Search Results for author:
Found 8 papers, 7 papers with code
Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data
Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure.
A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping
To address these challenges, we present a Self-supervised Learning-based Approach to Clustering multivariate Time-series data with missing values (SLAC-Time).
Execution-based Code Generation using Deep Reinforcement Learning
It's important to note that PPOCoder is a task-agnostic and model-agnostic framework that can be used across different code generation tasks and PLs.
WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Values
We also show that our Dynamic WindowSHAP algorithm focuses more on the most important time steps and provides more understandable explanations.
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury
The implemented methods were compared to one another in terms of several XAI characteristics such as understandability, fidelity, and stability.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence
To the best of our knowledge, it is the largest parallel dataset for source code both in terms of size and the number of languages.
StructCoder: Structure-Aware Transformer for Code Generation
This paper addresses the problem of code generation, where the goal is to generate target code given source code in a different language or a natural language description.
Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series
In addition, to tackle the problem of limited availability of labeled data (which is typically observed in many healthcare applications), STraTS utilizes self-supervision by leveraging unlabeled data to learn better representations by using time-series forecasting as an auxiliary proxy task.
