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0-0 Abstract In this paper, we present ShelfNet, a novel architecture for accurate fast semantic segmentation. Juntang Zhuang · Tommy Tang · Yifan Ding · Sekhar C Tatikonda · Nicha Dvornek · Xenophon Papademetris · James Duncan Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1864 2018-11-27 · In this project, we present ShelfNet, a lightweight convolutional neural network for accurate real-time semantic segmentation. Different from the standard encoder-decoder structure, ShelfNet has multiple encoder-decoder branch pairs with skip connections at each spatial level, which looks like a shelf with multiple columns. The shelf-shaped structure provides multiple paths for information U-Net has been providing state-of-the-art performance in many medical image segmentation problems.
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Neural ordinary differential equations (Neural ODEs) are a new family of deep-learning models with continuous depth. However, the numerical estimation of the gradient in the continuous case is not well solved: existing implementations of the adjoint method suffer from inaccuracy in reverse-time trajectory, while the naive method and the adaptive checkpoint adjoint method (ACA) have 2021-02-09 · Authors: Juntang Zhuang, Nicha C. Dvornek, Sekhar Tatikonda, James S. Duncan Download PDF Abstract: Neural ordinary differential equations (Neural ODEs) are a new family of deep-learning models with continuous depth. juntang-zhuang has 22 repositories available. Follow their code on GitHub. @article{zhuang2020adabelief, title={AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients}, author={Zhuang, Juntang and Tang, Tommy and Ding, Yifan and Tatikonda, Sekhar and Dvornek, Nicha and Papademetris, Xenophon and Duncan, James}, journal={Conference on Neural Information Processing Systems}, year={2020} } Juntang Zhuang, T. Tang, +4 authors J. Duncan; Published 2020; Computer Science, Mathematics; ArXiv; Most popular optimizers for deep learning can be broadly Source: Juntang Zhuang et al.
We demonstrate an explanation for their poorer performance is the inaccuracy of existing gradient estimation methods: the adjoint method has numerical errors in 2020-05-22 · BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis 3 43 retrieve ROI clustering patterns. Also, our GNN design facilitates model inter-44 pretability by regulating intermediate outputs with a novel loss term, which Juntang Zhuang (Yale University) · Nicha Dvornek (Yale University) · Xiaoxiao Li (Yale University) · Sekhar Tatikonda (Yale) · Xenophon Papademetris (Yale University) · James Duncan (Yale University) Streaming Submodular Maximization under a k-Set System Constraint Neural ordinary differential equations (Neural ODEs) are a new family of deeplearning models with continuous depth. However, the numerical estimation of the gradient in the continuous case is not well solved: existing implementations of the adjoint method suffer from inaccuracy in reverse-time trajectory, while the naive method and the adaptive checkpoint adjoint method (ACA) have a memory Upload an image to customize your repository’s social media preview.
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Articles Cited by Co-authors. Title.
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J. Read Juntang Zhuang's latest research, browse their coauthor's research, and play around with their algorithms Juntang ZHUANG | Cited by 81 | of Yale University, CT (YU) | Read 32 publications | Contact Juntang ZHUANG An ideal optimizer considers curva- ture of the loss function, instead of taking a large (small) step where the gradient is large (small). In region 3 , we demonstrate AdaBelief’s advantage over Adam in the “large gradient, small curvature” case. 10/15/2020 ∙ by Juntang Zhuang, et al.
image classification) are significantly inferior to discrete-layer models. We demonstrate an explanation for their poorer performance is the inaccuracy of existing gradient estimation methods: the adjoint method has numerical errors in
2020-05-22 · BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis 3 43 retrieve ROI clustering patterns. Also, our GNN design facilitates model inter-44 pretability by regulating intermediate outputs with a novel loss term, which
Juntang Zhuang (Yale University) · Nicha Dvornek (Yale University) · Xiaoxiao Li (Yale University) · Sekhar Tatikonda (Yale) · Xenophon Papademetris (Yale University) · James Duncan (Yale University) Streaming Submodular Maximization under a k-Set System Constraint
Neural ordinary differential equations (Neural ODEs) are a new family of deeplearning models with continuous depth. However, the numerical estimation of the gradient in the continuous case is not well solved: existing implementations of the adjoint method suffer from inaccuracy in reverse-time trajectory, while the naive method and the adaptive checkpoint adjoint method (ACA) have a memory
Upload an image to customize your repository’s social media preview. Images should be at least 640×320px (1280×640px for best display). Juntang Zhuang James Duncan Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. author = {Yang, Junlin and Dvornek, Nicha C. and Zhang, Fan and Zhuang, Juntang and Chapiro, Julius and Lin, MingDe and Duncan, James S.}, title = {Domain-Agnostic Learning With Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li, Sekhar Tatikonda, Xenophon Papademetris, James Duncan.
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Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar C. Tatikonda, Nicha Dvornek, Xenophon Papademetris, James Duncan. Abstract. Most popular optimizers for deep learning can be broadly categorized as adaptive methods (e.g.~Adam) and accelerated schemes (e.g.~stochastic gradient descent (SGD) with momentum). View the profiles of people named Juntang Zhuang.
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2020. Gradient descent as an approximation of the loss function. Another way to think of optimization is as an approximation.
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View the profiles of people named Juntang Zhuang.
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Xiaoxiao LI*, Nicha Dvornek, Xenophon Papademetris, Juntang Zhuang, Lawrence H. Staib, Pamela Ventola, James Duncan 2-Channel Convolutional 3D Deep Neural Network (2CC3D) for fMRI Analysis: ASD Classification and Feature Learning (ISBI 2018, oral presentation) Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li, Daniel Yang, Pamela Ventola, James Duncan Prediction of pivotal response treatment … 2018-11-27 author = {Yang, Junlin and Dvornek, Nicha C. and Zhang, Fan and Zhuang, Juntang and Chapiro, Julius and Lin, MingDe and Duncan, James S.}, title = {Domain-Agnostic Learning With Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, Juntang Zhuang, Nicha C. Dvornek, Qingyu Zhao, Xiaoxiao Li, Pamela Ventola, James S. Duncan. [Paper] Prediction of Pivotal response treatment outcome with task fMRI using random forest and variable selection Juntang Zhuang. Biomedical Engineering, Yale University. Verified email at yale.edu - Homepage. Articles Cited by Co-authors.
Juntang Zhuang James Duncan Significant progress has been made using fMRI to characterize the brain changes that occur in ASD, a complex neuro-developmental disorder. author = {Yang, Junlin and Dvornek, Nicha C. and Zhang, Fan and Zhuang, Juntang and Chapiro, Julius and Lin, MingDe and Duncan, James S.}, title = {Domain-Agnostic Learning With Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li, Sekhar Tatikonda, Xenophon Papademetris, James Duncan. Proceedings of the 37th International Conference on Graduate Student, Mentor: James Duncan. Fan Zhang, Graduate Student, Mentor: James Duncan. Juntang Zhuang, Graduate Student, Mentor: James Duncan Juntang Zhuang, Nicha C. Dvornek, Sekhar Tatikonda, Xenophon Papademetris, Pamela Ventola, James S. Duncan: Multiple-shooting adjoint method for 25 Jan 2021 Installation and Usage. git clone https://github.com/juntang-zhuang/Adabelief- Optimizer.git.