Iowa State University of Science and Technology

∙ Ames , United States ∙ 0 followers

Iowa State University of Science and Technology is a flagship public land-grant and space-grant research university in Ames, Iowa.

How useful is Active Learning for Image-based Plant Phenotyping?

Deep learning models have been successfully deployed for a diverse array...
06/07/2020 ∙ by Koushik Nagasubramanian, et al. ∙ 57 ∙ share

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Convolutional Neural Networks for Image-based Corn Kernel Detection and Counting

Precise in-season corn grain yield estimates enable farmers to make real...
03/26/2020 ∙ by Saeed Khaki, et al. ∙ 32 ∙ share

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

Performance monitoring, anomaly detection, and root-cause analysis in co...
05/31/2018 ∙ by Chao Liu, et al. ∙ 14 ∙ share

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Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers

Deep neural networks have been shown to exhibit an intriguing vulnerabil...
04/17/2019 ∙ by Ameya Joshi, et al. ∙ 14 ∙ share

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A perspective on multi-agent communication for information fusion

Collaborative decision making in multi-agent systems typically requires ...
11/09/2019 ∙ by Homagni Saha, et al. ∙ 13 ∙ share

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Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning

Microfluidic devices are utilized to control and direct flow behavior in...
11/29/2018 ∙ by Xian Yeow Lee, et al. ∙ 10 ∙ share

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Influence Function based Data Poisoning Attacks to Top-N Recommender Systems

Recommender system is an essential component of web services to engage u...
02/19/2020 ∙ by Minghong Fang, et al. ∙ 10 ∙ share

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Online Robust Policy Learning in the Presence of Unknown Adversaries

The growing prospect of deep reinforcement learning (DRL) being used in ...
07/16/2018 ∙ by Aaron J. Havens, et al. ∙ 8 ∙ share

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Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents

Robustness of Deep Reinforcement Learning (DRL) algorithms towards adver...
09/05/2019 ∙ by Xian Yeow Lee, et al. ∙ 8 ∙ share

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Driver Behavior Analysis Using Lane Departure Detection Under Challenging Conditions

In this paper, we present a novel model to detect lane regions and extra...
05/31/2019 ∙ by Luis Riera, et al. ∙ 6 ∙ share

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Partially-Typed NER Datasets Integration: Connecting Practice to Theory

While typical named entity recognition (NER) models require the training...
05/01/2020 ∙ by Shi Zhi, et al. ∙ 5 ∙ share

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Topology-Aware Graph Pooling Networks

Pooling operations have shown to be effective on computer vision and nat...
10/19/2020 ∙ by Hongyang Gao, et al. ∙ 5 ∙ share

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Interpretable deep learning for guided structure-property explorations in photovoltaics

The performance of an organic photovoltaic device is intricately connect...
11/14/2018 ∙ by Balaji Sesha Sarath Pokuri, et al. ∙ 4 ∙ share

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Multi-Resolution 3D Convolutional Neural Networks for Object Recognition

Learning from 3D Data is a fascinating idea which is well explored and s...
05/30/2018 ∙ by Sambit Ghadai, et al. ∙ 2 ∙ share

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A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact

Quantifying and predicting the long-term impact of scientific writings o...
03/26/2020 ∙ by Fan Zhou, et al. ∙ 1 ∙ share

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A Survey of Information Cascade Analysis: Models, Predictions and Recent Advances

The deluge of digital information in our daily life – from user-generate...
05/22/2020 ∙ by Fan Zhou, et al. ∙ 1 ∙ share

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A Mathematical Programming Approach for Integrated Multiple Linear Regression Subset Selection and Validation

Subset selection for multiple linear regression aims to construct a regr...
12/12/2017 ∙ by Seokhyun Chung, et al. ∙ 0 ∙ share

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Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes

3D Convolutional Neural Networks (3D-CNN) have been used for object reco...
11/13/2017 ∙ by Sambit Ghadai, et al. ∙ 0 ∙ share

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Learning Graphical Models from a Distributed Stream

A current challenge for data management systems is to support the constr...
10/05/2017 ∙ by Yu Zhang, et al. ∙ 0 ∙ share

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Video Denoising and Enhancement via Dynamic Video Layering

Video denoising refers to the problem of removing "noise" from a video s...
10/05/2017 ∙ by Han Guo, et al. ∙ 0 ∙ share

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Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification

Deep neural networks (DNNs) have transformed several artificial intellig...
09/17/2017 ∙ by Xiaoyu Cao, et al. ∙ 0 ∙ share

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Early Detection of Combustion Instabilities using Deep Convolutional Selective Autoencoders on Hi-speed Flame Video

This paper proposes an end-to-end convolutional selective autoencoder ap...
03/25/2016 ∙ by Adedotun Akintayo, et al. ∙ 0 ∙ share

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Occlusion Edge Detection in RGB-D Frames using Deep Convolutional Networks

Occlusion edges in images which correspond to range discontinuity in the...
12/22/2014 ∙ by Soumik Sarkar, et al. ∙ 0 ∙ share

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A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality

Symmetric nonnegative matrix factorization (SymNMF) has important applic...
03/24/2017 ∙ by Songtao Lu, et al. ∙ 0 ∙ share

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Optimization for L1-Norm Error Fitting via Data Aggregation

We propose a data aggregation-based algorithm with monotonic convergence...
03/15/2017 ∙ by Young Woong Park, et al. ∙ 0 ∙ share

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Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data

This paper proposes a hierarchical feature extractor for non-stationary ...
02/06/2017 ∙ by Adedotun Akintayo, et al. ∙ 0 ∙ share

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Energy Prediction using Spatiotemporal Pattern Networks

This paper presents a novel data-driven technique based on the spatiotem...
02/03/2017 ∙ by Zhanhong Jiang, et al. ∙ 0 ∙ share

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Bayesian Network Learning via Topological Order

We propose a mixed integer programming (MIP) model and iterative algorit...
01/20/2017 ∙ by Young Woong Park, et al. ∙ 0 ∙ share

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A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert knowledge

Crop yield forecasting is the methodology of predicting crop yields prio...
08/17/2016 ∙ by Vikas Chawla, et al. ∙ 0 ∙ share

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Privacy-Preserving Adversarial Networks

We propose a data-driven framework for optimizing privacy-preserving dat...
12/19/2017 ∙ by Ardhendu Tripathy, et al. ∙ 0 ∙ share

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Merging K-means with hierarchical clustering for identifying general-shaped groups

Clustering partitions a dataset such that observations placed together i...
12/23/2017 ∙ by Anna D. Peterson, et al. ∙ 0 ∙ share

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Scalable and Flexible Multiview MAX-VAR Canonical Correlation Analysis

Generalized canonical correlation analysis (GCCA) aims at finding latent...
05/31/2016 ∙ by Xiao Fu, et al. ∙ 0 ∙ share

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NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization

We study a stochastic and distributed algorithm for nonconvex problems w...
05/25/2016 ∙ by Davood Hajinezhad, et al. ∙ 0 ∙ share

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A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks

Exact Bayesian structure discovery in Bayesian networks requires exponen...
08/07/2014 ∙ by Yetian Chen, et al. ∙ 0 ∙ share

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An end-to-end convolutional selective autoencoder approach to Soybean Cyst Nematode eggs detection

This paper proposes a novel selective autoencoder approach within the fr...
03/25/2016 ∙ by Adedotun Akintayo, et al. ∙ 0 ∙ share

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Synthesis of Gaussian Trees with Correlation Sign Ambiguity: An Information Theoretic Approach

In latent Gaussian trees the pairwise correlation signs between the vari...
01/24/2016 ∙ by Ali Moharrer, et al. ∙ 0 ∙ share

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Projected Nesterov's Proximal-Gradient Algorithm for Sparse Signal Reconstruction with a Convex Constraint

We develop a projected Nesterov's proximal-gradient (PNPG) approach for ...
02/09/2015 ∙ by Renliang Gu, et al. ∙ 0 ∙ share

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Structure Learning in Bayesian Networks of Moderate Size by Efficient Sampling

We study the Bayesian model averaging approach to learning Bayesian netw...
01/19/2015 ∙ by Ru He, et al. ∙ 0 ∙ share

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Alternating direction method of multipliers for penalized zero-variance discriminant analysis

We consider the task of classification in the high dimensional setting w...
01/21/2014 ∙ by Brendan P. W. Ames, et al. ∙ 0 ∙ share

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A Max-Product EM Algorithm for Reconstructing Markov-tree Sparse Signals from Compressive Samples

We propose a Bayesian expectation-maximization (EM) algorithm for recons...
09/05/2012 ∙ by Zhao Song, et al. ∙ 0 ∙ share

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Spatial Multiresolution Cluster Detection Method

A novel multi-resolution cluster detection (MCD) method is proposed to i...
05/09/2012 ∙ by Lingsong Zhang, et al. ∙ 0 ∙ share

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Mask Iterative Hard Thresholding Algorithms for Sparse Image Reconstruction of Objects with Known Contour

We develop mask iterative hard thresholding algorithms (mask IHT and mas...
12/02/2011 ∙ by Aleksandar Dogandžić, et al. ∙ 0 ∙ share

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Exact Reconstruction Conditions for Regularized Modified Basis Pursuit

In this correspondence, we obtain exact recovery conditions for regulari...
08/16/2011 ∙ by Wei Lu, et al. ∙ 0 ∙ share

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PaFiMoCS: Particle Filtered Modified-CS and Applications in Visual Tracking across Illumination Change

We study the problem of tracking (causally estimating) a time sequence o...
01/08/2013 ∙ by R. Sarkar, et al. ∙ 0 ∙ share

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Real-time Robust Principal Components' Pursuit

In the recent work of Candes et al, the problem of recovering low rank m...
10/04/2010 ∙ by Chenlu Qiu, et al. ∙ 0 ∙ share

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Automated assessment of non-native learner essays: Investigating the role of linguistic features

Automatic essay scoring (AES) refers to the process of scoring free text...
12/02/2016 ∙ by Sowmya Vajjala, et al. ∙ 0 ∙ share

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Readability-based Sentence Ranking for Evaluating Text Simplification

We propose a new method for evaluating the readability of simplified sen...
03/18/2016 ∙ by Sowmya Vajjala, et al. ∙ 0 ∙ share

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Device-to-Device Networking Meets Cellular via Network Coding

Utilizing device-to-device (D2D) connections among mobile devices is pro...
12/08/2017 ∙ by Yasaman Keshtkarjahromi, et al. ∙ 0 ∙ share

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Notes on complexity of packing coloring

A packing k-coloring for some integer k of a graph G=(V,E) is a mapping ...
12/22/2017 ∙ by Minki Kim, et al. ∙ 0 ∙ share

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Projection Theorems Using Effective Dimension

In this paper we use the theory of computing to study fractal dimensions...
11/06/2017 ∙ by Neil Lutz, et al. ∙ 0 ∙ share

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