Search Results for author: Zelin Zang

Found 25 papers, 12 papers with code

FGBERT: Function-Driven Pre-trained Gene Language Model for Metagenomics

no code implementations24 Feb 2024 Chenrui Duan, Zelin Zang, Yongjie Xu, Hang He, Zihan Liu, Zijia Song, Ju-Sheng Zheng, Stan Z. Li

Metagenomic data, comprising mixed multi-species genomes, are prevalent in diverse environments like oceans and soils, significantly impacting human health and ecological functions.

Contrastive Learning Language Modelling

Neural Network Diffusion

1 code implementation20 Feb 2024 Kai Wang, Zhaopan Xu, Yukun Zhou, Zelin Zang, Trevor Darrell, Zhuang Liu, Yang You

The autoencoder extracts latent representations of a subset of the trained network parameters.

Must: Maximizing Latent Capacity of Spatial Transcriptomics Data

1 code implementation15 Jan 2024 Zelin Zang, Liangyu Li, Yongjie Xu, Chenrui Duan, Kai Wang, Yang You, Yi Sun, Stan Z. Li

MuST integrates the multi-modality information contained in the ST data effectively into a uniform latent space to provide a foundation for all the downstream tasks.

Deep Manifold Transformation for Protein Representation Learning

no code implementations12 Jan 2024 Bozhen Hu, Zelin Zang, Cheng Tan, Stan Z. Li

Protein representation learning is critical in various tasks in biology, such as drug design and protein structure or function prediction, which has primarily benefited from protein language models and graph neural networks.

Representation Learning

Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding

no code implementations12 Jan 2024 Bozhen Hu, Zelin Zang, Jun Xia, Lirong Wu, Cheng Tan, Stan Z. Li

Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding.

Graph Embedding

Graph-level Protein Representation Learning by Structure Knowledge Refinement

no code implementations5 Jan 2024 Ge Wang, Zelin Zang, Jiangbin Zheng, Jun Xia, Stan Z. Li

The mainstream method is utilizing contrastive learning to facilitate graph feature extraction, known as Graph Contrastive Learning (GCL).

Contrastive Learning Property Prediction +1

EVNet: An Explainable Deep Network for Dimension Reduction

1 code implementation21 Nov 2022 Zelin Zang, Shenghui Cheng, Linyan Lu, Hanchen Xia, Liangyu Li, Yaoting Sun, Yongjie Xu, Lei Shang, Baigui Sun, Stan Z. Li

The proposed techniques are integrated with a visual interface to help the user to adjust EVNet to achieve better DR performance and explainability.

Data Augmentation Dimensionality Reduction

UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection

no code implementations8 Jul 2022 Zelin Zang, Yongjie Xu, Linyan Lu, Yulan Geng, Senqiao Yang, Stan Z. Li

We propose that the ideal DR approach combines both FS and FP into a unified end-to-end manifold learning framework, simultaneously performing fundamental feature discovery while maintaining the intrinsic relationships between data samples in the latent space.

Data Augmentation feature selection

DLME: Deep Local-flatness Manifold Embedding

2 code implementations7 Jul 2022 Zelin Zang, Siyuan Li, Di wu, Ge Wang, Lei Shang, Baigui Sun, Hao Li, Stan Z. Li

To overcome the underconstrained embedding problem, we design a loss and theoretically demonstrate that it leads to a more suitable embedding based on the local flatness.

Contrastive Learning Data Augmentation +1

Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN

3 code implementations27 May 2022 Siyuan Li, Di wu, Fang Wu, Zelin Zang, Stan. Z. Li

We then propose an Architecture-Agnostic Masked Image Modeling framework (A$^2$MIM), which is compatible with both Transformers and CNNs in a unified way.

Instance Segmentation Object Detection +3

GenURL: A General Framework for Unsupervised Representation Learning

1 code implementation27 Oct 2021 Siyuan Li, Zicheng Liu, Zelin Zang, Di wu, ZhiYuan Chen, Stan Z. Li

Unsupervised representation learning (URL) that learns compact embeddings of high-dimensional data without supervision has achieved remarkable progress recently.

Contrastive Learning Dimensionality Reduction +4

Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks

no code implementations20 Oct 2021 Zihan Liu, Yun Luo, Zelin Zang, Stan Z. Li

Gray-box graph attacks aim at disrupting the performance of the victim model by using inconspicuous attacks with limited knowledge of the victim model.

Node Classification Representation Learning

Unsupervised Deep Manifold Attributed Graph Embedding

1 code implementation27 Apr 2021 Zelin Zang, Siyuan Li, Di wu, Jianzhu Guo, Yongjie Xu, Stan Z. Li

Unsupervised attributed graph representation learning is challenging since both structural and feature information are required to be represented in the latent space.

Clustering Graph Embedding +3

Deep Manifold Computing and Visualization Using Elastic Locally Isometric Smoothness

no code implementations1 Jan 2021 Stan Z. Li, Zelin Zang, Lirong Wu

The ability to preserve local geometry of highly nonlinear manifolds in high dimensional spaces and properly unfold them into lower dimensional hyperplanes is the key to the success of manifold computing, nonlinear dimensionality reduction (NLDR) and visualization.

Dimensionality Reduction

Consistent Representation Learning for High Dimensional Data Analysis

no code implementations1 Dec 2020 Stan Z. Li, Lirong Wu, Zelin Zang

In this paper, we propose a novel neural network-based method, called Consistent Representation Learning (CRL), to accomplish the three associated tasks end-to-end and improve the consistencies.

Clustering Dimensionality Reduction +2

Deep Manifold Transformation for Nonlinear Dimensionality Reduction

no code implementations28 Oct 2020 Stan Z. Li, Zelin Zang, Lirong Wu

The LGP constraints constitute the loss for deep manifold learning and serve as geometric regularizers for NLDR network training.

Dimensionality Reduction

Invertible Manifold Learning for Dimension Reduction

1 code implementation7 Oct 2020 Siyuan Li, Haitao Lin, Zelin Zang, Lirong Wu, Jun Xia, Stan Z. Li

Dimension reduction (DR) aims to learn low-dimensional representations of high-dimensional data with the preservation of essential information.

Dimensionality Reduction

Deep Clustering and Representation Learning that Preserves Geometric Structures

no code implementations28 Sep 2020 Lirong Wu, Zicheng Liu, Zelin Zang, Jun Xia, Siyuan Li, Stan Z. Li

To overcome the problem that clusteringoriented losses may deteriorate the geometric structure of embeddings in the latent space, an isometric loss is proposed for preserving intra-manifold structure locally and a ranking loss for inter-manifold structure globally.

Clustering Deep Clustering +1

Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation

1 code implementation21 Sep 2020 Lirong Wu, Zicheng Liu, Zelin Zang, Jun Xia, Siyuan Li, Stan Z. Li

Though manifold-based clustering has become a popular research topic, we observe that one important factor has been omitted by these works, namely that the defined clustering loss may corrupt the local and global structure of the latent space.

Clustering Deep Clustering +1

Markov-Lipschitz Deep Learning

2 code implementations15 Jun 2020 Stan Z. Li, Zelin Zang, Lirong Wu

We propose a novel framework, called Markov-Lipschitz deep learning (MLDL), to tackle geometric deterioration caused by collapse, twisting, or crossing in vector-based neural network transformations for manifold-based representation learning and manifold data generation.

Dimensionality Reduction Representation Learning +1

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