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20152022
most citedQuantum Graph Neural Networks

60 citations · 124 across the 19 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.LG20212 cited

An Online Riemannian PCA for Stochastic Canonical Correlation Analysis

Zihang Meng, Rudrasis Chakraborty, Vikas Singh

We present an efficient stochastic algorithm (RSG+) for canonical correlation analysis (CCA) using a reparametrization of the projection matrices. We show how this reparametrizatio…

cs.CV2021

Connecting What to Say With Where to Look by Modeling Human Attention Traces

Zihang Meng, Licheng Yu, Ning Zhang +4

We introduce a unified framework to jointly model images, text, and human attention traces. Our work is built on top of the recent Localized Narratives annotation framework [30], w…

cs.LG2021

Simpler Certified Radius Maximization by Propagating Covariances

Xingjian Zhen, Rudrasis Chakraborty, Vikas Singh

One strategy for adversarially training a robust model is to maximize its certified radius -- the neighborhood around a given training sample for which the model's prediction remai…

cs.CL202129 cited

Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention

Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty +4

Transformers have emerged as a powerful tool for a broad range of natural language processing tasks. A key component that drives the impressive performance of Transformers is the s…

cs.LG2021

Learning Invariant Representations using Inverse Contrastive Loss

Aditya Kumar Akash, Vishnu Suresh Lokhande, Sathya N. Ravi +1

Learning invariant representations is a critical first step in a number of machine learning tasks. A common approach corresponds to the so-called information bottleneck principle i…

cs.LG20211 cited

Graph Neural Networks to Predict Customer Satisfaction Following Interactions with a Corporate Call Center

Teja Kanchinadam, Zihang Meng, Joseph Bockhorst +2

Customer satisfaction is an important factor in creating and maintaining long-term relationships with customers. Near real-time identification of potentially dissatisfied customers…