activity
20192021
most citedImproving Local Identifiability in Probabilistic Box Embeddings

22 citations · 25 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2021

Box Embeddings: An open-source library for representation learning using geometric structures

Tejas Chheda, Purujit Goyal, Trang Tran +4

A major factor contributing to the success of modern representation learning is the ease of performing various vector operations. Recently, objects with geometric structures (eg. d…

cs.AI20212 cited

Probabilistic Box Embeddings for Uncertain Knowledge Graph Reasoning

Xuelu Chen, Michael Boratko, Muhao Chen +3

Knowledge bases often consist of facts which are harvested from a variety of sources, many of which are noisy and some of which conflict, resulting in a level of uncertainty for ea…

cs.LG202022 cited

Improving Local Identifiability in Probabilistic Box Embeddings

Shib Sankar Dasgupta, Michael Boratko, Dongxu Zhang +3

Geometric embeddings have recently received attention for their natural ability to represent transitive asymmetric relations via containment. Box embeddings, where objects are repr…

cs.CL20191 cited

Dating Documents using Graph Convolution Networks

Shikhar Vashishth, Shib Sankar Dasgupta, Swayambhu Nath Ray +1

Document date is essential for many important tasks, such as document retrieval, summarization, event detection, etc. While existing approaches for these tasks assume accurate know…

cs.CL2019

AD3: Attentive Deep Document Dater

Swayambhu Nath Ray, Shib Sankar Dasgupta, Partha Talukdar

Knowledge of the creation date of documents facilitates several tasks such as summarization, event extraction, temporally focused information extraction etc. Unfortunately, for mos…