22 citations · 102 across the 35 of their papers we have counts for
50 papers
Predicting Fine-Tuning Performance with Probing
Zining Zhu, Soroosh Shahtalebi, Frank Rudzicz
Large NLP models have recently shown impressive performance in language understanding tasks, typically evaluated by their fine-tuned performance. Alternatively, probing has receive…
Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data
Malikeh Ehghaghi, Frank Rudzicz, Jekaterina Novikova
A significant number of studies apply acoustic and linguistic characteristics of human speech as prominent markers of dementia and depression. However, studies on discriminating de…
wildNeRF: Complete view synthesis of in-the-wild dynamic scenes captured using sparse monocular data
Shuja Khalid, Frank Rudzicz
We present a novel neural radiance model that is trainable in a self-supervised manner for novel-view synthesis of dynamic unstructured scenes. Our end-to-end trainable algorithm l…
Conformal Mirror Descent with Logarithmic Divergences
Amanjit Singh Kainth, Ting-Kam Leonard Wong, Frank Rudzicz
The logarithmic divergence is an extension of the Bregman divergence motivated by optimal transport and a generalized convex duality, and satisfies many remarkable properties. Usin…
Detoxifying Language Models with a Toxic Corpus
Yoon A Park, Frank Rudzicz
Existing studies have investigated the tendency of autoregressive language models to generate contexts that exhibit undesired biases and toxicity. Various debiasing approaches have…
MeSHup: A Corpus for Full Text Biomedical Document Indexing
Xindi Wang, Robert E. Mercer, Frank Rudzicz
Medical Subject Heading (MeSH) indexing refers to the problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms. Curr…