Search Results for author:
Found 27 papers, 10 papers with code
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking Study
The paper contributes significantly by conducting an unbiased comparison of various anomaly detection algorithms, spanning classical machine learning including various tree-based approaches to deep learning and outlier detection methods.
Strong convexity-guided hyper-parameter optimization for flatter losses
We propose a novel white-box approach to hyper-parameter optimization.
DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries
Existing food delivery methods are sub-optimal because each delivery is individually optimized to go directly from the producer to the consumer via the shortest time path.
A novel RNA pseudouridine site prediction model using Utility Kernel and data-driven parameters
The existing models to predict the pseudouridine sites in a given RNA sequence mainly depend on user-defined features such as mono and dinucleotide composition/propensities of RNA sequences.
To prune or not to prune : A chaos-causality approach to principled pruning of dense neural networks
Reducing the size of a neural network (pruning) by removing weights without impacting its performance is an important problem for resource-constrained devices.
Decoupling Quantile Representations from Loss Functions
The simultaneous quantile regression (SQR) technique has been used to estimate uncertainties for deep learning models, but its application is limited by the requirement that the solution at the median quantile ({\tau} = 0. 5) must minimize the mean absolute error (MAE).
Correcting Model Misspecification via Generative Adversarial Networks
The "ABC-GAN" framework introduced is a novel generative modeling paradigm, which combines Generative Adversarial Networks (GANs) and Approximate Bayesian Computation (ABC).
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data
In particular, we propose Parametric Elliot Function (PEF) as an activation function (AF) inside LSTM, which saturates lately compared to sigmoid and tanh.
Investigation of a Machine learning methodology for the SKA pulsar search pipeline
We have successfully demonstrated this algorithm by detecting candidate signatures on a simulation dataset.
LogGENE: A smooth alternative to check loss for Deep Healthcare Inference Tasks
We propose log-cosh as a smooth-alternative to the check loss.
Hamiltonian Monte Carlo Particle Swarm Optimizer
We introduce the Hamiltonian Monte Carlo Particle Swarm Optimizer (HMC-PSO), an optimization algorithm that reaps the benefits of both Exponentially Averaged Momentum PSO and HMC sampling.
Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method
The algorithm is based on the postulate that Earth is an anomaly, with the possibility of existence of few other anomalies among thousands of data points.
Fairly Constricted Multi-Objective Particle Swarm Optimization
It has been well documented that the use of exponentially-averaged momentum (EM) in particle swarm optimization (PSO) is advantageous over the vanilla PSO algorithm.
A Swarm Variant for the Schrödinger Solver
This paper introduces application of the Exponentially Averaged Momentum Particle Swarm Optimization (EM-PSO) as a derivative-free optimizer for Neural Networks.
Estimation and Applications of Quantiles in Deep Binary Classification
We quantify the uncertainty of the class probabilities in terms of prediction intervals, and develop individualized confidence scores that can be used to decide whether a prediction is reliable or not at scoring time.
Automated Detection of Double Nuclei Galaxies using GOTHIC and the Discovery of a Large Sample of Dual AGN
We present a novel algorithm to detect double nuclei galaxies (DNG) called GOTHIC (Graph BOosted iterated HIll Climbing) - that detects whether a given image of a galaxy has two or more closely separated nuclei.
LALR: Theoretical and Experimental validation of Lipschitz Adaptive Learning Rate in Regression and Neural Networks
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 regression tasks.
AdaSwarm: Augmenting Gradient-Based optimizers in Deep Learning with Swarm Intelligence
This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks.
Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets
We leveraged the functional property of Mean Square Error, which is Lipschitz continuous to compute learning rate in shallow neural networks.
ChaosNet: A Chaos based Artificial Neural Network Architecture for Classification
Inspired by chaotic firing of neurons in the brain, we propose ChaosNet -- a novel chaos based artificial neural network architecture for classification tasks.
Evolution of Novel Activation Functions in Neural Network Training with Applications to Classification of Exoplanets
We present analytical exploration of novel activation functions as consequence of integration of several ideas leading to implementation and subsequent use in habitability classification of exoplanets.
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence
In this paper, we propose a novel method to compute the learning rate for training deep neural networks with stochastic gradient descent.
SBAF: A New Activation Function for Artificial Neural Net based Habitability Classification
We explore the efficacy of using a novel activation function in Artificial Neural Networks (ANN) in characterizing exoplanets into different classes.
TeamDL at SemEval-2018 Task 8: Cybersecurity Text Analysis using Convolutional Neural Network and Conditional Random Fields
For subtask 1 We experimented with two category of word embeddings namely native embeddings and task specific embedding using Word2vec and Glove algorithms.
Machine Learning in Astronomy: A Case Study in Quasar-Star Classification
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 Survey (SDSS), primarily distinguishing stars from quasars.
Predicting the direction of stock market prices using random forest
In this paper, we propose a novel way to minimize the risk of investment in stock market by predicting the returns of a stock using a class of powerful machine learning algorithms known as ensemble learning.
ASTROMLSKIT: A New Statistical Machine Learning Toolkit: A Platform for Data Analytics in Astronomy
Astroinformatics is a new impact area in the world of astronomy, occasionally called the final frontier, where several astrophysicists, statisticians and computer scientists work together to tackle various data intensive astronomical problems.
