7 papers
Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets
Rahul Yedida, Saad Mohammad Abrar, Cleber Melo-Filho +3
Objective: We aim to learn potential novel cures for diseases from unstructured text sources. More specifically, we seek to extract drug-disease pairs of potential cures to disease…
On the Value of Oversampling for Deep Learning in Software Defect Prediction
Rahul Yedida, Tim Menzies
One truism of deep learning is that the automatic feature engineering (seen in the first layers of those networks) excuses data scientists from performing tedious manual feature en…
Learning to Recognize Actionable Static Code Warnings (is Intrinsically Easy)
Xueqi Yang, Jianfeng Chen, Rahul Yedida +2
Static code warning tools often generate warnings that programmers ignore. Such tools can be made more useful via data mining algorithms that select the "actionable" warnings; i.e.…
Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets
Shailesh Sridhar, Snehanshu Saha, Azhar Shaikh +2
Rigorous mathematical investigation of learning rates used in back-propagation in shallow neural networks has become a necessity. This is because experimental evidence needs to be…
Evolution of Novel Activation Functions in Neural Network Training with Applications to Classification of Exoplanets
Snehanshu Saha, Nithin Nagaraj, Archana Mathur +1
We present analytical exploration of novel activation functions as consequence of integration of several ideas leading to implementation and subsequent use in habitability classifi…
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence
Rahul Yedida, Snehanshu Saha, Tejas Prashanth
Optimizing deep neural networks is largely thought to be an empirical process, requiring manual tuning of several hyper-parameters, such as learning rate, weight decay, and dropout…