no code implementations • 2 Aug 2013 • Bin Yang, Manohar Kaul, Christian S. Jensen
This paper formulates and addresses the problem of annotating all edges in a road network with travel cost based weights from a set of trips in the network that cover only a small fraction of the edges, each with an associated ground-truth travel cost.
no code implementations • 11 Sep 2016 • Wikor Pronobis, Danny Panknin, Johannes Kirschnick, Vignesh Srinivasan, Wojciech Samek, Volker Markl, Manohar Kaul, Klaus-Robert Mueller, Shinichi Nakajima
In this paper, we propose {multiple purpose LSH (mp-LSH) which shares the hash codes for different dissimilarities.
2 code implementations • ACL 2019 • Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul
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 research on knowledge base completion (also known as relation prediction).
Ranked #1 on Knowledge Graph Completion on FB15k-237
1 code implementation • ICML 2018 • 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 set of invariants that better detect higher-order structure.
1 code implementation • ICLR 2020 • 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.
1 code implementation • 19 May 2020 • 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 local graph properties, scalability, and interpretibility.
1 code implementation • ECCV 2020 • 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, has sparked an interest in the search for improved higher-order matching algorithms on warped images due to projection.
1 code implementation • 18 Sep 2020 • Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Isaiah Onando Mulang', Saeedeh Shekarpour, Johannes Hoffart, Manohar Kaul
In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG).
no code implementations • 27 Sep 2020 • Manohar Kaul, Dai Tamaki
Our model of such networks is a directed graph $Q$ equipped with a weight function $w$ on the set $Q_{1}$ of arrows in $Q$.
1 code implementation • NeurIPS 2020 • Charu Sharma, Manohar Kaul
We present a comprehensive empirical evaluation of our method on both downstream classification and segmentation tasks and show that supervised methods pre-trained with our self-supervised learning method significantly improve the accuracy of state-of-the-art methods.
no code implementations • 1 Jan 2021 • Manohar Kaul, Masaaki Imaizumi
In this paper, we overcome these obstacles by capturing higher-order interactions succinctly as simplices, model their neighborhood by face-vectors, and develop a nonparametric kernel estimator for simplices that views the evolving graph from the perspective of a time process (i. e., a sequence of graph snapshots).
no code implementations • 6 Feb 2021 • Manohar Kaul, Masaaki Imaizumi
In this paper, we overcome these obstacles by capturing higher-order interactions succinctly as \textit{simplices}, model their neighborhood by face-vectors, and develop a nonparametric kernel estimator for simplices that views the evolving graph from the perspective of a time process (i. e., a sequence of graph snapshots).
no code implementations • 13 Jun 2021 • 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 blackbox adversarial attacks, where the attacker is only allowed to query the target model outputs.
no code implementations • 3 Sep 2021 • Anson Bastos, Manohar Kaul
Language models have emerged as the prevalent choice of several natural language tasks due to the performance boost offered by these models.
1 code implementation • 2 May 2022 • 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 can be a challenging task.
no code implementations • 27 Aug 2023 • Siddharth Katageri, Arkadipta De, Chaitanya Devaguptapu, VSSV Prasad, Charu Sharma, Manohar Kaul
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 scene understanding, to name a few.