Search Results for author:
Found 25 papers, 8 papers with code
QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits
In the present noisy intermediate scale quantum computing era, there is a critical need to devise methods for the efficient implementation of gate-based variational quantum circuits.
Dyport: Dynamic Importance-based Hypothesis Generation Benchmarking Technique
This paper presents a novel benchmarking framework Dyport for evaluating biomedical hypothesis generation systems.
QArchSearch: A Scalable Quantum Architecture Search Package
The current era of quantum computing has yielded several algorithms that promise high computational efficiency.
Literature-based Discovery for Landscape Planning
This project demonstrates how medical corpus hypothesis generation, a knowledge discovery field of AI, can be used to derive new research angles for landscape and urban planners.
Learning To Optimize Quantum Neural Network Without Gradients
This extension from classical to quantum domain has been made possible due to the development of hybrid quantum-classical algorithms that allow a parameterized quantum circuit to be optimized using gradient based algorithms that run on a classical computer.
Towards Practical Explainability with Cluster Descriptors
The goal is to find a representative set of tags for each cluster, referred to as the cluster descriptors, with the constraint that these descriptors we find are pairwise disjoint, and the total size of all the descriptors is minimized.
BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithms
In this paper, we propose an alternative strategy which initializes the parameters of a unitary gate by drawing from a beta distribution.
Proactive Query Expansion for Streaming Data Using External Source
Choosing which terms to add in order to improve the performance of the query expansion methods or to enhance the quality of the retrieved results is an important aspect of any information retrieval system.
CONFAIR: Configurable and Interpretable Algorithmic Fairness
The rapid growth of data in the recent years has led to the development of complex learning algorithms that are often used to make decisions in real world.
Coping with Mistreatment in Fair Algorithms
In this paper, we study the algorithmic fairness in a supervised learning setting and examine the effect of optimizing a classifier for the Equal Opportunity metric.
Accelerating COVID-19 research with graph mining and transformer-based learning
In 2020, the White House released the, "Call to Action to the Tech Community on New Machine Readable COVID-19 Dataset," wherein artificial intelligence experts are asked to collect data and develop text mining techniques that can help the science community answer high-priority scientific questions related to COVID-19.
Classical symmetries and the Quantum Approximate Optimization Algorithm
Our approach formalizes the connection between quantum symmetry properties of the QAOA dynamics and the group of classical symmetries of the objective function.
Accelerating Text Mining Using Domain-Specific Stop Word Lists
Eliminating domain-specific common words in a corpus reduces the dimensionality of the feature space, and improves the performance of text mining tasks.
AML-SVM: Adaptive Multilevel Learning with Support Vector Machines
The support vector machines (SVM) is one of the most widely used and practical optimization based classification models in machine learning because of its interpretability and flexibility to produce high quality results.
Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization
In addition, supervised graph representation learning requires labeled data, which is expensive and error-prone.
CBAG: Conditional Biomedical Abstract Generation
We propose a transformer-based conditional language model with a shallow encoder "condition" stack, and a deep "language model" stack of multi-headed attention blocks.
AGATHA: Automatic Graph-mining And Transformer based Hypothesis generation Approach
Hypothesis generation systems address this challenge by mining the wealth of publicly available scientific information to predict plausible research directions.
Hypergraph Partitioning With Embeddings
As a result, hypergraph partitioning is an NP-Hard problem to both solve or approximate.
FOBE and HOBE: First- and High-Order Bipartite Embeddings
Typical graph embeddings may not capture type-specific bipartite graph features that arise in such areas as recommender systems, data visualization, and drug discovery.
Large-Scale Validation of Hypothesis Generation Systems via Candidate Ranking
This method evaluates a HG system by its ability to rank hypotheses by plausibility; a process reminiscent of human candidate selection.
Algebraic multigrid support vector machines
The support vector machine is a flexible optimization-based technique widely used for classification problems.
Scalable Dynamic Topic Modeling with Clustered Latent Dirichlet Allocation (CLDA)
In this paper, we introduce and empirically analyze Clustered Latent Dirichlet Allocation (CLDA), a method for extracting dynamic latent topics from a collection of documents.
Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values
This work is motivated by the needs of predictive analytics on healthcare data as represented by Electronic Medical Records.
Fast Imbalanced Classification of Healthcare Data with Missing Values
In medical domain, data features often contain missing values.
Fast Multilevel Support Vector Machines
Solving different types of optimization models (including parameters fitting) for support vector machines on large-scale training data is often an expensive computational task.
