5 citations · 15 across the 8 of their papers we have counts for
8 papers
Generalized Random Direction Newton Algorithms for Stochastic Optimization
Soumen Pachal, Prashanth L. A., Shalabh Bhatnagar +1
We present a family of generalized Hessian estimators of the objective using random direction stochastic approximation (RDSA) by utilizing only noisy function measurements. The for…
Public Transit Arrival Prediction: a Seq2Seq RNN Approach
Nancy Bhutani, Soumen Pachal, Avinash Achar
Arrival/Travel times for public transit exhibit variability on account of factors like seasonality, dwell times at bus stops, traffic signals, travel demand fluctuation etc. The de…
Sequence Prediction Under Missing Data : An RNN Approach Without Imputation
Soumen Pachal, Avinash Achar
Missing data scenarios are very common in ML applications in general and time-series/sequence applications are no exceptions. This paper pertains to a novel Recurrent Neural Networ…
Seasonal Encoder-Decoder Architecture for Forecasting
Avinash Achar, Soumen Pachal
Deep learning (DL) in general and Recurrent neural networks (RNNs) in particular have seen high success levels in sequence based applications. This paper pertains to RNNs for time…
Bus Travel Time Prediction: A Lognormal Auto-Regressive (AR) Modeling Approach
B. Dhivyabharathi, B. Anil Kumar, Avinash Achar +1
Providing real time information about the arrival time of the transit buses has become inevitable in urban areas to make the system more user-friendly and advantageous over various…
Predicting vehicular travel times by modeling heterogeneous influences between arterial roads
Avinash Achar, Venkatesh Sarangan, R Rohith +1
Predicting travel times of vehicles in urban settings is a useful and tangible quantity of interest in the context of intelligent transportation systems. We address the problem of…
A unified view of Automata-based algorithms for Frequent Episode Discovery
Avinash Achar, Srivatsan Laxman, P. S. Sastry
Frequent Episode Discovery framework is a popular framework in Temporal Data Mining with many applications. Over the years many different notions of frequencies of episodes have be…
Discovering general partial orders in event streams
Avinash Achar, Srivatsan Laxman, Raajay Viswanathan +1
Frequent episode discovery is a popular framework for pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event typ…