17 citations · 41 across the 14 of their papers we have counts for
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cs.LG2023★ 1 cited
Training-Free Uncertainty Estimation for Embedding Models
Young-Jin Park, Hao Wang, Addison Kristanto Julistiono +2
Embedding models, often obtained via self-supervised learning, extract general-purpose representations from data. Quantifying the reliability of these representations is crucial, a…
cs.CV2023
LLM2Loss: Leveraging Language Models for Explainable Model Diagnostics
Shervin Ardeshir
Trained on a vast amount of data, Large Language models (LLMs) have achieved unprecedented success and generalization in modeling fairly complex textual inputs in the abstract spac…
cs.CV2023
Improving Identity-Robustness for Face Models
Qi Qi, Shervin Ardeshir
Despite the success of deep-learning models in many tasks, there have been concerns about such models learning shortcuts, and their lack of robustness to irrelevant confounders. Wh…