1 citations · 1 across the 15 of their papers we have counts for
7 papers · 1 filter
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation
Protyay Dey, Ayush Roy, Hyuna Cho +2
Functional brain connectome represents neural connectivity as a matrix of pairwise interactions between brain regions. Generation of functional connectomes is not only a question o…
GeoTTER: Leveraging Local Geometry of Optimal Transport for Zero-Shot Classification
Wei-Yang Alex Lee, Rudrasis Chakraborty, Vishnu Lokhande
We present GeoTTER, a novel framework that redefines optimal transport in the realm of zero-shot classification. Conventional methods often suffer from miscalibration and a lack of…
Bayesian Data Reweighting Improves Multimodal Retrieval for Knowledge-Based Visual Question Answering
Jingchen Sun, Shaobo Han, Ruiyi Zhang +5
Multimodal retrievers are essential for knowledge-based visual question answering, where they retrieve external evidence for image-question pairs. However, existing contrastive tra…
Attending to Routers Aids Indoor Wireless Localization
Ayush Roy, Tahsin Fuad Hassan, Roshan Ayyalasomayajula +1
Modern machine learning-based wireless localization using Wi-Fi signals continues to face significant challenges in achieving groundbreaking performance across diverse environments…
Beyond Pooling: Matching for Robust Generalization under Data Heterogeneity
Ayush Roy, Rudrasis Chakraborty, Lav Varshney +1
Pooling heterogeneous datasets across domains is a common strategy in representation learning, but naive pooling can amplify distributional asymmetries and yield biased estimators,…
Model-Agnostic Gender Bias Control for Text-to-Image Generation via Sparse Autoencoder
Chao Wu, Zhenyi Wang, Kangxian Xie +3
Text-to-image (T2I) diffusion models often exhibit gender bias, particularly by generating stereotypical associations between professions and gendered subjects. This paper presents…
Pooling Image Datasets With Multiple Covariate Shift and Imbalance
Sotirios Panagiotis Chytas, Vishnu Suresh Lokhande, Peiran Li +1
Small sample sizes are common in many disciplines, which necessitates pooling roughly similar datasets across multiple institutions to study weak but relevant associations between…