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MIT
- Massachusetts, USA
- http://zhengqigao.github.io/
- @ZhengqiGao
- in/zhengqi-gao-729b51146
Highlights
- Pro
Stars
Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models
An implementation of EinsumNetworks in PyTorch.
KirchhoffNet: A Scalable Ultra Fast Analog Neural Network
A python package for sampling methods and flow models
NOFIS: Normalizing Flow for Rare Circuit Failure Analysis
[SIGCOMM 2023] Lightning: A Reconfigurable Photonic-Electronic SmartNIC for Fast and Energy-Efficient Inference
Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction (NeurIPS 2023) https://arxiv.org/abs/2310.15416
A PyTorch Implementation of Density Estimation Using Real NVP
[ICLR 23 oral] The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation
Code for our paper "Generative Flow Networks for Discrete Probabilistic Modeling"
A PyTorch Library for Photonic Integrated Circuit Simulation and Photonic AI Computing
A collection of papers in the area of photonic design automation
A Neural Operator-based Integrated Photonic Device Simulation Framework, NeurOLight NeurIPS 2022
This is the repo for multi-corner parametric yield estimation by Bayesian Inference
This is the code for multi-corner failure rate estimation published on TCAD.
Final project in Introduction to Functional Programming-From C/C++ to Haskell at Fudan Univ.
A simulator with programmable photonics and differentiability emphasis
Code for our paper 'Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion' published on ECCV 2022
[ECCV 2022] Tackling Long-Tailed Category Distribution Under Domain Shifts
Code for our paper 'Automatic Synthesis of Broadband Silicon Photonic Devices via Bayesian Optimization'
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.