112 citations · 136 across the 2 of their papers we have counts for
6 papers
Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation
Jyoti Narwariya, Pankaj Malhotra, Vishnu TV +2
Automated equipment health monitoring from streaming multisensor time-series data can be used to enable condition-based maintenance, avoid sudden catastrophic failures, and ensure…
Meta-Learning for Few-Shot Time Series Classification
Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig +2
Deep neural networks (DNNs) have achieved state-of-the-art results on time series classification (TSC) tasks. In this work, we focus on leveraging DNNs in the often-encountered pra…
Meta-Learning for Black-box Optimization
Vishnu TV, Pankaj Malhotra, Jyoti Narwariya +2
Recently, neural networks trained as optimizers under the "learning to learn" or meta-learning framework have been shown to be effective for a broad range of optimization tasks inc…
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models
Vishnu TV, Diksha, Pankaj Malhotra +2
Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of…
Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks
Narendhar Gugulothu, Vishnu TV, Pankaj Malhotra +3
We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assump…
TimeNet: Pre-trained deep recurrent neural network for time series classification
Pankaj Malhotra, Vishnu TV, Lovekesh Vig +2
Inspired by the tremendous success of deep Convolutional Neural Networks as generic feature extractors for images, we propose TimeNet: a deep recurrent neural network (RNN) trained…