Papers by yoshika chhabra
ArXiv, 2021
Even skilled fantasy football managers can be disappointed by their mid-season rosters as some pl... more Even skilled fantasy football managers can be disappointed by their mid-season rosters as some players inevitably fall short of draft day expectations. Team managers can quickly discover that their team has a low score ceiling even if they start their best active players. A novel and diverse combinatorial optimization system proposes high volume and unique player trades between complementary teams to balance trade fairness. Several algorithms create the valuation of each fantasy football player with an ensemble of computing models: Quantum Support Vector Classifier with Permutation Importance (QSVC-PI), Quantum Support Vector Classifier with Accumulated Local Effects (QSVC-ALE), Variational Quantum Circuit with Permutation Importance (VQC-PI), Hybrid Quantum Neural Network with Permutation Importance (HQNN-PI), eXtreme Gradient Boosting Classifier (XGB), and Subject Matter Expert (SME) rules. The valuation of each player is personalized based on league rules, roster, and selections....
Feature or predictor importance is a crucial part of data preprocessing pipelines in classical ma... more Feature or predictor importance is a crucial part of data preprocessing pipelines in classical machine learning. Since classical data is used in many quantum machine learning models, feature importance is equally important for quantum machine learning (QML) models. This work presents the first study of its kind in which feature importance for QML models has been explored and contrasted with their classical machine learning (CML) equivalents. We developed a hybrid quantum classical architecture where QML models are trained and feature importance values are calculated from classical algorithms on a realworld dataset. This architecture has been implemented on ESPN fantasy football data using Qiskit statevector simulators and IBM Quantum hardware such as the IBMQ Mumbai and IBMQ Montreal systems. Even though we are in the Noisy Intermediate-Scale Quantum (NISQ) era, the physical quantum computing results are promising. To facilitate current quantum scale, we created a data tiering, mode...
Hybrid particle swarm training for convolution neural network (CNN)
2017 Tenth International Conference on Contemporary Computing (IC3), 2017
Convolutional Neural Networks(CNN) are one of the most used neural networks in the present time. ... more Convolutional Neural Networks(CNN) are one of the most used neural networks in the present time. Its applications are extremely varied. Most recently they have been proving helpful with deep learning, as well. Since it is growing in more convoluted domains, its training complexity is also increasing. To tackle this problem, many hybrid algorithms have been implemented. In this paper, Particle Swarm Optimization (PSO) is used to reduce the overall complexity of the algorithm. The hybrid of PSO used with CNN decreases the required number of epochs for training and the dependency on GPU system. The algorithm so designed is capable of achieving 3–4% increase in accuracy with lesser number of epochs. The advantage of which is decreased hardware requirements for training of CNNs. The hybrid training algorithm is also capable of overcoming the local minima problem of the regular backpropagation training methodology.
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Papers by yoshika chhabra