12 citations · 13 across the 4 of their papers we have counts for
5 papers
Understanding the Gain from Data Filtering in Multimodal Contrastive Learning
Divyansh Pareek, Sewoong Oh, Simon S. Du
The success of modern multimodal representation learning relies on internet-scale datasets. Due to the low quality of a large fraction of raw web data, data curation has become a c…
Understanding the Gains from Repeated Self-Distillation
Divyansh Pareek, Simon S. Du, Sewoong Oh
Self-Distillation is a special type of knowledge distillation where the student model has the same architecture as the teacher model. Despite using the same architecture and the sa…
The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders
Divyansh Pareek, Andrej Risteski
Training and using modern neural-network based latent-variable generative models (like Variational Autoencoders) often require simultaneously training a generative direction along…
Finding Input Characterizations for Output Properties in ReLU Neural Networks
Saket Dingliwal, Divyansh Pareek, Jatin Arora
Deep Neural Networks (DNNs) have emerged as a powerful mechanism and are being increasingly deployed in real-world safety-critical domains. Despite the widespread success, their co…
Strong Collapse for Persistence
Jean-Daniel Boissonnat, Siddharth Pritam, Divyansh Pareek
We introduce a fast and memory efficient approach to compute the persistent homology (PH) of a sequence of simplicial complexes. The basic idea is to simplify the complexes of the…