29 citations · 55 across the 21 of their papers we have counts for
15 papers · 1 filter
ManifoldGD: Training-Free Hierarchical Manifold Guidance for Diffusion-Based Dataset Distillation
Ayush Roy, Wei-Yang Alex Lee, Rudrasis Chakraborty +1
In recent times, large datasets hinder efficient model training while also containing redundant concepts. Dataset distillation aims to synthesize compact datasets that preserve the…
On the Versatile Uses of Partial Distance Correlation in Deep Learning
Xingjian Zhen, Zihang Meng, Rudrasis Chakraborty +1
Comparing the functional behavior of neural network models, whether it is a single network over time or two (or more networks) during or post-training, is an essential step in unde…
VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifolds
Monami Banerjee, Rudrasis Chakraborty, Jose Bouza +1
Convolutional neural networks have been highly successful in image-based learning tasks due to their translation equivariance property. Recent work has generalized the traditional…
Flow-based Generative Models for Learning Manifold to Manifold Mappings
Xingjian Zhen, Rudrasis Chakraborty, Liu Yang +1
Many measurements or observations in computer vision and machine learning manifest as non-Euclidean data. While recent proposals (like spherical CNN) have extended a number of deep…
Orthogonal Convolutional Neural Networks
Jiayun Wang, Yubei Chen, Rudrasis Chakraborty +1
Deep convolutional neural networks are hindered by training instability and feature redundancy towards further performance improvement. A promising solution is to impose orthogonal…
POIRot: A rotation invariant omni-directional pointnet
Liu Yang, Rudrasis Chakraborty, Stella X. Yu
Point-cloud is an efficient way to represent 3D world. Analysis of point-cloud deals with understanding the underlying 3D geometric structure. But due to the lack of smooth topolog…