Relevant bibliographies by topics / Pytorch model

Academic literature on the topic 'Pytorch model'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Pytorch model.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Pytorch model"

1

Meshram, Anshuja Anand. "Review on Different Software Tools for Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 565–71. http://dx.doi.org/10.22214/ijraset.2022.39873.

Full text
Abstract:
Abstract: Deep Learning Applications are being applied in various domains in recent years. Training a deep learning model is a very time consuming task. But, many open source frameworks are available to simplify this task. In this review paper we have discussed the features of some popular open source software tools available for deep learning along with their advantages and disadvantages. Software tools discussed in this paper are Tensorflow, Keras, Pytorch, Microsoft Cognitive Toolkit (CNTK). Keywords: Deep Learning, Frameworks, Open Source, Tensorflow, Pytorch, Keras, CNTK
APA, Harvard, Vancouver, ISO, and other styles
2

Munjal, Rohan, Sohaib Arif, Frank Wendler, and Olfa Kanoun. "Comparative Study of Machine-Learning Frameworks for the Elaboration of Feed-Forward Neural Networks by Varying the Complexity of Impedimetric Datasets Synthesized Using Eddy Current Sensors for the Characterization of Bi-Metallic Coins." Sensors 22, no. 4 (February 9, 2022): 1312. http://dx.doi.org/10.3390/s22041312.

Full text
Abstract:
A suitable framework for the development of artificial neural networks is important because it decides the level of accuracy, which can be reached for a certain dataset and increases the certainty about the reached classification results. In this paper, we conduct a comparative study for the performance of four frameworks, Keras with TensorFlow, Pytorch, TensorFlow, and Cognitive Toolkit (CNTK), for the elaboration of neural networks. The number of neurons in the hidden layer of the neural networks is varied from 8 to 64 to understand its effect on the performance metrics of the frameworks. A test dataset is synthesized using an analytical model and real measured impedance spectra by an eddy current sensor coil on EUR 2 and TRY 1 coins. The dataset has been extended by using a novel method based on interpolation technique to create datasets with different difficulty levels to replicate the scenario with a good imitation of EUR 2 coins and to investigate the limit of the prediction accuracy. It was observed that the compared frameworks have high accuracy performance for a lower level of difficulty in the dataset. As the difficulty in the dataset is raised, there was a drop in the accuracy of CNTK and Keras with TensorFlow depending upon the number of neurons in the hidden layers. It was observed that CNTK has the overall worst accuracy performance with an increase in the difficulty level of the datasets. Therefore, the major comparison was confined to Pytorch and TensorFlow. It was observed for Pytorch and TensorFlow with 32 and 64 neurons in hidden layers that there is a minor drop in the accuracy with an increase in the difficulty level of the dataset and was above 90% until both the coins were 80% closer to each other in terms of electrical and magnetic properties. However, Pytorch with 32 neurons in the hidden layer has a reduction in model size by 70% and 16.3% and predicts the class, 73.6% and 15.6% faster in comparison to TensorFlow and Pytorch with 64 neurons.
APA, Harvard, Vancouver, ISO, and other styles
3

Kyrylenko, O. M. "Development of a method of re-identification of a person." Optoelectronic Information-Power Technologies 41, no. 1 (May 2, 2022): 25–32. http://dx.doi.org/10.31649/1681-7893-2021-41-1-25-32.

Full text
Abstract:
The review of OSNet neural network architecture is made for the purpose of training of own models of re-identification of the person. The structure of the neural network was also considered. Existing data sets for model training are investigated. Models were trained using PyTorch. The obtained own models were tested on the validation databases Market-1501 and DukeMTMC-reID. The results of learning neural network models are presented. The results are obtained in comparison with existing analogues.
APA, Harvard, Vancouver, ISO, and other styles
4

Pobeda, Sergey, M. Chernyh, F. Makarenko, and Konstantin Zolnikov. "Creation of a behavioral model of a LDMOS transistor based on an artificial MLP neural network and its description in Verilog-A language." Modeling of systems and processes 14, no. 2 (July 26, 2021): 28–34. http://dx.doi.org/10.12737/2219-0767-2021-14-2-28-34.

Full text
Abstract:
The article deals with the creation of a behavioral model of lateral metal oxide transistors (LDMOS) based on a neural network of the multilayer percep-tron type. The model is identified using a backpropa-gation algorithm. Demonstrated the process of creating an ANN model using Pytorch, a machine learning framework for the Python language, with subsequent transfer to the standard analog circuit modeling lan-guage Verilog-A.
APA, Harvard, Vancouver, ISO, and other styles
5

Belozerov, Ilya Andreevich, and Vladimir Anatolievich Sudakov. "Investigation of machine learning models for medical image segmentation." Keldysh Institute Preprints, no. 37 (2022): 1–15. http://dx.doi.org/10.20948/prepr-2022-37.

Full text
Abstract:
On the example of X-ray images of human lungs, the analysis and construction of models of semantic segmentation of computer vision is carried out. The paper explores various approaches to medical image processing, comparing methods for implementing deep learning models and evaluating them. 5 models of neural networks have been developed to perform the segmentation task, implemented using such well-known libraries as: TensorFlow and PyTorch. The model with the best performance can be used to build a system for automatic segmentation of various images of patients and calculate the characteristics of their organs.
APA, Harvard, Vancouver, ISO, and other styles
6

Dalskov, Anders, Daniel Escudero, and Marcel Keller. "Secure Evaluation of Quantized Neural Networks." Proceedings on Privacy Enhancing Technologies 2020, no. 4 (October 1, 2020): 355–75. http://dx.doi.org/10.2478/popets-2020-0077.

Full text
Abstract:
AbstractWe investigate two questions in this paper: First, we ask to what extent “MPC friendly” models are already supported by major Machine Learning frameworks such as TensorFlow or PyTorch. Prior works provide protocols that only work on fixed-point integers and specialized activation functions, two aspects that are not supported by popular Machine Learning frameworks, and the need for these specialized model representations means that it is hard, and often impossible, to use e.g., TensorFlow to design, train and test models that later have to be evaluated securely. Second, we ask to what extent the functionality for evaluating Neural Networks already exists in general-purpose MPC frameworks. These frameworks have received more scrutiny, are better documented and supported on more platforms. Furthermore, they are typically flexible in terms of the threat model they support. In contrast, most secure evaluation protocols in the literature are targeted to a specific threat model and their implementations are only a “proof-of-concept”, making it very hard for their adoption in practice. We answer both of the above questions in a positive way:We observe that the quantization techniques supported by both TensorFlow, PyTorch and MXNet can provide models in a representation that can be evaluated securely; and moreover, that this evaluation can be performed by a general purpose MPC framework. We perform extensive benchmarks to understand the exact trade-offs between different corruption models, network sizes and efficiency. These experiments provide an interesting insight into cost between active and passive security, as well as honest and dishonest majority. Our work shows then that the separating line between existing ML frameworks and existing MPC protocols may be narrower than implicitly suggested by previous works.
APA, Harvard, Vancouver, ISO, and other styles
7

Duan, Bo, Zhengmin Xu, Lili Pan, Wenxia Chen, and Zhongwei Qiao. "Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model." Journal of Healthcare Engineering 2022 (April 13, 2022): 1–6. http://dx.doi.org/10.1155/2022/4814577.

Full text
Abstract:
In order to compare magnetic resonance imaging (MRI) findings of patients with large vestibular aqueduct syndrome (LVAS) in the stable hearing loss (HL) group and the fluctuating HL group, this paper provides reference for clinicians' early intervention. From January 2001 to January 2016, patients with hearing impairment diagnosed as LVAS in infancy in the Department of Otorhinolaryngology, Head and Neck Surgery, Children's Hospital of Fudan University were collected and divided into the stable HL group (n = 29) and the fluctuating HL group (n = 30). MRI images at initial diagnosis were collected, and various deep learning neural network training models were established based on PyTorch to classify and predict the two series. Vgg16_bn, vgg19_bn, and ResNet18, convolutional neural networks (CNNs) with fewer layers, had favorable effects for model building, with accs of 0.9, 0.8, and 0.85, respectively. ResNet50, a CNN with multiple layers and an acc of 0.54, had relatively poor effects. The GoogLeNet-trained model performed best, with an acc of 0.98. We conclude that deep learning-based radiomics can assist doctors in accurately predicting LVAS patients to classify them into either fluctuating or stable HL types and adopt differentiated treatment methods.
APA, Harvard, Vancouver, ISO, and other styles
8

Li, Yaxin, Wei Jin, Han Xu, and Jiliang Tang. "DeepRobust: a Platform for Adversarial Attacks and Defenses." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 16078–80. http://dx.doi.org/10.1609/aaai.v35i18.18017.

Full text
Abstract:
DeepRobust is a PyTorch platform for generating adversarial examples and building robust machine learning models for different data domains. Users can easily evaluate the attack performance against different defense methods with DeepRobust and get performance analyzing visualization. In this paper, we introduce the functions of DeepRobust with detailed instructions. We believe that DeepRobust is a useful tool to measure deep learning model robustness and to find the suitable countermeasures against adversarial attacks. The platform is kept updated and can be found at https://github.com/DSE-MSU/DeepRobust. More details of instruction can be found in the documentation at https://deeprobust.readthedocs.io/en/latest/.
APA, Harvard, Vancouver, ISO, and other styles
9

How, Chun Kit, Ismail Mohd Khairuddin, Mohd Azraai Mohd Razman, Anwar P. P. Abdul Majeed, and Wan Hasbullah Mohd Isa. "Development of Audio-Visual Speech Recognition using Deep-Learning Technique." MEKATRONIKA 4, no. 1 (June 27, 2022): 88–95. http://dx.doi.org/10.15282/mekatronika.v4i1.8625.

Full text
Abstract:
Deep learning is a technique with artificial intelligent (AI) that simulate humans’ learning behavior. Audio-visual speech recognition is important for the listener understand the emotions behind the spoken words truly. In this thesis, two different deep learning models, Convolutional Neural Network (CNN) and Deep Neural Network (DNN), were developed to recognize the speech’s emotion from the dataset. Pytorch framework with torchaudio library was used. Both models were given the same training, validation, testing, and augmented datasets. The training will be stopped when the training loop reaches ten epochs, or the validation loss function does not improve for five epochs. At the end, the highest accuracy and lowest loss function of CNN model in the training dataset are 76.50% and 0.006029 respectively, meanwhile the DNN model achieved 75.42% and 0.086643 respectively. Both models were evaluated using confusion matrix. In conclusion, CNN model has higher performance than DNN model, but needs to improvise as the accuracy of testing dataset is low and the loss function is high.
APA, Harvard, Vancouver, ISO, and other styles
10

McAllister, Dianna, Mauro Mendez, Ariana Bermúdez, and Pascal Tyrrell. "Visualization of Layers Within a Convolutional Neural Network Using Gradient Activation Maps." Journal of Undergraduate Life Sciences 14, no. 1 (December 31, 2020): 6. http://dx.doi.org/10.33137/juls.v14i1.35833.

Full text
Abstract:
Introduction: Convolutional neural networks (CNNs) are machine learning tools that have great potential in the field of medical imaging. However, it is often regarded as a “black box” as the process that is used by the machine to acquire a result is not transparent. It would be valuable to find a method to be able to understand how the machine comes to its decision. Therefore, the purpose of this study is to examine how effective gradient-weighted class activation mapping (grad-CAM) visualizations are for certain layers in a CNN-based dental x-ray artifact prediction model. Methods: To tackle this project, Python code using PyTorch trained a CNN to classify dental plates as unusable or usable depending on the presence of artifacts. Furthermore, Python using PyTorch was also used to overlay grad-CAM visualizations on the given input images for various layers within the model. One image with seventeen different overlays of artifacts was used in this study. Results: In earlier layers, the model appeared to focus on general features such as lines and edges of the teeth, while in later layers, the model was more interested in detailed aspects of the image. All images that contained artifacts resulted in the model focusing on more detailed areas of the image rather than the artifacts themselves. Whereas the images without artifacts resulted in the model focusing on the visualization of areas that surrounded the teeth. Discussion and Conclusion: As subsequent layers examined more detailed aspects of the image as shown by the grad-CAM visualizations, they provided better insight into how the model processes information when it is making its classifications. Since all the images with artifacts showed similar trends in the visualizations of the various layers, it provides evidence to suggest that the location and size of the artifact does not affect the model’s pattern recognition and image classification.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Pytorch model"

1

Kazan, Baran. "Additional Classes Effect on Model Accuracy using Transfer Learning." Thesis, Högskolan i Gävle, Datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-33970.

Full text
Abstract:
This empirical research study discusses how much the model’s accuracy changes when adding a new image class by using a pre-trained model with the same labels and measuring the precision of the previous classes to observe the changes. The purpose is to determine if using transfer learning is beneficial for users that do not have enough data to train a model. The pre-trained model that was used to create a new model was the Inception V3. It has the same labels as the eight different classes that were used to train the model. To test this model, classes of wild and non-wild animals were taken as samples. The algorithm used to train the model was implemented in a single class programmed in Python programming language with PyTorch and TensorBoard library. The Tensorboard library was used to collect and represent the result. Research results showed that the accuracy of the first two classes was 94.96% in training and 97.07% in validation. When training the model with a total of eight classes, the accuracy was 91.89% in training and 95.40 in validation. The precision of both classes was detected at 100% when the model solely had cat and dog classes. After adding six additional classes in the model, the precision changed to 95.82% of the cats and 97.16% of the dogs.
APA, Harvard, Vancouver, ISO, and other styles
2

Awan, Ammar Ahmad. "Co-designing Communication Middleware and Deep Learning Frameworks for High-Performance DNN Training on HPC Systems." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587433770960088.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

(6012219), Ayush Jain. "Using Latent Discourse Indicators to identify goodness in online conversations." Thesis, 2020.

Find full text
Abstract:
In this work, we model latent discourse indicators to classify constructive and collaborative conversations online. Such conversations are considered good as they are rich in content and have a sense of direction to resolve an issue, solve a problem or gain new insights and knowledge. These unique discourse indicators are able to characterize flow of information, sentiment and community structure within discussions. We build a deep relational model that captures these complex discourse behaviors as latent variables and make a global prediction about overall conversation based on these higher level discourse behaviors. DRaiL, a Declarative Deep Relational Learning platform built on PyTorch, is used for our task in which relevant discourse behaviors are formulated as discrete latent variables and scored using a deep model. These variables capture the nuances involved in online conversations and provide the information needed for predicting the presence or absence of collaborative and constructive characterization in the entire conversational thread. We show that the joint modeling of such competing latent behaviors results in a performance improvement over the traditional direct classification methods in which all the raw features are just combined together to predict the final decision. The Yahoo News Annotated Comments Corpus is used as a dataset containing discussions on Yahoo news forums and final labels are annotated based on our precise and restricted definitions of positively labeled conversations. We formulated our annotation guidelines based on a sample set of conversations and resolved any conflict in specific annotation by revisiting those examples again.
APA, Harvard, Vancouver, ISO, and other styles
4

Kalgaonkar, Priyank B. "AI on the Edge with CondenseNeXt: An Efficient Deep Neural Network for Devices with Constrained Computational Resources." Thesis, 2021. http://dx.doi.org/10.7912/C2/64.

Full text
Abstract:
Indiana University-Purdue University Indianapolis (IUPUI)
Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove) redundant and insignificant elements that either are irrelevant or do not affect performance of the network upon disposition. Cardinality, a new dimension to the existing spatial dimensions, and class-balanced focal loss function, a weighting factor inversely proportional to the number of samples, has been incorporated in order to relieve the harsh effects of pruning, into the design of CondenseNeXt’s algorithm. Furthermore, extensive analyses of this novel CNN architecture was performed on three benchmarking image datasets: CIFAR-10, CIFAR-100 and ImageNet by deploying the trained weight on to an ARM-based embedded computing platform: NXP BlueBox 2.0, for real-time image classification. The outputs are observed in real-time in RTMaps Remote Studio’s console to verify the correctness of classes being predicted. CondenseNeXt achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error), and up to 59.98% reduction in forward FLOPs compared to CondenseNet. CondenseNeXt can also achieve a final trained model size of 2.9 MB, however at the cost of 2.26% in accuracy loss. Thus, performing image classification on ARM-Based computing platforms without requiring a CUDA enabled GPU support, with outstanding efficiency.
APA, Harvard, Vancouver, ISO, and other styles
5

(10911822), Priyank Kalgaonkar. "AI on the Edge with CondenseNeXt: An Efficient Deep Neural Network for Devices with Constrained Computational Resources." Thesis, 2021.

Find full text
Abstract:
Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove) redundant and insignificant elements that either are irrelevant or do not affect performance of the network upon disposition. Cardinality, a new dimension to the existing spatial dimensions, and class-balanced focal loss function, a weighting factor inversely proportional to the number of samples, has been incorporated in order to relieve the harsh effects of pruning, into the design of CondenseNeXt’s algorithm. Furthermore, extensive analyses of this novel CNN architecture was performed on three benchmarking image datasets: CIFAR-10, CIFAR-100 and ImageNet by deploying the trained weight on to an ARM-based embedded computing platform: NXP BlueBox 2.0, for real-time image classification. The outputs are observed in real-time in RTMaps Remote Studio’s console to verify the correctness of classes being predicted. CondenseNeXt achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error), and up to 59.98% reduction in forward FLOPs compared to CondenseNet. CondenseNeXt can also achieve a final trained model size of 2.9 MB, however at the cost of 2.26% in accuracy loss. Thus, performing image classification on ARM-Based computing platforms without requiring a CUDA enabled GPU support, with outstanding efficiency.
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Pytorch model"

1

Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch. New York: Apress L. P., 2021.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Liu, Yuxi (Hayden). PyTorch 1.x Reinforcement Learning Cookbook: Over 60 recipes to design, develop, and deploy self-learning AI models using Python. Birmingham, UK: Packt Publishing, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch. Packt Publishing, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Sharma, Nitin Ranjan, Akshay Kulkarni, and Adarsha Shivananda. Computer Vision Projects with Pytorch: Design and Develop Production-Grade Models. Apress L. P., 2022.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Mathew, Jibin. PyTorch Artificial Intelligence Fundamentals: A Recipe-Based Approach to Design, Build and Deploy Your Own AI Models with Pytorch 1. x. Packt Publishing, Limited, 2020.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Mishra, Pradeepta. PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models. Apress L. P., 2022.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Gridin, Ivan. Automated Deep Learning Using Neural Network Intelligence: Develop and Design Pytorch and TensorFlow Models Using Python. Apress L. P., 2022.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Machine Learning with Pytorch and Scikit-Learn: Develop Machine Learning and Deep Learning Models with Python. Packt Publishing, Limited, 2022.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Julian, David. Deep Learning with Pytorch Quick Start Guide: Learn to Train and Deploy Neural Network Models in Python. Packt Publishing, Limited, 2018.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Sawarkar, Kunal, and Dheeraj Arremsetty. Deep Learning with PyTorch Lightning: Build and Train High-Performance Artificial Intelligence and Self-Supervised Models Using Python. Packt Publishing, Limited, 2021.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Pytorch model"

1

Mishra, Pradeepta. "Distributed PyTorch Modelling, Model Optimization, and Deployment." In PyTorch Recipes, 187–212. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8925-9_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Mishra, Pradeepta. "PyTorch Model Interpretability and Interface to Sklearn." In PyTorch Recipes, 237–60. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8925-9_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kulkarni, Akshay, Adarsha Shivananda, and Nitin Ranjan Sharma. "Building an Object Detection Model." In Computer Vision Projects with PyTorch, 85–128. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8273-1_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Kulkarni, Akshay, Adarsha Shivananda, and Nitin Ranjan Sharma. "Building an Image Segmentation Model." In Computer Vision Projects with PyTorch, 129–66. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8273-1_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Singh, Chandan, Wooseok Ha, and Bin Yu. "Interpreting and Improving Deep-Learning Models with Reality Checks." In xxAI - Beyond Explainable AI, 229–54. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_12.

Full text
Abstract:
AbstractRecent deep-learning models have achieved impressive predictive performance by learning complex functions of many variables, often at the cost of interpretability. This chapter covers recent work aiming to interpret models by attributing importance to features and feature groups for a single prediction. Importantly, the proposed attributions assign importance to interactions between features, in addition to features in isolation. These attributions are shown to yield insights across real-world domains, including bio-imaging, cosmology image and natural-language processing. We then show how these attributions can be used to directly improve the generalization of a neural network or to distill it into a simple model. Throughout the chapter, we emphasize the use of reality checks to scrutinize the proposed interpretation techniques. (Code for all methods in this chapter is available at "Image missing"github.com/csinva and "Image missing"github.com/Yu-Group, implemented in PyTorch [54]).
APA, Harvard, Vancouver, ISO, and other styles
6

Mishra, Pradeepta. "Fine-Tuning Deep Learning Models Using PyTorch." In PyTorch Recipes, 151–64. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4258-2_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Mishra, Pradeepta. "Fine-Tuning Deep Learning Models Using PyTorch." In PyTorch Recipes, 157–70. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8925-9_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Guerdoux, Guillaume, Théophile Tiffet, and Cedric Bousquet. "Inference Time of a CamemBERT Deep Learning Model for Sentiment Analysis of COVID Vaccines on Twitter." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220714.

Full text
Abstract:
In previous work, we implemented a deep learning model with CamemBERT and PyTorch, and built a microservices architecture using the TorchServe serving library. Without TorchServe, inference time was three times faster when the model was loaded once in memory compared when the model was loaded each time. The preloaded model without TorchServe presented comparable inference time with the TorchServe instance. However, using a PyTorch preloaded model in a web application without TorchServe would necessitate to implement functionalities already present in TorchServe.
APA, Harvard, Vancouver, ISO, and other styles
9

Guerdoux, Guillaume, Bissan Audeh, Théophile Tiffet, and Cédric Bousquet. "Implementing a Microservices Architecture for Predicting the Opinion of Twitter Users on COVID Vaccines." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220417.

Full text
Abstract:
A strong trend in the software industry is to merge the activities of deployment and operationalization through the DevOps approach, which in the case of artificial intelligence is called Machine Learning Operations (MLOps). We present here a microservices architecture containing the whole pipeline (frontend, backend, data predictions) hosted in Docker containers which exposes a model implemented for opinion prediction in Twitter on the COVID vaccines. This is the first description in the literature of implementing a microservice architecture using TorchServe, a library for serving Pytorch models.
APA, Harvard, Vancouver, ISO, and other styles
10

Chaudhary, Anmol, Kuldeep Singh Chouhan, Jyoti Gajrani, and Bhavna Sharma. "Deep Learning With PyTorch." In Machine Learning and Deep Learning in Real-Time Applications, 61–95. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3095-5.ch003.

Full text
Abstract:
In the last decade, deep learning has seen exponential growth due to rise in computational power as a result of graphics processing units (GPUs) and a large amount of data due to the democratization of the internet and smartphones. This chapter aims to throw light on both the theoretical aspects 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 discusses some of the practical applications like image classification and machine translation. The chapter also discusses the various frameworks built with the help of PyTorch. PyTorch consists of various models that increases its flexibility and accessibility to a greater extent. As a result, many frameworks built on top of PyTorch are discussed in this chapter. The authors believe that this chapter will help readers in getting a better understanding of deep learning making neural networks using PyTorch.
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Pytorch model"

1

Lee, Kyung Hee, Jaebok Park, Seon-Tae Kim, Ji Young Kwak, and Chang Sik Cho. "Design of NNEF-PyTorch Neural Network Model Converter." In 2021 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2021. http://dx.doi.org/10.1109/ictc52510.2021.9621003.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Kwon, Hyunjeong, Hyun Mi Kim, Chun-Gi Lyuh, Jin-Kyu Kim, Jinho Han, and Youngsu Kwon. "AIWareK: Compiling PyTorch Model for AI Processor Using MLIR Framework." In 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS). IEEE, 2022. http://dx.doi.org/10.1109/aicas54282.2022.9869913.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Sudakov, V., and N. Yashin. "IMPLEMENTING THE GRAPH MODEL OF THE SPREAD OF A PANDEMIC ON GPUS." In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.53.71.001.

Full text
Abstract:
Modeling the spread of viruses is an urgent task in modern conditions. In the created model, contactsbetween people are represented in the form of the Watz and Strogatz graph. We studied graphs withtens of thousands of vertices with a simulation period of six months. The paper proposes methods foraccelerating computations on graph models using graphics processors. In the considered problem,there were two resource-intensive computational tasks: generating an adjacency matrix of a graph thatsimulates the presence of contacts between people and traversing this graph in order to simulateinfection. The calculations were carried out in sequential mode and with acceleration on GPUs. Themodeling system software is implemented using the Cuda, CuPy, PyTorch libraries. The calculationswere carried out using the Tesla T4 graphics accelerator. Compared to computations without usinggraphics accelerators, their application gave a 7-fold increase in speed.
APA, Harvard, Vancouver, ISO, and other styles
4

Zheng, Ling-Yun, Zhi-Gang Zhang, and Xin-Wen Wang. "Model Evaluation of Various Thermo-Physical Properties of Nanofluids and ANN Modelling for 10kWe Integrated Reactor." In 2022 29th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icone29-92476.

Full text
Abstract:
Abstract A 10kWe integrated reactor with Stirling generator is in design, to satisfy China’s power demand for both Earth orbit and deep space exploration in the next two decades. The integration of the core and the thermoelectric conversion system, reduces the number of tubes and pump structures, which leads to a higher energy conversion rate, fewer failure risks and coolant leaks. The waste heat of this reactor would be transferred through its heat pipes to its radiators, then to the space. Applying nanofluids would help reduce the heat pipe sizes, because nanofluids have great alterations in their thermo-physical properties with a small fraction of nanoparticles. Numerous models have been proposed to characterize the thermo-physical properties of nanofluids. However, it is found that researchers have different, sometimes even contradictory conclusions about some of the properties. At the same time, these properties could be affected by various aspects, the simple models are not sufficient for the reference. This work focuses on evaluating the models of density, specific heat capacity, thermal conductivity, viscosity, and Nusselt number of nanofluids with statistical methods, and provides reference thermo-physical properties for the design of the heat pipes in the space reactor. For this reason, a great amount of experimental data is collected. Profiting from the collected data, artificial neural network (ANN) models based on Pytorch are trained and compared with the other models.
APA, Harvard, Vancouver, ISO, and other styles
5

Cheng, Xiang, Yunzhe Hao, Jiaming Xu, and Bo Xu. "LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/211.

Full text
Abstract:
Spiking Neural Network (SNN) is considered more biologically plausible and energy-efficient on emerging neuromorphic hardware. Recently backpropagation algorithm has been utilized for training SNN, which allows SNN to go deeper and achieve higher performance. However, most existing SNN models for object recognition are mainly convolutional structures or fully-connected structures, which only have inter-layer connections, but no intra-layer connections. Inspired by Lateral Interactions in neuroscience, we propose a high-performance and noise-robust Spiking Neural Network (dubbed LISNN). Based on the convolutional SNN, we model the lateral interactions between spatially adjacent neurons and integrate it into the spiking neuron membrane potential formula, then build a multi-layer SNN on a popular deep learning framework, i.\,e., PyTorch. We utilize the pseudo-derivative method to solve the non-differentiable problem when applying backpropagation to train LISNN and test LISNN on multiple standard datasets. Experimental results demonstrate that the proposed model can achieve competitive or better performance compared to current state-of-the-art spiking neural networks on MNIST, Fashion-MNIST, and N-MNIST datasets. Besides, thanks to lateral interactions, our model processes stronger noise-robustness than other SNN. Our work brings a biologically plausible mechanism into SNN, hoping that it can help us understand the visual information processing in the brain.
APA, Harvard, Vancouver, ISO, and other styles
6

Dogan, Eren, H. Fatih Ugurdag, and Hasan Unlu. "Using Deep Compression on PyTorch Models for Autonomous Systems." In 2022 30th Signal Processing and Communications Applications Conference (SIU). IEEE, 2022. http://dx.doi.org/10.1109/siu55565.2022.9864848.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Rozemberczki, Benedek, Paul Scherer, Yixuan He, George Panagopoulos, Alexander Riedel, Maria Astefanoaei, Oliver Kiss, et al. "PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models." In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459637.3482014.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Febvay, Mathieu, and Ahmed Bounekkar. "Deep Learning Frameworks Evaluation for Image Classification on Resource Constrained Device." In 11th International Conference on Embedded Systems and Applications (EMSA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120603.

Full text
Abstract:
Each new generation of smartphone gains capabilities that increase performance and power efficiency allowing us to use them for increasingly complex calculations such as Deep Learning. This paper implemented four Android deep learning inference frameworks (TFLite, MNN, NCNN and PyTorch) to evaluate the most recent generation of System On a Chip (SoC) Samsung Exynos 2100, Qualcomm Snapdragon 865+ and 865. Our work focused on image classification task using five state-of-the-art models. The 50 000 images of the ImageNet 2012 validation subset were inferred. Latency and accuracy with various scenarios like CPU, OpenCL, Vulkan with and without multi-threading were measured. Power efficiency and realworld use-case were evaluated from these results as we run the same experiment on the device's camera stream until they consumed 3% of their battery. Our results show that low-level software optimizations, image pre-processing algorithms, conversion process and cooling design have an impact on latency, accuracy and energy efficiency.
APA, Harvard, Vancouver, ISO, and other styles
9

K, Shalini, Abhishek Kumar Srivastava, Surendra Allam, and Dilip Lilaramani. "Comparative analysis on Deep Convolution Neural Network models using Pytorch and OpenCV DNN frameworks for identifying optimum fruit detection solution on RISC-V architecture." In 2021 IEEE Mysore Sub Section International Conference (MysuruCon). IEEE, 2021. http://dx.doi.org/10.1109/mysurucon52639.2021.9641594.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Pytorch model' for particular source types:
Journal articles Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography