activity
20202023
most citedBalanced Semi-Supervised Generative Adversarial Network for Damage Assessment from Low-Data Imbalanced-Class Regime

101 citations · 124 across the 6 of their papers we have counts for

collaborators

7 papers

quant-ph2023

NFNet: Non-interacting Fermion Network for Efficient Simulation of Large-scale Quantum Systems

Pengyuan Zhai, Susanne Yelin

We present NFNet, a PyTorch-based framework for polynomial-time simulation of large-scale, continuously controlled quantum systems, supporting parallel matrix computation and auto-…

quant-ph2022

Are Quantum Circuits Better than Neural Networks at Learning Multi-dimensional Discrete Data? An Investigation into Practical Quantum Circuit Generative Models

Pengyuan Zhai

Are multi-layer parameterized quantum circuits (MPQCs) more expressive than classical neural networks (NNs)? How, why, and in what aspects? In this work, we survey and develop intu…

cs.LG2022★ 101 cited

Balanced Semi-Supervised Generative Adversarial Network for Damage Assessment from Low-Data Imbalanced-Class Regime

Yuqing Gao, Pengyuan Zhai, Khalid M. Mosalam

In recent years, applying deep learning (DL) to assess structural damages has gained growing popularity in vision-based structural health monitoring (SHM). However, both data defic…

quant-ph2022

Sample-efficient Quantum Born Machine through Coding Rate Reduction

Pengyuan Zhai

The quantum circuit Born machine (QCBM) is a quantum physics inspired implicit generative model naturally suitable for learning binary images, with a potential advantage of modelin…

cs.CV2022★ 21 cited

Revisiting Sparse Convolutional Model for Visual Recognition

Xili Dai, Mingyang Li, Pengyuan Zhai +6

Despite strong empirical performance for image classification, deep neural networks are often regarded as ``black boxes'' and they are difficult to interpret. On the other hand, sp…

cs.CV2021

Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction

Xili Dai, Shengbang Tong, Mingyang Li +8

This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a closed-loop transcription…