115 citations · 204 across the 30 of their papers we have counts for
35 papers
To prune or not to prune : A chaos-causality approach to principled pruning of dense neural networks
Rajan Sahu, Shivam Chadha, Nithin Nagaraj +2
Reducing the size of a neural network (pruning) by removing weights without impacting its performance is an important problem for resource-constrained devices. In the past, pruning…
Correcting Model Misspecification via Generative Adversarial Networks
Pronoma Banerjee, Manasi V Gude, Rajvi J Sampat +3
Machine learning models are often misspecified in the likelihood, which leads to a lack of robustness in the predictions. In this paper, we introduce a framework for correcting lik…
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data
Snehanshu Saha, Jyotirmoy Sarkar, Soma Dhavala +2
Anomalies refer to the departure of systems and devices from their normal behaviour in standard operating conditions. An anomaly in an industrial device can indicate an upcoming fa…
Investigation of a Machine learning methodology for the SKA pulsar search pipeline
Shashank Sanjay Bhat, Thiagaraj Prabu, Ben Stappers +4
The SKA pulsar search pipeline will be used for real time detection of pulsars. Modern radio telescopes such as SKA will be generating petabytes of data in their full scale of oper…
Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method
Jyotirmoy Sarkar, Kartik Bhatia, Snehanshu Saha +2
A profound shift in the study of cosmology came with the discovery of thousands of exoplanets and the possibility of the existence of billions of them in our Galaxy. The biggest go…
A Swarm Variant for the Schrödinger Solver
Urvil Nileshbhai Jivani, Omatharv Bharat Vaidya, Anwesh Bhattacharya +1
This paper introduces application of the Exponentially Averaged Momentum Particle Swarm Optimization (EM-PSO) as a derivative-free optimizer for Neural Networks. It adopts PSO's ma…
Estimation and Applications of Quantiles in Deep Binary Classification
Anuj Tambwekar, Anirudh Maiya, Soma Dhavala +1
Quantile regression, based on check loss, is a widely used inferential paradigm in Econometrics and Statistics. The conditional quantiles provide a robust alternative to classical…
LALR: Theoretical and Experimental validation of Lipschitz Adaptive Learning Rate in Regression and Neural Networks
Snehanshu Saha, Tejas Prashanth, Suraj Aralihalli +3
We propose a theoretical framework for an adaptive learning rate policy for the Mean Absolute Error loss function and Quantile loss function and evaluate its effectiveness for regr…
Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets
Shailesh Sridhar, Snehanshu Saha, Azhar Shaikh +2
Rigorous mathematical investigation of learning rates used in back-propagation in shallow neural networks has become a necessity. This is because experimental evidence needs to be…
ChaosNet: A Chaos based Artificial Neural Network Architecture for Classification
Harikrishnan Nellippallil Balakrishnan, Aditi Kathpalia, Snehanshu Saha +1
Inspired by chaotic firing of neurons in the brain, we propose ChaosNet -- a novel chaos based artificial neural network architecture for classification tasks. ChaosNet is built us…
Evolution of Novel Activation Functions in Neural Network Training with Applications to Classification of Exoplanets
Snehanshu Saha, Nithin Nagaraj, Archana Mathur +1
We present analytical exploration of novel activation functions as consequence of integration of several ideas leading to implementation and subsequent use in habitability classifi…
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence
Rahul Yedida, Snehanshu Saha, Tejas Prashanth
Optimizing deep neural networks is largely thought to be an empirical process, requiring manual tuning of several hyper-parameters, such as learning rate, weight decay, and dropout…
SBAF: A New Activation Function for Artificial Neural Net based Habitability Classification
Snehanshu Saha, Archana Mathur, Kakoli Bora +2
We explore the efficacy of using a novel activation function in Artificial Neural Networks (ANN) in characterizing exoplanets into different classes. We call this Saha-Bora Activat…
A Novel Exoplanetary Habitability Score via Particle Swarm Optimization of CES Production Functions
Abhijit Theophilus, Snehanshu Saha, Suryoday Basak +1
The search for life has two goals essentially: looking for planets with Earth-like conditions (Earth similarity) and looking for the possibility of life in some form (habitability)…
Habitability Classification of Exoplanets: A Machine Learning Insight
Suryoday Basak, Surbhi Agrawal, Snehanshu Saha +4
We explore the efficacy of machine learning (ML) in characterizing exoplanets into different classes. The source of the data used in this work is University of Puerto Rico's Planet…
ALVEC: Auto-scaling by Lotka Volterra Elastic Cloud: A QoS aware Non Linear Dynamical Allocation Model
Bidisha Goswami, Jyotirmoy Sarkar, Snehanshu Saha +2
Elasticity in resource allocation is still a relevant problem in cloud computing. There are many academic and white papers which have investigated the problem and offered solutions…
Time Reversed Delay Differential Equation Based Modeling Of Journal Influence In An Emerging Area
Poulami Sarkar, Snehanshu Saha, Archana Mathur +4
A recent independent study resulted in a ranking system which ranked Astronomy and Computing (ASCOM) much higher than most of the older journals highlighting its niche prominence.…
Use of NoSQL database and visualization techniques to analyze massive scholarly article data from journals
Gouri Ginde, Snehanshu Saha, Archana Mathur +3
Visualization of the massive data is a challenging endeavor. Extracting data and providing graphical representations can aid in its effective utilization in terms of interpretation…
Machine Learning in Astronomy: A Case Study in Quasar-Star Classification
Mohammed Viquar, Suryoday Basak, Ariruna Dasgupta +2
We present the results of various automated classification methods, based on machine learning (ML), of objects from data releases 6 and 7 (DR6 and DR7) of the Sloan Digital Sky Sur…
Model Visualization in understanding rapid growth of a journal in an emerging area
Snehanshu Saha, Poulami Sarkar, Archana Mathur +1
A recent independent study resulted in a ranking system which ranked Astronomy and Computing (ASCOM) much higher than most of the older journals highlighting the niche prominence o…
Genealogy tree: understanding academic lineage of authors via algorithmic and visual analysis
Sandra Anil, Abu Kurian, Sudeepa Roy Dey +2
Ancestry and genealogy tree are proven tools to determine the lineage of any person and establish dependencies among individuals. Genealogy tree can be exploited further to gain in…
CDSFA Stochastic Frontier Analysis Approach to Revenue Modeling in Large Cloud Data Centers
Jyotirmoy Sarkar, Bidisha Goswami, Snehanshu Saha +1
Enterprises are investing heavily in cloud data centers to meet the ever surging business demand. Data Center is a facility, which houses computer systems and associated components…
A Study of Revenue Cost Dynamics in Large Data Centers: A Factorial Design Approach
Gambhire Swati Sampatrao, Sudeepa Roy Dey, Bidisha Goswami +2
Revenue optimization of large data centers is an open and challenging problem. The intricacy of the problem is due to the presence of too many parameters posing as costs or investm…
CISER: An Amoebiasis inspired Model for Epidemic Message Propagation in DTN
Sobin CC, Snehanshu Saha, Vaskar Raychoudhury +2
Delay Tolerant Networks (DTNs) are sparse mobile networks, which experiences frequent disruptions in connectivity among nodes. Usually, DTN follows store-carry-and forward mechanis…