47 citations · 79 across the 14 of their papers we have counts for
15 papers
Synergizing Contrastive Learning and Optimal Transport for 3D Point Cloud Domain Adaptation
Siddharth Katageri, Arkadipta De, Chaitanya Devaguptapu +3
Recently, the fundamental problem of unsupervised domain adaptation (UDA) on 3D point clouds has been motivated by a wide variety of applications in robotics, virtual reality, and…
BERTops: Studying BERT Representations under a Topological Lens
Jatin Chauhan, Manohar Kaul
Proposing scoring functions to effectively understand, analyze and learn various properties of high dimensional hidden representations of large-scale transformer models like BERT c…
ALLWAS: Active Learning on Language models in WASserstein space
Anson Bastos, Manohar Kaul
Active learning has emerged as a standard paradigm in areas with scarcity of labeled training data, such as in the medical domain. Language models have emerged as the prevalent cho…
Target Model Agnostic Adversarial Attacks with Query Budgets on Language Understanding Models
Jatin Chauhan, Karan Bhukar, Manohar Kaul
Despite significant improvements in natural language understanding models with the advent of models like BERT and XLNet, these neural-network based classifiers are vulnerable to bl…
Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach
Manohar Kaul, Masaaki Imaizumi
Dynamic graphs are rife with higher-order interactions, such as co-authorship relationships and protein-protein interactions in biological networks, that naturally arise between mo…
Self-Supervised Few-Shot Learning on Point Clouds
Charu Sharma, Manohar Kaul
The increased availability of massive point clouds coupled with their utility in a wide variety of applications such as robotics, shape synthesis, and self-driving cars has attract…
A Weighted Quiver Kernel using Functor Homology
Manohar Kaul, Dai Tamaki
In this paper, we propose a new homological method to study weighted directed networks. Our model of such networks is a directed graph equipped with a weight function on th…
Simplicial Complex based Point Correspondence between Images warped onto Manifolds
Charu Sharma, Manohar Kaul
Recent increase in the availability of warped images projected onto a manifold (e.g., omnidirectional spherical images), coupled with the success of higher-order assignment methods…
Learning Representations using Spectral-Biased Random Walks on Graphs
Charu Sharma, Jatin Chauhan, Manohar Kaul
Several state-of-the-art neural graph embedding methods are based on short random walks (stochastic processes) because of their ease of computation, simplicity in capturing complex…
Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures
Jatin Chauhan, Deepak Nathani, Manohar Kaul
We propose to study the problem of few shot graph classification in graph neural networks (GNNs) to recognize unseen classes, given limited labeled graph examples. Despite several…
Solving Partial Assignment Problems using Random Clique Complexes
Charu Sharma, Deepak Nathani, Manohar Kaul
We present an alternate formulation of the partial assignment problem as matching random clique complexes, that are higher-order analogues of random graphs, designed to provide a s…
Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
Deepak Nathani, Jatin Chauhan, Charu Sharma +1
The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of re…
Understanding and Predicting Links in Graphs: A Persistent Homology Perspective
Sumit Bhatia, Bapi Chatterjee, Deepak Nathani +1
Persistent Homology is a powerful tool in Topological Data Analysis (TDA) to capture topological properties of data succinctly at different spatial resolutions. For graphical data,…
Sharing Hash Codes for Multiple Purposes
Wikor Pronobis, Danny Panknin, Johannes Kirschnick +6
Locality sensitive hashing (LSH) is a powerful tool for sublinear-time approximate nearest neighbor search, and a variety of hashing schemes have been proposed for different dissim…
Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version
Bin Yang, Manohar Kaul, Christian S. Jensen
We are witnessing increasing interests in the effective use of road networks. For example, to enable effective vehicle routing, weighted-graph models of transportation networks are…