64 citations · 142 across the 15 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…