16 citations · 16 across the 3 of their papers we have counts for
4 papers
NARVis: Neural Accelerated Rendering for Real-Time Scientific Point Cloud Visualization
Srinidhi Hegde, Kaur Kullman, Thomas Grubb +3
Exploring scientific datasets with billions of samples in real-time visualization presents a challenge - balancing high-fidelity rendering with speed. This work introduces a neural…
Diffusion Models Beat GANs on Image Classification
Soumik Mukhopadhyay, Matthew Gwilliam, Vatsal Agarwal +5
While many unsupervised learning models focus on one family of tasks, either generative or discriminative, we explore the possibility of a unified representation learner: a model w…
Rethinking Common Assumptions to Mitigate Racial Bias in Face Recognition Datasets
Matthew Gwilliam, Srinidhi Hegde, Lade Tinubu +1
Many existing works have made great strides towards reducing racial bias in face recognition. However, most of these methods attempt to rectify bias that manifests in models during…
Variational Student: Learning Compact and Sparser Networks in Knowledge Distillation Framework
Srinidhi Hegde, Ranjitha Prasad, Ramya Hebbalaguppe +1
The holy grail in deep neural network research is porting the memory- and computation-intensive network models on embedded platforms with a minimal compromise in model accuracy. To…