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no code implementations • 22 Dec 2021 • Ioana Dumitriu, Haixiao Wang, Yizhe Zhu

When the random hypergraph has bounded expected degrees, we provide a spectral algorithm that outputs a partition with at least a $\gamma$ fraction of the vertices classified correctly, where $\gamma\in (0. 5, 1)$ depends on the signal-to-noise ratio (SNR) of the model.

no code implementations • 20 Sep 2021 • Zhichao Wang, Yizhe Zhu

In this paper, we study the two-layer fully connected neural network given by $f(X)=\frac{1}{\sqrt{d_1}}\boldsymbol{a}^\top\sigma\left(WX\right)$, where $X\in\mathbb{R}^{d_0\times n}$ is a deterministic data matrix, $W\in\mathbb{R}^{d_1\times d_0}$ and $\boldsymbol{a}\in\mathbb{R}^{d_1}$ are random Gaussian weights, and $\sigma$ is a nonlinear activation function.

5 code implementations • ICLR 2021 • Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal

Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images.

Ranked #2 on Image Generation on ADE-Indoor

1 code implementation • 16 Dec 2020 • Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal

Moreover, with the proposed sketch generator, the model shows a promising performance on style mixing and style transfer, which require synthesized images to be both style-consistent and semantically meaningful.

no code implementations • 26 Aug 2020 • Ioana Dumitriu, Yizhe Zhu

We compute the eigenvalue fluctuations of uniformly distributed random biregular bipartite graphs with fixed and growing degrees for a large class of analytic functions.

Probability Combinatorics

no code implementations • 27 May 2020 • Bingchen Liu, Kunpeng Song, Yizhe Zhu, Gerard de Melo, Ahmed Elgammal

Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework.

no code implementations • CVPR 2020 • Yizhe Zhu, Martin Renqiang Min, Asim Kadav, Hans Peter Graf

We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e. g., videos and audios) under self-supervision.

no code implementations • ICLR 2020 • Xingchao Peng, Zijun Huang, Yizhe Zhu, Kate Saenko

In this work, we present a principled approach to the problem of federated domain adaptation, which aims to align the representations learned among the different nodes with the data distribution of the target node.

2 code implementations • 23 Oct 2019 • Kameron Decker Harris, Yizhe Zhu

We provide a novel analysis of low-rank tensor completion based on hypergraph expanders.

1 code implementation • 26 May 2019 • Bingchen Liu, Yizhe Zhu, Zuohui Fu, Gerard de Melo, Ahmed Elgammal

Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regularization (OOGAN).

1 code implementation • ICCV 2019 • Yizhe Zhu, Jianwen Xie, Bingchen Liu, Ahmed Elgammal

We investigate learning feature-to-feature translator networks by alternating back-propagation as a general-purpose solution to zero-shot learning (ZSL) problems.

no code implementations • 11 Apr 2019 • Soumik Pal, Yizhe Zhu

We consider the community detection problem in sparse random hypergraphs.

no code implementations • NeurIPS 2019 • Yizhe Zhu, Jianwen Xie, Zhiqiang Tang, Xi Peng, Ahmed Elgammal

Zero-shot learning extends the conventional object classification to the unseen class recognition by introducing semantic representations of classes.

no code implementations • 16 Nov 2018 • Sam Cole, Yizhe Zhu

We consider the exact recovery problem in the hypergraph stochastic block model (HSBM) with $k$ blocks of equal size.

1 code implementation • 20 Aug 2018 • Zhiqiang Tang, Xi Peng, Shijie Geng, Yizhe Zhu, Dimitris N. Metaxas

We design a new connectivity pattern for the U-Net architecture.

Ranked #24 on Pose Estimation on MPII Human Pose

no code implementations • CVPR 2018 • Yizhe Zhu, Mohamed Elhoseiny, Bingchen Liu, Xi Peng, Ahmed Elgammal

Most existing zero-shot learning methods consider the problem as a visual semantic embedding one.

no code implementations • CVPR 2017 • Mohamed Elhoseiny, Yizhe Zhu, Han Zhang, Ahmed Elgammal

We propose a learning framework that is able to connect text terms to its relevant parts and suppress connections to non-visual text terms without any part-text annotations.

no code implementations • ICCV 2017 • Yizhe Zhu, Ahmed Elgammal

The exponentially increasing use of moving platforms for video capture introduces the urgent need to develop the general background subtraction algorithms with the capability to deal with the moving background.

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