171 citations
- GE Global Research (United States)US11 papers
- University at Albany, State University of New YorkUS4 papers
- University of Southern MississippiUS4 papers
- Institut Jean Le Rond d'AlembertFR3 papers
- General Electric (Israel)IL2 papers
- Michigan State UniversityUS2 papers
- Princeton Plasma Physics LaboratoryUS2 papers
- Ruhr University BochumDE2 papers
- Tel Aviv UniversityIL2 papers
- University of ArizonaUS2 papers
- University of California, BerkeleyUS2 papers
- Academy Of FinanceVN1 paper
44 papers
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
Panagiotis Tsilifis, Piyush Pandita, Sayan Ghosh +3
We present a Bayesian approach to identify optimal transformations that map model input points to low dimensional latent variables. The "projection" mapping consists of an orthonor…
Advances in Bayesian Probabilistic Modeling for Industrial Applications
Sayan Ghosh, Piyush Pandita, Steven Atkinson +5
Industrial applications frequently pose a notorious challenge for state-of-the-art methods in the contexts of optimization, designing experiments and modeling unknown physical resp…
Sparse Polynomial Chaos expansions using Variational Relevance Vector Machines
Panagiotis Tsilifis, Iason Papaioannou, Daniel Straub +1
The challenges for non-intrusive methods for Polynomial Chaos modeling lie in the computational efficiency and accuracy under a limited number of model simulations. These challenge…
Justification-Based Reliability in Machine Learning
Nurali Virani, Naresh Iyer, Zhaoyuan Yang
With the advent of Deep Learning, the field of machine learning (ML) has surpassed human-level performance on diverse classification tasks. At the same time, there is a stark need…
Multitask Classification and Segmentation for Cancer Diagnosis in Mammography
Thi-Lam-Thuy Le, Nicolas Thome, Sylvain Bernard +2
Annotation cost is a bottleneck for collecting massive data in mammography, especially for training deep neural networks. In this paper, we study the use of heterogeneous levels of…
On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach
Weizhong Yan, Lijie Yu
Monitoring gas turbine combustors health, in particular, early detecting abnormal behaviors and incipient faults, is critical in ensuring gas turbines operating efficiently and in…