2 citations · 2 across the 6 of their papers we have counts for
6 papers
Uncovering Memorization Effect in the Presence of Spurious Correlations
Chenyu You, Haocheng Dai, Yifei Min +3
Machine learning models often rely on simple spurious features -- patterns in training data that correlate with targets but are not causally related to them, like image backgrounds…
Refining Skewed Perceptions in Vision-Language Contrastive Models through Visual Representations
Haocheng Dai, Sarang Joshi
Large vision-language contrastive models (VLCMs), such as CLIP, have become foundational, demonstrating remarkable success across a variety of downstream tasks. Despite their advan…
Neural Operator Learning for Ultrasound Tomography Inversion
Haocheng Dai, Michael Penwarden, Robert M. Kirby +1
Neural operator learning as a means of mapping between complex function spaces has garnered significant attention in the field of computational science and engineering (CS&E). In t…
Modeling the Shape of the Brain Connectome via Deep Neural Networks
Haocheng Dai, Martin Bauer, P. Thomas Fletcher +1
The goal of diffusion-weighted magnetic resonance imaging (DWI) is to infer the structural connectivity of an individual subject's brain in vivo. To statistically study the variabi…
Integrated Construction of Multimodal Atlases with Structural Connectomes in the Space of Riemannian Metrics
Kristen M. Campbell, Haocheng Dai, Zhe Su +3
The structural network of the brain, or structural connectome, can be represented by fiber bundles generated by a variety of tractography methods. While such methods give qualitati…
Structural Connectome Atlas Construction in the Space of Riemannian Metrics
Kristen M. Campbell, Haocheng Dai, Zhe Su +3
The structural connectome is often represented by fiber bundles generated from various types of tractography. We propose a method of analyzing connectomes by representing them as a…