347 citations · 728 across the 17 of their papers we have counts for
20 papers
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions
Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra +2
We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, o…
Learning to Liquidate Forex: Optimal Stopping via Adaptive Top-K Regression
Diksha Garg, Pankaj Malhotra, Anil Bhatia +3
We consider learning a trading agent acting on behalf of the treasury of a firm earning revenue in a foreign currency (FC) and incurring expenses in the home currency (HC). The goa…
Electricity Consumption Forecasting for Out-of-distribution Time-of-Use Tariffs
Jyoti Narwariya, Chetan Verma, Pankaj Malhotra +3
In electricity markets, retailers or brokers want to maximize profits by allocating tariff profiles to end consumers. One of the objectives of such demand response management is to…
Systematic Generalization in Neural Networks-based Multivariate Time Series Forecasting Models
Hritik Bansal, Gantavya Bhatt, Pankaj Malhotra +1
Systematic generalization aims to evaluate reasoning about novel combinations from known components, an intrinsic property of human cognition. In this work, we study systematic gen…
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation
Diksha Garg, Priyanka Gupta, Pankaj Malhotra +2
Most of the existing deep reinforcement learning (RL) approaches for session-based recommendations either rely on costly online interactions with real users, or rely on potentially…
Handling Variable-Dimensional Time Series with Graph Neural Networks
Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra +2
Several applications of Internet of Things (IoT) technology involve capturing data from multiple sensors resulting in multi-sensor time series. Existing neural networks based appro…
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…
NISER: Normalized Item and Session Representations to Handle Popularity Bias
Priyanka Gupta, Diksha Garg, Pankaj Malhotra +2
The goal of session-based recommendation (SR) models is to utilize the information from past actions (e.g. item/product clicks) in a session to recommend items that a user is likel…
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…
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification
Kathan Kashiparekh, Jyoti Narwariya, Pankaj Malhotra +2
Training deep neural networks often requires careful hyper-parameter tuning and significant computational resources. In this paper, we propose ConvTimeNet (CTN): an off-the-shelf d…
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks
Priyanka Gupta, Pankaj Malhotra, Jyoti Narwariya +2
Deep neural networks have shown promising results for various clinical prediction tasks. However, training deep networks such as those based on Recurrent Neural Networks (RNNs) req…
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…
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks
Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig +1
Deep neural networks have shown promising results for various clinical prediction tasks such as diagnosis, mortality prediction, predicting duration of stay in hospital, etc. Howev…
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…
Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder
Pankaj Malhotra, Vishnu TV, Anusha Ramakrishnan +4
Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e.g.…
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand +3
Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine. However, there ar…
ODE - Augmented Training Improves Anomaly Detection in Sensor Data from Machines
Mohit Yadav, Pankaj Malhotra, Lovekesh Vig +2
Machines of all kinds from vehicles to industrial equipment are increasingly instrumented with hundreds of sensors. Using such data to detect anomalous behaviour is critical for sa…
Incremental Entity Resolution from Linked Documents
Pankaj Malhotra, Puneet Agarwal, Gautam Shroff
In many government applications we often find that information about entities, such as persons, are available in disparate data sources such as passports, driving licences, bank ac…