Pinned Repositories
bootstrap
The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.
flops-counter.pytorch
Flops counter for convolutional networks in pytorch framework
ICCAD-Accel-GNN
Official Implementation of "Accel-GNN: High-Performance GPU Accelerator Design for Graph Neural Networks"
LinGCN-Neurips23
Official Implementation of "LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference"
normaldist-benchmark
Normally Distributed Random Number Generator Benchmark
PristiQ
ProjectOxford-ClientSDK
The official home for the Microsoft Cognitive Services client SDK and samples
PureWeber2015-Summer
PureWeber 2015 年夏季学期 Web 培训班
QuantumFlow_Tutorial
A step-by-step tutorial of QuantumFlow, using the MNIST sub-dataset {3,6} and the 16-2-2 neural network as an example.
torchquantum
A PyTorch-based framework for Quantum Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
wangger's Repositories
wangger/bootstrap
The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.
wangger/flops-counter.pytorch
Flops counter for convolutional networks in pytorch framework
wangger/ICCAD-Accel-GNN
Official Implementation of "Accel-GNN: High-Performance GPU Accelerator Design for Graph Neural Networks"
wangger/LinGCN-Neurips23
Official Implementation of "LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference"
wangger/normaldist-benchmark
Normally Distributed Random Number Generator Benchmark
wangger/PristiQ
wangger/ProjectOxford-ClientSDK
The official home for the Microsoft Cognitive Services client SDK and samples
wangger/PureWeber2015-Summer
PureWeber 2015 年夏季学期 Web 培训班
wangger/QuantumFlow_Tutorial
A step-by-step tutorial of QuantumFlow, using the MNIST sub-dataset {3,6} and the 16-2-2 neural network as an example.
wangger/torchquantum
A PyTorch-based framework for Quantum Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.