2023
61.
Parayil, N.; You, A.; Grimm, C.; Davidson, J.r.
Follow the leader: a path generator and controller for precision tree scanning with a robotic manipulator Proceedings Article
In: Precision agriculture, pp. 167–174, Wageningen Academic Publishers, 2023, ISBN: 978-90-8686-393-8, (Section: 19).
Links | BibTeX | Tags: Pruning, Thinning
@inproceedings{parayil_19_2023,
title = {Follow the leader: a path generator and controller for precision tree scanning with a robotic manipulator},
author = {Parayil, N. and You, A. and Grimm, C. and Davidson, J.r.},
url = {https://www.wageningenacademic.com/doi/10.3920/978-90-8686-947-3_19},
doi = {10.3920/978-90-8686-947-3_19},
isbn = {978-90-8686-393-8},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-01},
booktitle = {Precision agriculture},
pages = {167\textendash174},
publisher = {Wageningen Academic Publishers},
note = {Section: 19},
keywords = {Pruning, Thinning},
pubstate = {published},
tppubtype = {inproceedings}
}
62.
Wang, T.; Sankari, P.; Brown, J.; Paudel, A.; He, L.; Karkee, M.; Thompson, A.; Grimm, C.; Davidson, J.r.; Todorovic, S.
Automatic estimation of trunk cross sectional area using deep learning Proceedings Article
In: Precision agriculture, pp. 491–498, Wageningen Academic Publishers, 2023, ISBN: 978-90-8686-393-8, (Section: 62).
Links | BibTeX | Tags: AI, Labor, Pruning
@inproceedings{wang_62_2023,
title = {Automatic estimation of trunk cross sectional area using deep learning},
author = {Wang, T. and Sankari, P. and Brown, J. and Paudel, A. and He, L. and Karkee, M. and Thompson, A. and Grimm, C. and Davidson, J.r. and Todorovic, S.},
url = {https://www.wageningenacademic.com/doi/10.3920/978-90-8686-947-3_62},
doi = {10.3920/978-90-8686-947-3_62},
isbn = {978-90-8686-393-8},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-01},
booktitle = {Precision agriculture},
pages = {491\textendash498},
publisher = {Wageningen Academic Publishers},
note = {Section: 62},
keywords = {AI, Labor, Pruning},
pubstate = {published},
tppubtype = {inproceedings}
}
63.
Mishra, Ritwick; Heavey, Jack; Kaur, Gursharn; Adiga, Abhijin; Vullikanti, Anil
Reconstructing an Epidemic Outbreak Using Steiner Connectivity Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 10, pp. 11613–11620, 2023, ISSN: 2374-3468, (Number: 10).
Abstract | Links | BibTeX | Tags: AI
@article{mishra_reconstructing_2023,
title = {Reconstructing an Epidemic Outbreak Using Steiner Connectivity},
author = {Mishra, Ritwick and Heavey, Jack and Kaur, Gursharn and Adiga, Abhijin and Vullikanti, Anil},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/26372},
doi = {10.1609/aaai.v37i10.26372},
issn = {2374-3468},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {37},
number = {10},
pages = {11613\textendash11620},
abstract = {Only a subset of infections is actually observed in an outbreak, due to multiple reasons such as asymptomatic cases and under-reporting. Therefore, reconstructing an epidemic cascade given some observed cases is an important step in responding to such an outbreak. A maximum likelihood solution to this problem ( referred to as CascadeMLE ) can be shown to be a variation of the classical Steiner subgraph problem, which connects a subset of observed infections. In contrast to prior works on epidemic reconstruction, which consider the standard Steiner tree objective, we show that a solution to CascadeMLE, based on the actual MLE objective, has a very different structure. We design a logarithmic approximation algorithm for CascadeMLE, and evaluate it on multiple synthetic and social contact networks, including a contact network constructed for a hospital. Our algorithm has significantly better performance compared to a prior baseline.},
note = {Number: 10},
keywords = {AI},
pubstate = {published},
tppubtype = {article}
}
Only a subset of infections is actually observed in an outbreak, due to multiple reasons such as asymptomatic cases and under-reporting. Therefore, reconstructing an epidemic cascade given some observed cases is an important step in responding to such an outbreak. A maximum likelihood solution to this problem ( referred to as CascadeMLE ) can be shown to be a variation of the classical Steiner subgraph problem, which connects a subset of observed infections. In contrast to prior works on epidemic reconstruction, which consider the standard Steiner tree objective, we show that a solution to CascadeMLE, based on the actual MLE objective, has a very different structure. We design a logarithmic approximation algorithm for CascadeMLE, and evaluate it on multiple synthetic and social contact networks, including a contact network constructed for a hospital. Our algorithm has significantly better performance compared to a prior baseline.
64.
Belkhouja, Taha; Yan, Yan; Doppa, Janardhan Rao
Dynamic Time Warping Based Adversarial Framework for Time-Series Domain Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 6, pp. 7353–7366, 2023, ISSN: 1939-3539, (Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence).
Abstract | Links | BibTeX | Tags: AI
@article{belkhouja_dynamic_2023,
title = {Dynamic Time Warping Based Adversarial Framework for Time-Series Domain},
author = {Belkhouja, Taha and Yan, Yan and Doppa, Janardhan Rao},
url = {https://ieeexplore.ieee.org/document/9970291},
doi = {10.1109/TPAMI.2022.3224754},
issn = {1939-3539},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {45},
number = {6},
pages = {7353\textendash7366},
abstract = {Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data arises in diverse applications including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. In this paper, we propose a novel framework for the time-series domain referred as Dynamic Time Warping for Adversarial Robustness (DTW-AR) using the dynamic time warping measure. Theoretical and empirical evidence is provided to demonstrate the effectiveness of DTW over the standard euclidean distance metric employed in prior methods for the image domain. We develop a principled algorithm justified by theoretical analysis to efficiently create diverse adversarial examples using random alignment paths. Experiments on diverse real-world benchmarks show the effectiveness of DTW-AR to fool DNNs for time-series data and to improve their robustness using adversarial training.},
note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {AI},
pubstate = {published},
tppubtype = {article}
}
Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data arises in diverse applications including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. In this paper, we propose a novel framework for the time-series domain referred as Dynamic Time Warping for Adversarial Robustness (DTW-AR) using the dynamic time warping measure. Theoretical and empirical evidence is provided to demonstrate the effectiveness of DTW over the standard euclidean distance metric employed in prior methods for the image domain. We develop a principled algorithm justified by theoretical analysis to efficiently create diverse adversarial examples using random alignment paths. Experiments on diverse real-world benchmarks show the effectiveness of DTW-AR to fool DNNs for time-series data and to improve their robustness using adversarial training.
65.
Ghosh, Subhankar; Belkhouja, Taha; Yan, Yan; Doppa, Janardhan Rao
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 6, pp. 7722–7730, 2023, ISSN: 2374-3468, (Number: 6).
Abstract | Links | BibTeX | Tags: AI, Water
@article{ghosh_improving_2023,
title = {Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis},
author = {Ghosh, Subhankar and Belkhouja, Taha and Yan, Yan and Doppa, Janardhan Rao},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/25936},
doi = {10.1609/aaai.v37i6.25936},
issn = {2374-3468},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {37},
number = {6},
pages = {7722\textendash7730},
abstract = {Safe deployment of deep neural networks in high-stake real-world applications require theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled framework for uncertainty quantification of deep models in the form of prediction set for classification tasks with a user-specified coverage (i.e., true class label is contained with high probability). This paper proposes a novel algorithm referred to as Neighborhood Conformal Prediction (NCP) to improve the efficiency of uncertainty quantification from CP for deep classifiers (i.e., reduce prediction set size). The key idea behind NCP is to use the learned representation of the neural network to identify k nearest-neighbor calibration examples for a given testing input and assign them importance weights proportional to their distance to create adaptive prediction sets. We theoretically show that if the learned data representation of the neural network satisfies some mild conditions, NCP will produce smaller prediction sets than traditional CP algorithms. Our comprehensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using diverse deep neural networks strongly demonstrate that NCP leads to significant reduction in prediction set size over prior CP methods.},
note = {Number: 6},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {article}
}
Safe deployment of deep neural networks in high-stake real-world applications require theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled framework for uncertainty quantification of deep models in the form of prediction set for classification tasks with a user-specified coverage (i.e., true class label is contained with high probability). This paper proposes a novel algorithm referred to as Neighborhood Conformal Prediction (NCP) to improve the efficiency of uncertainty quantification from CP for deep classifiers (i.e., reduce prediction set size). The key idea behind NCP is to use the learned representation of the neural network to identify k nearest-neighbor calibration examples for a given testing input and assign them importance weights proportional to their distance to create adaptive prediction sets. We theoretically show that if the learned data representation of the neural network satisfies some mild conditions, NCP will produce smaller prediction sets than traditional CP algorithms. Our comprehensive experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using diverse deep neural networks strongly demonstrate that NCP leads to significant reduction in prediction set size over prior CP methods.
66.
Ghosh, Subhankar; Shi, Yuanjie; Belkhouja, Taha; Yan, Yan; Doppa, Jana; Jones, Brian
Probabilistically Robust Conformal Prediction Proceedings Article
In: 2023.
Abstract | Links | BibTeX | Tags: AI
@inproceedings{ghosh_probabilistically_2023,
title = {Probabilistically Robust Conformal Prediction},
author = {Ghosh, Subhankar and Shi, Yuanjie and Belkhouja, Taha and Yan, Yan and Doppa, Jana and Jones, Brian},
url = {https://openreview.net/forum?id=4xI4oaqIs2},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
abstract = {Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, CP produces a prediction set of candidate labels with a user-specified coverage (i.e., true class label is contained with high probability). Almost all the existing work on CP assumes clean testing data and there is not much known about the robustness of CP algorithms w.r.t natural/adversarial perturbations to testing examples. This paper studies the problem of probabilistically robust conformal prediction (PRCP) which ensures robustness to most perturbations around clean input examples. PRCP generalizes the standard CP (cannot handle perturbations) and adversarially robust CP (ensures robustness w.r.t worst-case perturbations) to achieve better trade-offs between nominal performance and robustness. We propose a novel adaptive PRCP (aPRCP) algorithm to achieve probabilistically robust coverage. The key idea behind aPRCP is to determine two parallel thresholds, one for data samples and another one for the perturbations on data (aka "quantile-of-quantile'' design). We provide theoretical analysis to show that aPRCP algorithm achieves robust coverage. Our experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using deep neural networks demonstrate that aPRCP achieves better trade-offs than state-of-the-art CP and adversarially robust CP algorithms.},
keywords = {AI},
pubstate = {published},
tppubtype = {inproceedings}
}
Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, CP produces a prediction set of candidate labels with a user-specified coverage (i.e., true class label is contained with high probability). Almost all the existing work on CP assumes clean testing data and there is not much known about the robustness of CP algorithms w.r.t natural/adversarial perturbations to testing examples. This paper studies the problem of probabilistically robust conformal prediction (PRCP) which ensures robustness to most perturbations around clean input examples. PRCP generalizes the standard CP (cannot handle perturbations) and adversarially robust CP (ensures robustness w.r.t worst-case perturbations) to achieve better trade-offs between nominal performance and robustness. We propose a novel adaptive PRCP (aPRCP) algorithm to achieve probabilistically robust coverage. The key idea behind aPRCP is to determine two parallel thresholds, one for data samples and another one for the perturbations on data (aka "quantile-of-quantile'' design). We provide theoretical analysis to show that aPRCP algorithm achieves robust coverage. Our experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using deep neural networks demonstrate that aPRCP achieves better trade-offs than state-of-the-art CP and adversarially robust CP algorithms.
67.
He, Liqiang; Wei Wang, Albert Chen; Min Sun; Cheng-hao Kuo; Sinisa Todorovic
Bidirectional alignment for domain adaptive detection with transformers Proceedings Article
In: Proceedings of International Conference on Computer Vision, 2023.
Abstract | Links | BibTeX | Tags: Pruning
@inproceedings{noauthor_bidirectional_nodate,
title = {Bidirectional alignment for domain adaptive detection with transformers},
author = {He, Liqiang; Wei Wang, Albert Chen; Min Sun; Cheng-hao Kuo; Sinisa Todorovic},
url = {https://www.amazon.science/publications/bidirectional-alignment-for-domain-adaptive-detection-with-transformers},
year = {2023},
date = {2023-03-08},
urldate = {2023-03-08},
journal = {Amazon Science},
publisher = {Proceedings of International Conference on Computer Vision},
abstract = {We propose a Bidirectional Alignment for domain adaptive Detection with Transformers (BiADT) to improve cross domain object detection performance. Existing adversarial learning based methods use gradient reverse layer (GRL) to reduce the domain gap between the source and target domains in feature…},
keywords = {Pruning},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a Bidirectional Alignment for domain adaptive Detection with Transformers (BiADT) to improve cross domain object detection performance. Existing adversarial learning based methods use gradient reverse layer (GRL) to reduce the domain gap between the source and target domains in feature…
68.
Bertucci, Donald; Hamid, Md Montaser; Anand, Yashwanthi; Ruangrotsakun, Anita; Tabatabai, Delyar; Perez, Melissa; Kahng, Minsuk
DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 320–330, 2023, ISSN: 1941-0506, (Conference Name: IEEE Transactions on Visualization and Computer Graphics).
Abstract | Links | BibTeX | Tags: AI, Humans
@article{bertucci_dendromap_2023,
title = {DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps},
author = {Bertucci, Donald and Hamid, Md Montaser and Anand, Yashwanthi and Ruangrotsakun, Anita and Tabatabai, Delyar and Perez, Melissa and Kahng, Minsuk},
url = {https://ieeexplore.ieee.org/document/9904448},
doi = {10.1109/TVCG.2022.3209425},
issn = {1941-0506},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
volume = {29},
number = {1},
pages = {320\textendash330},
abstract = {In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning (ML). ML practitioners often explore image datasets by generating a grid of images or projecting high-dimensional representations of images into 2-D using dimensionality reduction techniques (e.g., t-SNE). However, neither approach effectively scales to large datasets because images are ineffectively organized and interactions are insufficiently supported. To address these challenges, we develop DendroMap by adapting Treemaps, a well-known visualization technique. DendroMap effectively organizes images by extracting hierarchical cluster structures from high-dimensional representations of images. It enables users to make sense of the overall distributions of datasets and interactively zoom into specific areas of interests at multiple levels of abstraction. Our case studies with widely-used image datasets for deep learning demonstrate that users can discover insights about datasets and trained models by examining the diversity of images, identifying underperforming subgroups, and analyzing classification errors. We conducted a user study that evaluates the effectiveness of DendroMap in grouping and searching tasks by comparing it with a gridified version of t-SNE and found that participants preferred DendroMap. DendroMap is available at https://div-lab.github.io/dendromap/.},
note = {Conference Name: IEEE Transactions on Visualization and Computer Graphics},
keywords = {AI, Humans},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning (ML). ML practitioners often explore image datasets by generating a grid of images or projecting high-dimensional representations of images into 2-D using dimensionality reduction techniques (e.g., t-SNE). However, neither approach effectively scales to large datasets because images are ineffectively organized and interactions are insufficiently supported. To address these challenges, we develop DendroMap by adapting Treemaps, a well-known visualization technique. DendroMap effectively organizes images by extracting hierarchical cluster structures from high-dimensional representations of images. It enables users to make sense of the overall distributions of datasets and interactively zoom into specific areas of interests at multiple levels of abstraction. Our case studies with widely-used image datasets for deep learning demonstrate that users can discover insights about datasets and trained models by examining the diversity of images, identifying underperforming subgroups, and analyzing classification errors. We conducted a user study that evaluates the effectiveness of DendroMap in grouping and searching tasks by comparing it with a gridified version of t-SNE and found that participants preferred DendroMap. DendroMap is available at https://div-lab.github.io/dendromap/.
69.
Tanvir Ferdousi; Mingliang Liu; Kirti Rajagopalan; Jennifer Adam; Abhijin Adiga; Mandy Wilson; SS Ravi; Anil Vullikanti; Madhav V Marathe; Samarth Swarup
A Machine Learning Framework to Explain Complex Geospatial Simulations: A Climate Change Case Study Proceedings Article
In: 2023.
Abstract | Links | BibTeX | Tags: AI, Water
@inproceedings{ferdousi_machine_2023,
title = {A Machine Learning Framework to Explain Complex Geospatial Simulations: A Climate Change Case Study},
author = {Tanvir Ferdousi and Mingliang Liu and Kirti Rajagopalan and Jennifer Adam and Abhijin Adiga and Mandy Wilson and SS Ravi and Anil Vullikanti and Madhav V Marathe and Samarth Swarup},
url = {https://tanvir-ferdousi.github.io/assets/pdf/explainability_wsc23.pdf},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
abstract = {The explainability of large and complex simulation models is an open problem. We present a framework to analyze such models by processing multidimensional data through a pipeline of target variable computation, clustering, supervised classification, and feature importance analysis. As a use case, the well-known large-scale hydrology and crop systems simulator VIC-CropSyst is utilized to evaluate how climate change may affect water availability in Washington, United States. We study how snowmelt varies with climate variables (temperature, precipitation) to identify different response characteristics. Based on these characteristics, spatial units are clustered into six distinct classes. A random forest classifier is used with Shapley values to rank static soil and land parameters that help detect each class. The results also include an analysis of risk across different classes to identify areas vulnerable to climate change. This paper demonstrates the usefulness of the proposed framework in providing explainability for large and complex simulations.},
keywords = {AI, Water},
pubstate = {published},
tppubtype = {inproceedings}
}
The explainability of large and complex simulation models is an open problem. We present a framework to analyze such models by processing multidimensional data through a pipeline of target variable computation, clustering, supervised classification, and feature importance analysis. As a use case, the well-known large-scale hydrology and crop systems simulator VIC-CropSyst is utilized to evaluate how climate change may affect water availability in Washington, United States. We study how snowmelt varies with climate variables (temperature, precipitation) to identify different response characteristics. Based on these characteristics, spatial units are clustered into six distinct classes. A random forest classifier is used with Shapley values to rank static soil and land parameters that help detect each class. The results also include an analysis of risk across different classes to identify areas vulnerable to climate change. This paper demonstrates the usefulness of the proposed framework in providing explainability for large and complex simulations.
70.
Aseem Saxena; Paola Pesantez-Cabrera; Rohan Ballapragada; Markus Keller; Alan Fern
Multi-Task Learning for Budbreak Prediction Workshop
2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS), arXiv, 2023, (arXiv:2301.01815 [cs]).
Abstract | Links | BibTeX | Tags: Cold Hardiness, Computer Science
@workshop{saxena_multi-task_2023,
title = {Multi-Task Learning for Budbreak Prediction},
author = {Aseem Saxena and Paola Pesantez-Cabrera and Rohan Ballapragada and Markus Keller and Alan Fern},
url = {https://openreview.net/pdf?id=kvGm8DJ-cM},
doi = {10.48550/arXiv.2301.01815},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS)},
publisher = {arXiv},
abstract = {Grapevine budbreak is a key phenological stage of seasonal development, which serves as a signal for the onset of active growth. This is also when grape plants are most vulnerable to damage from freezing temperatures. Hence, it is important for winegrowers to anticipate the day of budbreak occurrence to protect their vineyards from late spring frost events. This work investigates deep learning for budbreak prediction using data collected for multiple grape cultivars. While some cultivars have over 30 seasons of data others have as little as 4 seasons, which can adversely impact prediction accuracy. To address this issue, we investigate multi-task learning, which combines data across all cultivars to make predictions for individual cultivars. Our main result shows that several variants of multi-task learning are all able to significantly improve prediction accuracy compared to learning for each cultivar independently.},
note = {arXiv:2301.01815 [cs]},
keywords = {Cold Hardiness, Computer Science },
pubstate = {published},
tppubtype = {workshop}
}
Grapevine budbreak is a key phenological stage of seasonal development, which serves as a signal for the onset of active growth. This is also when grape plants are most vulnerable to damage from freezing temperatures. Hence, it is important for winegrowers to anticipate the day of budbreak occurrence to protect their vineyards from late spring frost events. This work investigates deep learning for budbreak prediction using data collected for multiple grape cultivars. While some cultivars have over 30 seasons of data others have as little as 4 seasons, which can adversely impact prediction accuracy. To address this issue, we investigate multi-task learning, which combines data across all cultivars to make predictions for individual cultivars. Our main result shows that several variants of multi-task learning are all able to significantly improve prediction accuracy compared to learning for each cultivar independently.
