2020-10-15 · Authors: Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar Tatikonda, Nicha Dvornek, Xenophon Papademetris, James S. Duncan Download PDF 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).

NeurIPS 2020 • Juntang Zhuang • Tommy Tang • Yifan Ding • Sekhar Tatikonda • Nicha Dvornek • Xenophon Papademetris • James S. Duncan 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).

image classification) are significantly inferior to discrete-layer models. View Juntang Zhuang’s profile on LinkedIn, the world’s largest professional community. Juntang’s education is listed on their profile. See the complete profile on LinkedIn and discover Update for adabelief-tf==0.2.0 (Crucial). In adabelief-tf==0.1.0, we modify adabelief-tf to have the same feature as adabelief-pytorch, inlcuding decoupled weight decay and learning rate rectification. 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 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. U-Net has been providing state-of-the-art performance in many medical image segmentation problems.

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Adam) and accelerated schemes (e.g. stochastic gradient descent (SGD) with momentum). @article{zhuang2020adabelief, title={AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients}, author={Zhuang, Juntang and Tang, Tommy and Tatikonda, Sekhar and and Dvornek, Nicha and Ding, Yifan and Papademetris, Xenophon and Duncan, James}, journal={Conference on Neural Information Processing Systems}, year={2020} } Repository for NeurIPS 2020 Spotlight "AdaBelief Optimizer: Adapting stepsizes by the belief in observed gradients" Juntang Zhuang. Username zhuangjt12. Date joined Joined Jun 9, 2020 4 projects adabelief-pytorch. Last released on Feb 16, 2021 PyTorch implementation of AdaBelief Optimizer. TorchDiffEqPack.

Gradient descent accomplished that in a linear form.

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8e6dde2. Git stats. 5 commits Files Permalink. Failed to load Juntang Zhuang 1 , Nicha C Dvornek 2 3 , Qingyu Zhao 4 , Xiaoxiao Li 1 , Pamela Ventola 2 , James S Duncan 1 3 Affiliations 1 Biomedical Engineering, Yale University, New Haven, CT, USA. Juntang Zhuang 1; Tommy Tang2; Yifan Ding3; Sekhar Tatikonda ; Nicha Dvornek1; Xenophon Papademetris 1 ; James S. Duncan 1 Yale University; 2 University of Illinois at Urbana-Champaign; 3 University of Central Florida 2020-10-04 · Juntang Zhuang.

25 Jan 2021 Installation and Usage. git clone https://github.com/juntang-zhuang/Adabelief- Optimizer.git. 1. PyTorch implementations. See folder

Enter email addresses associated with all of your current and historical institutional affiliations, as well as all your previous publications, and the Toronto Paper Matching System. Juntang Zhuang; Nicha C. Dvornek; Sekhar Tatikonda; James S. Duncan fj.zhuang; nicha.dvornek; sekhar.tatikonda; james.duncang@yale.edu Yale University, New Haven, CT, USA ABSTRACT Neural ordinary differential equations (Neural ODEs) are a new family of deep-learning models with continuous depth. However, the numerical estimation of 2020-10-18 2020-06-03 2018-10-01 2020-10-15 2020-10-20 Juntang Zhuang is on Facebook. Join Facebook to connect with Juntang Zhuang and others you may know. Facebook gives people the power to share and makes the world more open and connected.

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. Proceedings of the 37th International Conference on Graduate Student, Mentor: James Duncan.
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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.

Xiaoxiao Li, Yuan Zhou, Nicha C. Dvornek, Muhan Zhang, Juntang Zhuang, Pamela Ventola, James S. Duncan: Pooling Regularized Graph Neural Network for Through extensive experiments, we validated the superior performance of ShelfNet. We provide link to the implementation https://github.com/ juntang- zhuang/ [2] Zhuang, Juntang, et al. "Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE." arXiv preprint arXiv:2006.02493 (2020).
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2020-10-04 · Juntang Zhuang. 1; Pamela Ventola. 4; James S. Duncan. 1; 2; 3; 1. Biomedical Engineering Yale University New Haven USA; 2. Electrical Engineering Yale University New Haven USA; 3. Radiology and Biomedical Imaging, Yale School of Medicine New Haven USA; 4. Child Study Center, Yale School of Medicine New Haven USA; 5. Facebook AI Research New

Proceedings of the 37th International Conference on Juntang Zhuang, Junlin Yang, Lin Gu, Nicha Dvornek; Proceedings of the IEEE/ CVF International Conference on Computer Vision (ICCV), 2019, pp. 0-0.