Topic: qnn Goto Github
Some thing interesting about qnn
Some thing interesting about qnn
qnn,In this repository, I classify the Iris dataset using Qutrits and IBM Quantum pulse technology.
User: alejomonbar
qnn,QNN-based correlation for frictional pressure drop of non-azeotropic mixtures during cryogenic forced boiling.
User: alejomonbar
qnn,Feed forward QNN
User: ashharr
qnn,A Toolbox for Binarized Spectral Compressive Imaging (NeurIPS 2023)
User: caiyuanhao1998
Home Page: https://arxiv.org/abs/2305.10299
qnn,A toolbox for spectral compressive imaging reconstruction including MST (CVPR 2022), CST (ECCV 2022), DAUHST (NeurIPS 2022), BiSCI (NeurIPS 2023), HDNet (CVPR 2022), MST++ (CVPRW 2022), etc.
User: caiyuanhao1998
qnn,Quantum Neural Network (QNN), Variational Quantum Eigensolver (VQE)
User: clnrp
qnn,Hybrid Quantum Neural Network for classification of the MNIST Dataset using Classiq
User: devilkiller-ag
Home Page: https://jaisarita.hashnode.dev/qcourse511-qnn
qnn,Mobilenet v1 trained on Imagenet for STM32 using extended CMSIS-NN with INT-Q quantization support
Organization: eeeslab
qnn,Official PyTorch repository for Quaternion Generative Adversarial Networks.
User: elegan23
qnn,Implementation of a Quantized Neural Network with low bitwidth of weights, activations and gradients.
User: lorepas
qnn,This repository containts the pytorch scripts to train mixed-precision networks for microcontroller deployment, based on the memory contraints of the target device.
User: mrusci
qnn,Simplified AI runtime integration for mobile app development
Organization: neuralize-ai
Home Page: https://runlocal.ai
qnn,Kotlin bindings for Edgerunner
Organization: neuralize-ai
Home Page: https://github.com/neuralize-ai/edgerunner
qnn,The Qualcomm® AI Hub apps are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.
Organization: quic
qnn,The Qualcomm® AI Hub Models are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.
Organization: quic
Home Page: https://aihub.qualcomm.com
qnn,Low-Precision YOLO on PYNQ with FINN
User: sefaburakokcu
qnn,Diabetic Retinotherapy using Quantum Computing
User: shreyansh0242
qnn,javascript literal object manipulation plug-in in code file | 代码文件中的js字面量对象操作插件
User: wangzongming
qnn,基于react和antd开发的cron表达式生成组件 React and Antd based cron expression generation components
User: wangzongming
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