Search Results for author: Peng Zhou

Found 37 papers, 14 papers with code

Neuromorphic Hebbian learning with magnetic tunnel junction synapses

no code implementations21 Aug 2023 Peng Zhou, Alexander J. Edwards, Frederick B. Mancoff, Sanjeev Aggarwal, Stephen K. Heinrich-Barna, Joseph S. Friedman

Neuromorphic computing aims to mimic both the function and structure of biological neural networks to provide artificial intelligence with extreme efficiency.

Handwritten Digit Recognition

FocalDreamer: Text-driven 3D Editing via Focal-fusion Assembly

no code implementations21 Aug 2023 Yuhan Li, Yishun Dou, Yue Shi, Yu Lei, Xuanhong Chen, Yi Zhang, Peng Zhou, Bingbing Ni

While text-3D editing has made significant strides in leveraging score distillation sampling, emerging approaches still fall short in delivering separable, precise and consistent outcomes that are vital to content creation.

Fair Causal Feature Selection

no code implementations17 Jun 2023 Zhaolong Ling, Enqi Xu, Peng Zhou, Liang Du, Kui Yu, Xindong Wu

Fair feature selection for classification decision tasks has recently garnered significant attention from researchers.

Fairness feature selection

Gradient-based Neuromorphic Learning on Dynamical RRAM Arrays

no code implementations26 Jun 2022 Peng Zhou, Jason K. Eshraghian, Dong-Uk Choi, Wei D. Lu, Sung-Mo Kang

We present MEMprop, the adoption of gradient-based learning to train fully memristive spiking neural networks (MSNNs).

A Fully Memristive Spiking Neural Network with Unsupervised Learning

no code implementations2 Mar 2022 Peng Zhou, Dong-Uk Choi, Jason K. Eshraghian, Sung-Mo Kang

We present a fully memristive spiking neural network (MSNN) consisting of physically-realizable memristive neurons and memristive synapses to implement an unsupervised Spiking Time Dependent Plasticity (STDP) learning rule.

Multi-class Classification Retrieval

SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networks

no code implementations2 Mar 2022 Peng Zhou, Jason K. Eshraghian, Dong-Uk Choi, Sung-Mo Kang

The natural spiking dynamics of the MIF neuron model are fully differentiable, eliminating the need for gradient approximations that are prevalent in the spiking neural network literature.

Synchronous Unsupervised STDP Learning with Stochastic STT-MRAM Switching

no code implementations10 Dec 2021 Peng Zhou, Julie A. Smith, Laura Deremo, Stephen K. Heinrich-Barna, Joseph S. Friedman

The use of analog resistance states for storing weights in neuromorphic systems is impeded by fabrication imprecision and device stochasticity that limit the precision of synapse weights.

Experimental Demonstration of Neuromorphic Network with STT MTJ Synapses

no code implementations9 Dec 2021 Peng Zhou, Alexander J. Edwards, Fred B. Mancoff, Dimitri Houssameddine, Sanjeev Aggarwal, Joseph S. Friedman

We present the first experimental demonstration of a neuromorphic network with magnetic tunnel junction (MTJ) synapses, which performs image recognition via vector-matrix multiplication.

Handwritten Digit Recognition

Improved Method of Stock Trading under Reinforcement Learning Based on DRQN and Sentiment Indicators ARBR

no code implementations19 Nov 2021 Peng Zhou, Jingling Tang

With the application of artificial intelligence in the financial field, quantitative trading is considered to be profitable.

Prediction of Fund Net Value Based on ARIMA-LSTM Hybrid Model

no code implementations19 Nov 2021 Peng Zhou, Fangyi Li

The net value of the fund is affected by performance and market, and the researchers try to quantify these effects to predict the future net value by establishing different models.

An Improved Reinforcement Learning Model Based on Sentiment Analysis

no code implementations19 Nov 2021 Yizhuo Li, Peng Zhou, Fangyi Li, Xiao Yang

The authors combined the deep Q network in reinforcement learning with the sentiment quantitative indicator ARBR to build a high-frequency stock trading model for the share market.

reinforcement-learning Reinforcement Learning (RL) +1

CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis

1 code implementation19 Oct 2021 Peng Zhou, Lingxi Xie, Bingbing Ni, Qi Tian

The style-based GAN (StyleGAN) architecture achieved state-of-the-art results for generating high-quality images, but it lacks explicit and precise control over camera poses.

3D-Aware Image Synthesis Transfer Learning

Deep Video Inpainting Detection

no code implementations26 Jan 2021 Peng Zhou, Ning Yu, Zuxuan Wu, Larry S. Davis, Abhinav Shrivastava, Ser-Nam Lim

This paper studies video inpainting detection, which localizes an inpainted region in a video both spatially and temporally.

Video Inpainting

Omni-GAN: On the Secrets of cGANs and Beyond

2 code implementations ICCV 2021 Peng Zhou, Lingxi Xie, Bingbing Ni, Cong Geng, Qi Tian

The conditional generative adversarial network (cGAN) is a powerful tool of generating high-quality images, but existing approaches mostly suffer unsatisfying performance or the risk of mode collapse.

Conditional Image Generation

Searching towards Class-Aware Generators for Conditional Generative Adversarial Networks

1 code implementation25 Jun 2020 Peng Zhou, Lingxi Xie, Xiaopeng Zhang, Bingbing Ni, Qi Tian

To learn the sampling policy, a Markov decision process is embedded into the search algorithm and a moving average is applied for better stability.

Image Generation

Inclusive GAN: Improving Data and Minority Coverage in Generative Models

1 code implementation ECCV 2020 Ning Yu, Ke Li, Peng Zhou, Jitendra Malik, Larry Davis, Mario Fritz

Generative Adversarial Networks (GANs) have brought about rapid progress towards generating photorealistic images.

DeepStrip: High Resolution Boundary Refinement

no code implementations25 Mar 2020 Peng Zhou, Brian Price, Scott Cohen, Gregg Wilensky, Larry S. Davis

In this paper, we target refining the boundaries in high resolution images given low resolution masks.

Vocal Bursts Intensity Prediction

Reservoir Computing with Planar Nanomagnet Arrays

no code implementations24 Mar 2020 Peng Zhou, Nathan R. McDonald, Alexander J. Edwards, Lisa Loomis, Clare D. Thiem, Joseph S. Friedman

Reservoir computing is an emerging methodology for neuromorphic computing that is especially well-suited for hardware implementations in size, weight, and power (SWaP) constrained environments.

Wasserstein-Bounded Generative Adversarial Networks

no code implementations ICLR 2020 Peng Zhou, Bingbing Ni, Lingxi Xie, Xiaopeng Zhang, Hang Wang, Cong Geng, Qi Tian

In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem.

Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning

no code implementations26 Nov 2019 Kekai Sheng, Wei-Ming Dong, Menglei Chai, Guohui Wang, Peng Zhou, Feiyue Huang, Bao-Gang Hu, Rongrong Ji, Chongyang Ma

In this paper, we revisit the problem of image aesthetic assessment from the self-supervised feature learning perspective.

K-BERT: Enabling Language Representation with Knowledge Graph

2 code implementations arXiv 2019 Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, Ping Wang

For machines to achieve this capability, we propose a knowledge-enabled language representation model (K-BERT) with knowledge graphs (KGs), in which triples are injected into the sentences as domain knowledge.

Knowledge Graphs

Sionnx: Automatic Unit Test Generator for ONNX Conformance

1 code implementation12 Jun 2019 Xinli Cai, Peng Zhou, Shuhan Ding, Guoyang Chen, Weifeng Zhang

Finally, through this easy-to-use specification language, we are able to build a full testing specification which leverages LLVM TableGen to automatically generate unit tests for ONNX operators with much large coverage.

M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental Learning

no code implementations3 Apr 2019 Peng Zhou, Long Mai, Jianming Zhang, Ning Xu, Zuxuan Wu, Larry S. Davis

Instead of sequentially distilling knowledge only from the last model, we directly leverage all previous model snapshots.

Incremental Learning Knowledge Distillation

Generate, Segment and Refine: Towards Generic Manipulation Segmentation

1 code implementation24 Nov 2018 Peng Zhou, Bor-Chun Chen, Xintong Han, Mahyar Najibi, Abhinav Shrivastava, Ser Nam Lim, Larry S. Davis

The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet.

Detecting Image Manipulation Image Generation +3

Pose Transferrable Person Re-Identification

no code implementations CVPR 2018 Jinxian Liu, Bingbing Ni, Yichao Yan, Peng Zhou, Shuo Cheng, Jianguo Hu

On the other hand, in addition to the conventional discriminator of GAN (i. e., to distinguish between REAL/FAKE samples), we propose a novel guider sub-network which encourages the generated sample (i. e., with novel pose) towards better satisfying the ReID loss (i. e., cross-entropy ReID loss, triplet ReID loss).

Person Re-Identification

Learning Rich Features for Image Manipulation Detection

2 code implementations CVPR 2018 Peng Zhou, Xintong Han, Vlad I. Morariu, Larry S. Davis

Image manipulation detection is different from traditional semantic object detection because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned.

Image Manipulation Image Manipulation Detection +3

Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling

3 code implementations COLING 2016 Peng Zhou, Zhenyu Qi, Suncong Zheng, Jiaming Xu, Hongyun Bao, Bo Xu

To integrate the features on both dimensions of the matrix, this paper explores applying 2D max pooling operation to obtain a fixed-length representation of the text.

Binary Classification General Classification +2

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