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20162022
most citedCollaborative Deep Learning in Fixed Topology Networks

77 citations · 135 across the 26 of their papers we have counts for

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9 papers · 1 filter

stat.ML2018

Root-cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systems

Chao Liu, Kin Gwn Lore, Zhanhong Jiang +1

Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, di…

stat.ML2018

On Consensus-Optimality Trade-offs in Collaborative Deep Learning

Zhanhong Jiang, Aditya Balu, Chinmay Hegde +1

In distributed machine learning, where agents collaboratively learn from diverse private data sets, there is a fundamental tension between consensus and optimality. In this paper,…

stat.ML2018

Predicting County Level Corn Yields Using Deep Long Short Term Memory Models

Zehui Jiang, Chao Liu, Nathan P. Hendricks +3

Corn yield prediction is beneficial as it provides valuable information about production and prices prior the harvest. Publicly available high-quality corn yield prediction can hel…

stat.ML20176 cited

A Forward-Backward Approach for Visualizing Information Flow in Deep Networks

Aditya Balu, Thanh V. Nguyen, Apurva Kokate +2

We introduce a new, systematic framework for visualizing information flow in deep networks. Specifically, given any trained deep convolutional network model and a given test image,…

stat.ML20171 cited

Interpretable Deep Learning applied to Plant Stress Phenotyping

Sambuddha Ghosal, David Blystone, Asheesh K. Singh +3

Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and…

stat.ML201777 cited

Collaborative Deep Learning in Fixed Topology Networks

Zhanhong Jiang, Aditya Balu, Chinmay Hegde +1

There is significant recent interest to parallelize deep learning algorithms in order to handle the enormous growth in data and model sizes. While most advances focus on model para…