280 citations · 1.1k across the 43 of their papers we have counts for
6 papers · 1 filter
A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging
Thomas Henn, Yasukazu Sakamoto, Clément Jacquet +8
Machine learning models commonly exhibit unexpected failures post-deployment due to either data shifts or uncommon situations in the training environment. Domain experts typically…
Interpreting diffusion score matching using normalizing flow
Wenbo Gong, Yingzhen Li
Scoring matching (SM), and its related counterpart, Stein discrepancy (SD) have achieved great success in model training and evaluations. However, recent research shows their limit…
Sparse Uncertainty Representation in Deep Learning with Inducing Weights
Hippolyt Ritter, Martin Kukla, Cheng Zhang +1
Bayesian neural networks and deep ensembles represent two modern paradigms of uncertainty quantification in deep learning. Yet these approaches struggle to scale mainly due to memo…
Contextual HyperNetworks for Novel Feature Adaptation
Angus Lamb, Evgeny Saveliev, Yingzhen Li +7
While deep learning has obtained state-of-the-art results in many applications, the adaptation of neural network architectures to incorporate new output features remains a challeng…
Active Slices for Sliced Stein Discrepancy
Wenbo Gong, Kaibo Zhang, Yingzhen Li +1
Sliced Stein discrepancy (SSD) and its kernelized variants have demonstrated promising successes in goodness-of-fit tests and model learning in high dimensions. Despite their theor…
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification
Yi Zhu, Ehsan Shareghi, Yingzhen Li +2
Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, th…