Academic literature on the topic 'Pytorch model'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
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"
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 textMunjal, 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 textKyrylenko, 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 textPobeda, 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 textBelozerov, 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 textDalskov, 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 textDuan, 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 textLi, 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 textHow, 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 textMcAllister, 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 textDissertations / Theses on the topic "Pytorch model"
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 textAwan, 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(6012219), Ayush Jain. "Using Latent Discourse Indicators to identify goodness in online conversations." Thesis, 2020.
Find full textKalgaonkar, 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 textResearch 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.
(10911822), Priyank Kalgaonkar. "AI on the Edge with CondenseNeXt: An Efficient Deep Neural Network for Devices with Constrained Computational Resources." Thesis, 2021.
Find full textBooks on the topic "Pytorch model"
Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch. New York: Apress L. P., 2021.
Find full textLiu, 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 textDeep Learning with PyTorch: A practical approach to building neural network models using PyTorch. Packt Publishing, 2018.
Find full textSharma, Nitin Ranjan, Akshay Kulkarni, and Adarsha Shivananda. Computer Vision Projects with Pytorch: Design and Develop Production-Grade Models. Apress L. P., 2022.
Find full textMathew, 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 textMishra, Pradeepta. PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models. Apress L. P., 2022.
Find full textGridin, Ivan. Automated Deep Learning Using Neural Network Intelligence: Develop and Design Pytorch and TensorFlow Models Using Python. Apress L. P., 2022.
Find full textMachine Learning with Pytorch and Scikit-Learn: Develop Machine Learning and Deep Learning Models with Python. Packt Publishing, Limited, 2022.
Find full textJulian, David. Deep Learning with Pytorch Quick Start Guide: Learn to Train and Deploy Neural Network Models in Python. Packt Publishing, Limited, 2018.
Find full textSawarkar, 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 textBook chapters on the topic "Pytorch model"
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 textMishra, 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 textKulkarni, 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 textKulkarni, 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 textSingh, 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 textMishra, 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 textMishra, 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 textGuerdoux, 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 textGuerdoux, 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 textChaudhary, 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 textConference papers on the topic "Pytorch model"
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 textKwon, 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 textSudakov, 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 textZheng, 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 textCheng, 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 textDogan, 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 textRozemberczki, 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 textFebvay, 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 textK, 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