64 citations · 142 across the 15 of their papers we have counts for
17 papers
A Preliminary Study on Explaining Risk of Code Changes using LLM-Based Prediction Models
Yalin Liu, Kosay Jabre, Rui Abreu +10
Predictions by machine learning (ML) and artificial intelligence (AI) models are often received skeptically unless they are paired with intelligible explanations. In the context of…
This Probably Looks Exactly Like That: An Invertible Prototypical Network
Zachariah Carmichael, Timothy Redgrave, Daniel Gonzalez Cedre +1
We combine concept-based neural networks with generative, flow-based classifiers into a novel, intrinsically explainable, exactly invertible approach to supervised learning. Protot…
How Well Do Feature-Additive Explainers Explain Feature-Additive Predictors?
Zachariah Carmichael, Walter J. Scheirer
Surging interest in deep learning from high-stakes domains has precipitated concern over the inscrutable nature of black box neural networks. Explainable AI (XAI) research has led…
Pixel-Grounded Prototypical Part Networks
Zachariah Carmichael, Suhas Lohit, Anoop Cherian +2
Prototypical part neural networks (ProtoPartNNs), namely PROTOPNET and its derivatives, are an intrinsically interpretable approach to machine learning. Their prototype learning sc…
HomOpt: A Homotopy-Based Hyperparameter Optimization Method
Sophia J. Abraham, Kehelwala D. G. Maduranga, Jeffery Kinnison +3
Machine learning has achieved remarkable success over the past couple of decades, often attributed to a combination of algorithmic innovations and the availability of high-quality…
Unfooling Perturbation-Based Post Hoc Explainers
Zachariah Carmichael, Walter J Scheirer
Monumental advancements in artificial intelligence (AI) have lured the interest of doctors, lenders, judges, and other professionals. While these high-stakes decision-makers are op…
Motif Mining: Finding and Summarizing Remixed Image Content
William Theisen, Daniel Gonzalez Cedre, Zachariah Carmichael +3
On the internet, images are no longer static; they have become dynamic content. Thanks to the availability of smartphones with cameras and easy-to-use editing software, images can…
Learning Interpretable Models Through Multi-Objective Neural Architecture Search
Zachariah Carmichael, Tim Moon, Sam Ade Jacobs
Monumental advances in deep learning have led to unprecedented achievements across various domains. While the performance of deep neural networks is indubitable, the architectural…
A Framework for Evaluating Post Hoc Feature-Additive Explainers
Zachariah Carmichael, Walter J. Scheirer
Many applications of data-driven models demand transparency of decisions, especially in health care, criminal justice, and other high-stakes environments. Modern trends in machine…
Adaptive Autonomy in Human-on-the-Loop Vision-Based Robotics Systems
Sophia Abraham, Zachariah Carmichael, Sreya Banerjee +5
Computer vision approaches are widely used by autonomous robotic systems to sense the world around them and to guide their decision making as they perform diverse tasks such as col…
SIRNet: Understanding Social Distancing Measures with Hybrid Neural Network Model for COVID-19 Infectious Spread
Nicholas Soures, David Chambers, Zachariah Carmichael +5
The SARS-CoV-2 infectious outbreak has rapidly spread across the globe and precipitated varying policies to effectuate physical distancing to ameliorate its impact. In this study,…
Cheetah: Mixed Low-Precision Hardware & Software Co-Design Framework for DNNs on the Edge
Hamed F. Langroudi, Zachariah Carmichael, David Pastuch +1
Low-precision DNNs have been extensively explored in order to reduce the size of DNN models for edge devices. Recently, the posit numerical format has shown promise for DNN data re…
Deep Learning Training on the Edge with Low-Precision Posits
Hamed F. Langroudi, Zachariah Carmichael, Dhireesha Kudithipudi
Recently, the posit numerical format has shown promise for DNN data representation and compute with ultra-low precision ([5..8]-bit). However, majority of studies focus only on DNN…
Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting
Zachariah Carmichael, Humza Syed, Dhireesha Kudithipudi
Echo state networks are computationally lightweight reservoir models inspired by the random projections observed in cortical circuitry. As interest in reservoir computing has grown…
Performance-Efficiency Trade-off of Low-Precision Numerical Formats in Deep Neural Networks
Zachariah Carmichael, Hamed F. Langroudi, Char Khazanov +3
Deep neural networks (DNNs) have been demonstrated as effective prognostic models across various domains, e.g. natural language processing, computer vision, and genomics. However,…
Deep Positron: A Deep Neural Network Using the Posit Number System
Zachariah Carmichael, Hamed F. Langroudi, Char Khazanov +3
The recent surge of interest in Deep Neural Networks (DNNs) has led to increasingly complex networks that tax computational and memory resources. Many DNNs presently use 16-bit or…
Mod-DeepESN: Modular Deep Echo State Network
Zachariah Carmichael, Humza Syed, Stuart Burtner +1
Neuro-inspired recurrent neural network algorithms, such as echo state networks, are computationally lightweight and thereby map well onto untethered devices. The baseline echo sta…