53 citations · 71 across the 8 of their papers we have counts for
10 papers
Generative AI for FFRDCs
Arun S. Maiya
Federally funded research and development centers (FFRDCs) face text-heavy workloads, from policy documents to scientific and engineering papers, that are slow to analyze manually.…
OnPrem.LLM: A Privacy-Conscious Document Intelligence Toolkit
Arun S. Maiya
We present OnPremLLM, a Python-based toolkit for applying large language models (LLMs) to sensitive, non-public data in offline or restricted environments. The system is designe…
CausalNLP: A Practical Toolkit for Causal Inference with Text
Arun S. Maiya
Causal inference is the process of estimating the effect or impact of a treatment on an outcome with other covariates as potential confounders (and mediators) that may need to be c…
ktrain: A Low-Code Library for Augmented Machine Learning
Arun S. Maiya
We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply. As a wrapper to TensorFlow and many other libraries (e.g., transformer…
A Framework for Comparing Groups of Documents
Arun S. Maiya
We present a general framework for comparing multiple groups of documents. A bipartite graph model is proposed where document groups are represented as one node set and the compari…
Mining Measured Information from Text
Arun S. Maiya, Dale Visser, Andrew Wan
We present an approach to extract measured information from text (e.g., a 1370 degrees C melting point, a BMI greater than 29.9 kg/m^2 ). Such extractions are critically important…
Topic Similarity Networks: Visual Analytics for Large Document Sets
Arun S. Maiya, Robert M. Rolfe
We investigate ways in which to improve the interpretability of LDA topic models by better analyzing and visualizing their outputs. We focus on examining what we refer to as topic…
Exploratory Analysis of Highly Heterogeneous Document Collections
Arun S. Maiya, John P. Thompson, Francisco Loaiza-Lemos +1
We present an effective multifaceted system for exploratory analysis of highly heterogeneous document collections. Our system is based on intelligently tagging individual documents…
Benefits of Bias: Towards Better Characterization of Network Sampling
Arun S. Maiya, Tanya Y. Berger-Wolf
From social networks to P2P systems, network sampling arises in many settings. We present a detailed study on the nature of biases in network sampling strategies to shed light on h…
Expansion and Search in Networks
Arun S. Maiya, Tanya Y. Berger-Wolf
Borrowing from concepts in expander graphs, we study the expansion properties of real-world, complex networks (e.g. social networks, unstructured peer-to-peer or P2P networks) and…