7 citations · 7 across the 3 of their papers we have counts for
6 papers
Deep Representation Learning-Based Dynamic Trajectory Phenotyping for Acute Respiratory Failure in Medical Intensive Care Units
Alan Wu, Tilendra Choudhary, Pulakesh Upadhyaya +3
Sepsis-induced acute respiratory failure (ARF) is a serious complication with a poor prognosis. This paper presents a deep representation learningbased phenotyping method to identi…
Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine
Pulakesh Upadhyaya, Kai Zhang, Can Li +2
Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care. This p…
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark
Yaobin Ling, Pulakesh Upadhyaya, Luyao Chen +2
Developing new drugs for target diseases is a time-consuming and expensive task, drug repurposing has become a popular topic in the drug development field. As much health claim dat…
CodNN -- Robust Neural Networks From Coded Classification
Netanel Raviv, Siddharth Jain, Pulakesh Upadhyaya +2
Deep Neural Networks (DNNs) are a revolutionary force in the ongoing information revolution, and yet their intrinsic properties remain a mystery. In particular, it is widely known…
Machine Learning for Error Correction with Natural Redundancy
Pulakesh Upadhyaya, Anxiao Jiang
The persistent storage of big data requires advanced error correction schemes. The classical approach is to use error correcting codes (ECCs). This work studies an alternative appr…
Representation-Oblivious Error Correction by Natural Redundancy
Pulakesh Upadhyaya, Anxiao, Jiang
Storage systems have a strong need for substantially improving their error correction capabilities, especially for long-term storage where the accumulating errors can exceed the de…