347 citations · 634 across the 14 of their papers we have counts for
15 papers
MEETING BOT: Reinforcement Learning for Dialogue Based Meeting Scheduling
Vishwanath D, Lovekesh Vig, Gautam Shroff +1
In this paper we present Meeting Bot, a reinforcement learning based conversational system that interacts with multiple users to schedule meetings. The system is able to interpret…
Evolutionary RL for Container Loading
S Saikia, R Verma, P Agarwal +3
Loading the containers on the ship from a yard, is an impor- tant part of port operations. Finding the optimal sequence for the loading of containers, is known to be computationall…
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…
Minimally-Supervised Attribute Fusion for Data Lakes
Karamjit Singh, Garima Gupta, Gautam Shroff +1
Aggregate analysis, such as comparing country-wise sales versus global market share across product categories, is often complicated by the unavailability of common join attributes,…
Deep Convolutional Neural Networks for Pairwise Causality
Karamjit Singh, Garima Gupta, Lovekesh Vig +2
Discovering causal models from observational and interventional data is an important first step preceding what-if analysis or counterfactual reasoning. As has been shown before, th…
Neuro-symbolic EDA-based Optimisation using ILP-enhanced DBNs
Sarmimala Saikia, Lovekesh Vig, Ashwin Srinivasan +3
We investigate solving discrete optimisation problems using the estimation of distribution (EDA) approach via a novel combination of deep belief networks(DBN) and inductive logic p…
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.…
Generation of Near-Optimal Solutions Using ILP-Guided Sampling
Ashwin Srinivasan, Gautam Shroff, Lovekesh Vig +2
Our interest in this paper is in optimisation problems that are intractable to solve by direct numerical optimisation, but nevertheless have significant amounts of relevant domain-…
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…
Relationship Queries on Large graphs using Pregel
Puneet Agarwal, Maya Ramanath, Gautam Shroff
Large-scale graph-structured data arising from social networks, databases, knowledge bases, web graphs, etc. is now available for analysis and mining. Graph-mining often involves '…
Efficiently Discovering Frequent Motifs in Large-scale Sensor Data
Puneet Agarwal, Gautam Shroff, Sarmimala Saikia +1
While analyzing vehicular sensor data, we found that frequently occurring waveforms could serve as features for further analysis, such as rule mining, classification, and anomaly d…
Warranty Cost Estimation Using Bayesian Network
Karamjit Singh, Puneet Agarwal, Gautam Shroff
All multi-component product manufacturing companies face the problem of warranty cost estimation. Failure rate analysis of components plays a key role in this problem. Data source…
Multi-Sensor Event Detection using Shape Histograms
Ehtesham Hassan, Gautam Shroff, Puneet Agarwal
Vehicular sensor data consists of multiple time-series arising from a number of sensors. Using such multi-sensor data we would like to detect occurrences of specific events that ve…
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…