78 citations · 144 across the 11 of their papers we have counts for
16 papers
Towards PAC Multi-Object Detection and Tracking
Shuo Li, Sangdon Park, Xiayan Ji +2
Accurately detecting and tracking multi-objects is important for safety-critical applications such as autonomous navigation. However, it remains challenging to provide guarantees o…
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
Ramneet Kaur, Susmit Jha, Anirban Roy +4
Machine learning methods such as deep neural networks (DNNs), despite their success across different domains, are known to often generate incorrect predictions with high confidence…
Detecting OODs as datapoints with High Uncertainty
Ramneet Kaur, Susmit Jha, Anirban Roy +3
Deep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on out-of-distribution inputs (OODs). This limitation is one of the key challenges…
Improving Neural Network Robustness via Persistency of Excitation
Kaustubh Sridhar, Oleg Sokolsky, Insup Lee +1
Improving adversarial robustness of neural networks remains a major challenge. Fundamentally, training a neural network via gradient descent is a parameter estimation problem. In a…
ModelGuard: Runtime Validation of Lipschitz-continuous Models
Taylor J. Carpenter, Radoslav Ivanov, Insup Lee +1
This paper presents ModelGuard, a sampling-based approach to runtime model validation for Lipschitz-continuous models. Although techniques exist for the validation of many classes…
Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs
Ramneet Kaur, Susmit Jha, Anirban Roy +2
Deep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on out-of-distribution (OOD) inputs. This limitation is one of the key challenges i…