research
Evaluating the Robustness of Biomedical Concept Normalization
BERT is vulnerable to adversarial attacks and input transformations. This leads to the linkage of invalid inputs to concepts in an ontology. We study the robustness of different BERT-based Normalization models, including 13 different input transformations, and propose novel adversarial attacks. We found a significant drop in performance. Existing mitigation strategies are also explored.
Latest Project
How to perform Unsupervised Feature Selection using Supervised Algorithms
This article will provide you with a step-by-step process on how we came up with a new Machine Learning (ML) algorithm that performs unsupervised feature selection using any supervised algorithm of your choice such as XGBoost. Our goal is to provide you with insights into the process for developing a new ML algorithm.