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20192022
most citedNyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention

29 citations · 50 across the 13 of their papers we have counts for

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7 papers · 1 filter

cs.LG2022★ 1 cited

Multi Resolution Analysis (MRA) for Approximate Self-Attention

Zhanpeng Zeng, Sourav Pal, Jeffery Kline +2

Transformers have emerged as a preferred model for many tasks in natural langugage processing and vision. Recent efforts on training and deploying Transformers more efficiently hav…

cs.LG2021★ 3 cited

You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling

Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi +3

Transformer-based models are widely used in natural language processing (NLP). Central to the transformer model is the self-attention mechanism, which captures the interactions of…

cs.LG2021

Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection

Eric Bunch, Jeffery Kline, Daniel Dickinson +2

Metric space magnitude, an active field of research in algebraic topology, is a scalar quantity that summarizes the effective number of distinct points that live in a general metri…

cs.LG2021★ 1 cited

A Simple yet Brisk and Efficient Active Learning Platform for Text Classification

Teja Kanchinadam, Qian You, Keith Westpfahl +4

In this work, we propose the use of a fully managed machine learning service, which utilizes active learning to directly build models from unstructured data. With this tool, busine…

cs.LG2021★ 1 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…

cs.LG2020★ 2 cited

SGD Distributional Dynamics of Three Layer Neural Networks

Victor Luo, Yazhen Wang, Glenn Fung

With the rise of big data analytics, multi-layer neural networks have surfaced as one of the most powerful machine learning methods. However, their theoretical mathematical propert…

cs.LG2020

Practical applications of metric space magnitude and weighting vectors

Eric Bunch, Daniel Dickinson, Jeffery Kline +1

Metric space magnitude, an active subject of research in algebraic topology, originally arose in the context of biology, where it was used to represent the effective number of dist…