Liang Liang, Minliang Liu, John A. Elefteriades, Wei Sun,
... new library of FEA code and methods, named PyTorch-FEA, by taking advantage of autograd, an automatic differentiation mechanism in PyTorch. We develop a class of PyTorch-FEA functionalities to solve forward and inverse problems ... loss functions, and we demonstrate the capability of PyTorch-FEA in a series of applications related to ... In one of the inverse methods, we combine PyTorch-FEA with deep neural networks (DNNs) to further improve performance. We applied PyTorch-FEA in four fundamental applications for biomechanical analysis ...
Tópico(s): Coronary Interventions and Diagnostics
2023 - Elsevier BV | Computer Methods and Programs in Biomedicine
Jon López-Zorrilla, Xabier M. Aretxabaleta, In Won Yeu, I. Etxebarria, Hegoi Manzano, Nongnuch Artrith,
In this work, we present ænet-PyTorch, a PyTorch-based implementation for training artificial neural network-based machine learning interatomic potentials. Developed as an extension of the atomic energy network (ænet), ænet-PyTorch provides access to all the tools included in ... Here, we demonstrate the main features of ænet-PyTorch and show its performance on open databases. Our ...
Tópico(s): Protein Structure and Dynamics
2023 - American Institute of Physics | The Journal of Chemical Physics
JohnMark Taylor, Nikolaus Kriegeskorte,
... of any DNN. Many models are implemented in PyTorch, the leading framework for building DNN models. Here ... for extracting and characterizing hidden-layer activations in PyTorch models. Uniquely among existing approaches to this problem, ... all intermediate operations, not just those associated with PyTorch module objects, yielding a full record of every ... it uses can be automatically applied to any PyTorch model with no modifications, including models with conditional ( ...
Tópico(s): Functional Brain Connectivity Studies
2023 - Nature Portfolio | Scientific Reports
Lucas Zimmer, Marius Lindauer, Frank Hutter,
... architecture search. In this paper, we introduce Auto-PyTorch, which brings together the best of these two ... to enable fully automated deep learning (AutoDL). Auto-PyTorch achieves state-of-the-art performance on several ... run extensive ablation studies of the full Auto-PyTorch on typical AutoML benchmarks, eventually showing that Auto-PyTorch performs better than several state-of-the-art ...
Tópico(s): Domain Adaptation and Few-Shot Learning
2021 - IEEE Computer Society | IEEE Transactions on Pattern Analysis and Machine Intelligence
Xiang Gao, Farhad Ramezanghorbani, Olexandr Isayev, Justin S. Smith, Adrián E. Roitberg,
This paper presents TorchANI, a PyTorch-based program for training/inference of ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces and other physical properties ... and atomic neural networks are all implemented using PyTorch operators, TorchANI is able to use PyTorch's autograd engine to automatically compute analytical forces ...
Tópico(s): Computational Drug Discovery Methods
2020 - American Chemical Society | Journal of Chemical Information and Modeling
Li Shen, Yanli Zhao, Rohan Varma, Omkar Salpekar, Pieter Noordhuis, Teng Li, Adam Paszke, Jeff Smith, Brian Vaughan, Pritam Damania, Soumith Chintala,
... presents the design, implementation, and evaluation of the PyTorch distributed data parallel module. Py-Torch is a ... the distributed training efficiency. As of v1.5, PyTorch natively provides several techniques to accelerate distributed data ... synchronization. Evaluations show that, when configured appropriately, the PyTorch distributed data parallel module attains near-linear scalability ...
Tópico(s): Advanced Neural Network Applications
2020 - Association for Computing Machinery | Proceedings of the VLDB Endowment
Xingping Zhao, Minqiang Liu, Susu Wu, Baiyun Zhang, Arvind Burjoo, Yimin Yang, Dabao Xu,
... CNNs on ImageNet by applying both TensorFlow and PyTorch. All 3D-TVUS images were normalized and mixed ... VGG16, InceptionV3, ResNet50, and ResNet101 were better in PyTorch as opposed to TensorFlow. Through PyTorch, the best CNN model was InceptionV3 with its ... 0.999, 0.997, and 0.999, respectively. PyTorch also successfully transferred information from the source to ...
Tópico(s): Hernia repair and management
2023 - AME Publishing Company | Quantitative Imaging in Medicine and Surgery
Florian Bruckner, Sabri Koraltan, Claas Abert, Dieter Suess,
... difference library completely based on the tensor library PyTorch. The use of such a high level library ... benefits from the device abstraction and optimizations of PyTorch enabling the efficient execution of micromagnetic simulations on ... Furthermore, handling inverse problems becomes possible by using PyTorch's autograd feature.
Tópico(s): Magnetic Properties and Applications
2023 - Nature Portfolio | Scientific Reports
S. Sagar Imambi, Kolla Bhanu Prakash, G. R. Kanagachidambaresan,
PyTorch is a library for Python programs that encourages deep learning programs. With this receptiveness and convenience ... neural networks using TensorFlow and Keras. [Authors: RajalingappaaShanmugamani]), PyTorch makes it useful in developing deep neural networks. ... for various applications. As Python is for programming, PyTorch is both a magnificent prologue to profound learning ...
Tópico(s): Brain Tumor Detection and Classification
2021 - Springer International Publishing | EAI/Springer Innovations in Communication and Computing
Aazad Abbas, Jacob Mosseri, Johnathan R. Lex, Jay Toor, Bheeshma Ravi, Elias B. Khalil, Cari Whyne,
... to the training set in scikit-learn and PyTorch, with hyperparameters tuned on the validation set. The ... were included in this study. During validation, the PyTorch MLPs had the best MSEs for DOS (0.918) and LOS (0.715). During testing, the PyTorch MLPs similarly performed best based on MSEs for ... minute buffer accuracy for DOS (78.8%), the PyTorch MLP provided the best 1-day buffer accuracy ...
Tópico(s): Orthopedic Infections and Treatments
2021 - Elsevier BV | International Journal of Medical Informatics
Mario Christopher Bedrunka, Dominik Wilde, Martin L. Kliemank, Dirk Reith, Holger Foysi, Andreas Krämer,
... learning and LBM. Here, we introduce Lettuce, a PyTorch-based LBM code with a threefold aim. Lettuce ... LBM models, and enables integrating LBM simulations with PyTorch's deep learning and automatic differentiation facility. As ... turbulence. We also exemplify the added benefit of PyTorch's automatic differentiation framework in flow control and ...
Tópico(s): Model Reduction and Neural Networks
2021 - Springer Science+Business Media | Lecture notes in computer science
... simulation results with reverse-mode automatic differentiation of PyTorch version 1.10.1. In particular, the instability ... comments including restrictions and unusual features: TORCWA utilizes PyTorch version 1.10.1 [1]. PyTorch, https://pytorch.org/
Tópico(s): Plasmonic and Surface Plasmon Research
2022 - Elsevier BV | Computer Physics Communications
Sergey Kastryulin, Jamil Zakirov, Denis Prokopenko, Dmitry V. Dylov,
... forming, restoring, transforming, or enhancing algorithms. We present PyTorch Image Quality (PIQ), a usability-centric library that ... acceleration given the library is used within the PyTorch backend. PyTorch Image Quality is an open source software: https:// ...
2022 - RELX Group (Netherlands) | SSRN Electronic Journal
Christoph Linse, Hammam Alshazly, Thomas Martinetz,
... export the results. We also provide a novel Pytorch module to dynamically link PyTorch with Unity, which gives developers and researchers a ... architectures. The visualization is directly created from the PyTorch class that defines the Pytorch model used for training and testing. This approach ...
Tópico(s): Generative Adversarial Networks and Image Synthesis
2022 - Springer Science+Business Media | Neural Computing and Applications
Qingzhong Wang, Pengfei Zhang, Haoyi Xiong, Jian Zhao,
... top of different deep learning platforms, such as PyTorch and PaddlePaddle, which facilitates researchers to work on ... secured the first place. The version that supports PyTorch is publicly available at https://github.com/ZhaoJ9014/face.evoLVe.PyTorch and the PaddlePaddle version is available at https://github.com/ZhaoJ9014/face.evoLVe.PyTorch/tree/master/paddle. Face.evoLVe has been widely ...
Tópico(s): Face and Expression Recognition
2022 - Elsevier BV | Neurocomputing
Shidan Wang, Donghan M. Yang, Ruichen Rong, Xiaowei Zhan, Guanghua Xiao,
... https://keras.io, last accessed March 2019), and PyTorch (https://pytorch.org, last accessed July 2019).31Paszke A, Gross ... A, Antiga L, Lerer A: Automatic differentiation in PyTorch. 31st Conference on Neural Information Processing Systems (NIPS ...
Tópico(s): Medical Imaging and Analysis
2019 - Elsevier BV | American Journal Of Pathology
Produção Nacional
Felipe de Almeida Florencio, Thiago Valen�, Edward David Moreno, Methanias Colaço Júnior,
... evaluating and comparing these two libraries: TensorFlow and PyTorch. We have used three parameters: Hardware utilization, hardware ... statistical tests. After data analysis, we show that PyTorch library presented a better performance, even though the ...
Tópico(s): Anomaly Detection Techniques and Applications
2019 - Science Publications | Journal of Computer Science
Olivier Codol, Jonathan A. Michaels, Mehrdad Kashefi, J. Andrew Pruszynski, Paul L. Gribble,
... a typical desktop computer. MotorNet is built on PyTorch and therefore can implement any network architecture that is possible using the PyTorch framework. Consequently, it will immediately benefit from advances in artificial intelligence through PyTorch updates. Finally, it is open source, enabling users ...
Tópico(s): Motor Control and Adaptation
2023 - eLife Sciences Publications Ltd | eLife
Wujie Zhou, Yun Zhu, Jingsheng Lei, Rongwang Yang, Lu Yu,
... MB), and inference speed (9.95 fps for PyTorch, batch size of 1, and Intel i5-7500 processor; 93.53 fps for PyTorch, batch size of 1, and NVIDIA TITAN V graphics processor; 936.68 fps for PyTorch, batch size of 20, and graphics processor; 538. ...
Tópico(s): Advanced Image and Video Retrieval Techniques
2023 - Institute of Electrical and Electronics Engineers | IEEE Transactions on Image Processing
Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, Alban Desmaison, Can Balioglu, Pritam Damania, Bernard Nguyen, Geeta Chauhan, Yuchen Hao, Ajit Mathews, Shen Li,
... leverage these technologies. In this paper, we introduce PyTorch Fully Sharded Data Parallel (FSDP) as an industry- ... has been closely co-designed with several key PyTorch core components including Tensor implementation, dispatcher system, and ...
Tópico(s): Advanced Data Storage Technologies
2023 - Association for Computing Machinery | Proceedings of the VLDB Endowment
Syed Ijaz Ul Haq, Muhammad Naveed Tahir, Yubin Lan,
... study, we used two different frameworks, TensorFlow and PyTorch, to apply deep learning algorithms. PyTorch’s implementation of deep learning algorithms performed comparatively ... concluded that the neural network implemented through the PyTorch framework achieves a superior outcome in speed and ...
Tópico(s): Date Palm Research Studies
2023 - Multidisciplinary Digital Publishing Institute | Applied Sciences
Hyoung Suk Suh, Chulmin Kweon, Brian Lester, Sharlotte Kramer, WaiChing Sun,
This paper introduces a publicly available PyTorch-ABAQUS deep-learning framework of a family of plasticity models where the yield surface is implicitly represented by a scalar-valued function. ... of the trained neural networks obtained via the PyTorch library can be automatically converted into a generic ...
Tópico(s): Numerical methods in engineering
2023 - Elsevier BV | Mechanics of Materials
N. Hussain, Aitazaz A. Farooque, Arnold W. Schumann, Farhat Abbas, Bishnu Acharya, Andrew McKenzie‐Gopsill, Ryan Barrett, Hassan Afzaal, Qamar U. Zaman, Muhammad Jehanzeb Masud Cheema,
... plants. Performance of two frameworks, namely TensorFlow and PyTorch, were compared in training, testing, and during inferring ... plant classification (accuracy > 90%). However, the EfficientNet with PyTorch framework showed a maximum accuracy of (0.92– ... GeForce GTX1050. All the DCNNs performed better with PyTorch than TensorFlow frameworks. It was concluded that the ...
Tópico(s): Date Palm Research Studies
2021 - Elsevier BV | Computers and Electronics in Agriculture
Waqed H. Hassan, Hamid H. Hussein, Musa H. Alshammari, Halah Kais Jalal, Sajjad E. Rasheed,
... major modern coding language, being based on the PyTorch interface. The empirical equations and models derived to ... of the equation obtained from the ANN-based PyTorch technique is more accurate in predicting the scour ...
Tópico(s): Soil erosion and sediment transport
2022 - Elsevier BV | Results in Engineering
Huynh Thai Hoc, Radek Šilhavý, Zdenka Prokopová, Petr Šilhavý,
This study compares the performance of Pytorch-based Deep Learning, Multiple Perceptron Neural Networks with Multiple Linear Regression in terms of software effort estimations based on function point analysis. ... level of 0.30, respectively. The findings of Pytorch-based Deep Learning are similar to Multiple Perceptron ... 72%, respectively. The results reveal that both the Pytorch-based Deep Learning and Multiple Perceptron model outperformed ...
Tópico(s): Machine Learning and Data Classification
2022 - Institute of Electrical and Electronics Engineers | IEEE Access
Anmol Chaudhary, Kuldeep Chouhan, Jyoti Gajrani, Bhavna Sharma,
... of deep learning and its practical aspects using PyTorch. The chapter primarily discusses new technologies using deep learning and PyTorch in detail. The chapter discusses the advantages of using PyTorch compared to other deep learning libraries. The chapter ... the various frameworks built with the help of PyTorch. PyTorch consists of various models that increases its flexibility ... a result, many frameworks built on top of PyTorch are discussed in this chapter. The authors believe ... understanding of deep learning making neural networks using PyTorch.
Tópico(s): Machine Learning and Data Classification
2020 - IGI Global | Advances in computer and electrical engineering book series
Nhut-Minh Ho, Himeshi De Silva, John L. Gustafson, Weng‐Fai Wong,
... tool which enables next generation number formats on Pytorch, a widely popular high-level Deep Learning framework. ... DNN) training and inference. Qtorch+ works seamlessly with Pytorch, one of the most versatile DNN frameworks, with ...
Tópico(s): Digital Filter Design and Implementation
2022 - Springer Science+Business Media | Lecture notes in computer science
TorchGAN is a PyTorch based framework for writing succinct and comprehensible code for training and evaluation of Generative Adversarial Networks. The framework's ... evaluation metrics, and zero overhead compared to vanilla PyTorch. By using the framework to implement several popular ... framework for said models against the corresponding baseline PyTorch implementations and observe that TorchGAN's features bear ...
Tópico(s): Model Reduction and Neural Networks
2021 - Open Journals | The Journal of Open Source Software
Minh Doan, Ivan A. Vorobjev, Paul Rees, Andrew Filby, Olaf Wolkenhauer, Anne E. Goldfeld, Judy Lieberman, Natasha S. Barteneva, Anne E. Carpenter, Holger Hennig,
... Recent open-source frameworks, such as TensorFlow, Keras, PyTorch and CAFFE, provide convenient and effective means to ... Recent open-source frameworks, such as TensorFlow, Keras, PyTorch and CAFFE, provide convenient and effective means to ...
Tópico(s): Advanced Fluorescence Microscopy Techniques
2018 - Elsevier BV | Trends in biotechnology
Е. Постников, A. P. Kryukov, S. P. Polyakov, Д. Шипилов, Д. Журов,
... we compare two open source machine learning libraries, PyTorch and TensorFlow, as software platforms for rejecting hadron ...
Tópico(s): Gamma-ray bursts and supernovae
2019 - IOP Publishing | Journal of Physics Conference Series