activity
20162018
most citedLSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

347 citations · 459 across the 3 of their papers we have counts for

collaborators

6 papers

cs.AI2018

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…

cs.AI2018

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…

cs.LG2017

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…

cs.LG2017112 cited

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…

cs.AI2016347 cited

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…

cs.DB2016

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 '…