Technology - Publications - Faculty & Research

2024
Conference Paper

Quantifying Uncertainty in Natural Language Explanations of Large Language Models

By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal

Large Language Models (LLMs) are increasingly used as powerful tools for several high-stakes natural language processing (NLP) applications. Recent prompting works claim to elicit intermediate reasoning steps and key tokens that serve as proxy explanations for LLM... View Details

Keywords: Large Language Model; AI and Machine Learning

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2023
Article

Evaluating Explainability for Graph Neural Networks

By: Chirag Agarwal, Owen Queen, Himabindu Lakkaraju and Marinka Zitnik

As explanations are increasingly used to understand the behavior of graph neural networks (GNNs), evaluating the quality and reliability of GNN explanations is crucial. However, assessing the quality of GNN explanations is challenging as existing graph datasets have no... View Details

Keywords: Analytics and Data Science

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2022
Article

Efficiently Training Low-Curvature Neural Networks

By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret

Standard deep neural networks often have excess non-linearity, making them susceptible to issues such as low adversarial robustness and gradient instability. Common methods to address these downstream issues, such as adversarial training, are expensive and often... View Details

Keywords: AI and Machine Learning

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Article

Adaptive Machine Unlearning

By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites

Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees... View Details

Keywords: Machine Learning; AI and Machine Learning

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2021
Article

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation

By: Chuang Gan, Jeremy Schwartz, Seth Alter, Damian Mrowca, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Michael Lingelbach, Aidan Curtis, Kevin Feigelis, Daniel M. Bear, Dan Gutfreund, David Cox, Antonio Torralba, James J. DiCarlo, Joshua B. Tenenbaum, Josh H. McDermott and Daniel L.K. Yamins

We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between mobile agents and objects in rich 3D environments. Unique properties include: real-time... View Details

Keywords: Artificial Intelligence; Platform; Interactive Physical Simulation; Virtual Environment; Multi-modal; AI and Machine Learning

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Article

Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs

By: Michael G. Endres, Florian Hillen, Marios Salloumis, Ahmad R. Sedaghat, Stefan M. Niehues, Olivia Quatela, Henning Hanken, Ralf Smeets, Benedicta Beck-Broichsitter, Carsten Rendenbach, Karim R. Lakhani, Max Helland and Robert A. Gaudin

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts, and tumors. In this study, we seek to investigate the... View Details

Keywords: Artificial Intelligence; Diagnosis; Computer-assisted; Image Interpretation; Machine Learning; Radiography; Panoramic Radiograph; AI and Machine Learning

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