Search Results for author: Hongteng Xu

Found 58 papers, 24 papers with code

Generalizable Face Landmarking Guided by Conditional Face Warping

1 code implementation18 Apr 2024 Jiayi Liang, Haotian Liu, Hongteng Xu, Dixin Luo

Given a pair of real and stylized facial images, the conditional face warper predicts a warping field from the real face to the stylized one, in which the face landmarker predicts the ending points of the warping field and provides us with high-quality pseudo landmarks for the corresponding stylized facial images.

Domain Adaptation

To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process

1 code implementation4 Apr 2024 Zhongxiang Sun, Zihua Si, Xiao Zhang, Xiaoxue Zang, Yang song, Hongteng Xu, Jun Xu

The model, referred to as Neural Hawkes Process-based Open-App Motivation prediction model (NHP-OAM), employs a hierarchical transformer and a novel intensity function to encode multiple factors, and open-app motivation prediction layer to integrate time and user-specific information for predicting users' open-app motivations.

Robust Graph Matching Using An Unbalanced Hierarchical Optimal Transport Framework

no code implementations18 Oct 2023 Haoran Cheng, Dixin Luo, Hongteng Xu

Given two graphs, we align their node embeddings within the same modality and across different modalities, respectively.

Graph Matching

A Quasi-Wasserstein Loss for Learning Graph Neural Networks

1 code implementation18 Oct 2023 Minjie Cheng, Hongteng Xu

To eliminate such inconsistency, in this study we propose a novel Quasi-Wasserstein (QW) loss with the help of the optimal transport defined on graphs, leading to new learning and prediction paradigms of GNNs.

Decentralized Entropic Optimal Transport for Privacy-preserving Distributed Distribution Comparison

no code implementations28 Jan 2023 Xiangfeng Wang, Hongteng Xu, Moyi Yang

Privacy-preserving distributed distribution comparison measures the distance between the distributions whose data are scattered across different agents in a distributed system and cannot be shared among the agents.

Domain Adaptation Privacy Preserving

Text2Poster: Laying out Stylized Texts on Retrieved Images

1 code implementation6 Jan 2023 Chuhao Jin, Hongteng Xu, Ruihua Song, Zhiwu Lu

Poster generation is a significant task for a wide range of applications, which is often time-consuming and requires lots of manual editing and artistic experience.

Image Retrieval Layout Design +1

HOTNAS: Hierarchical Optimal Transport for Neural Architecture Search

no code implementations CVPR 2023 Jiechao Yang, Yong liu, Hongteng Xu

To address these issues, we propose a hierarchical optimal transport metric called HOTNN for measuring the similarity of different networks.

Bayesian Optimization Neural Architecture Search

Regularized Optimal Transport Layers for Generalized Global Pooling Operations

1 code implementation13 Dec 2022 Hongteng Xu, Minjie Cheng

Making the parameters of the ROT problem learnable, we develop a family of regularized optimal transport pooling (ROTP) layers.

Graph Classification Image Classification

Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling

no code implementations18 Sep 2022 Yang Zhang, Gengmo Zhou, Zhewei Wei, Hongteng Xu

The prediction of protein-ligand binding affinity is of great significance for discovering lead compounds in drug research.

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

1 code implementation ChemRxiv 2022 Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, Guolin Ke

Uni-Mol is composed of two models with the same SE(3)-equivariant transformer architecture: a molecular pretraining model trained by 209M molecular conformations; a pocket pretraining model trained by 3M candidate protein pocket data.

3D Geometry Prediction molecular representation +3

Hilbert Curve Projection Distance for Distribution Comparison

1 code implementation30 May 2022 Tao Li, Cheng Meng, Hongteng Xu, Jun Yu

Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling.

Efficient Approximation of Gromov-Wasserstein Distance Using Importance Sparsification

1 code implementation26 May 2022 Mengyu Li, Jun Yu, Hongteng Xu, Cheng Meng

As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for matching problems of structured data like point clouds and graphs.

valid

Interventional Multi-Instance Learning with Deconfounded Instance-Level Prediction

no code implementations20 Apr 2022 Tiancheng Lin, Hongteng Xu, Canqian Yang, Yi Xu

When applying multi-instance learning (MIL) to make predictions for bags of instances, the prediction accuracy of an instance often depends on not only the instance itself but also its context in the corresponding bag.

Causal Inference

Revisiting Global Pooling through the Lens of Optimal Transport

1 code implementation23 Jan 2022 Minjie Cheng, Hongteng Xu

Global pooling is one of the most significant operations in many machine learning models and tasks, whose implementation, however, is often empirical in practice.

Graph Classification Graph Embedding +1

Learning Graphon Autoencoders for Generative Graph Modeling

no code implementations29 May 2021 Hongteng Xu, Peilin Zhao, Junzhou Huang, Dixin Luo

A linear graphon factorization model works as a decoder, leveraging the latent representations to reconstruct the induced graphons (and the corresponding observed graphs).

Decoder

Hawkes Processes on Graphons

no code implementations4 Feb 2021 Hongteng Xu, Dixin Luo, Hongyuan Zha

We propose a novel framework for modeling multiple multivariate point processes, each with heterogeneous event types that share an underlying space and obey the same generative mechanism.

Point Processes

Learning Graphons via Structured Gromov-Wasserstein Barycenters

1 code implementation10 Dec 2020 Hongteng Xu, Dixin Luo, Lawrence Carin, Hongyuan Zha

Accordingly, given a set of graphs generated by an underlying graphon, we learn the corresponding step function as the Gromov-Wasserstein barycenter of the given graphs.

LEMMA

A Hypergradient Approach to Robust Regression without Correspondence

no code implementations ICLR 2021 Yujia Xie, Yixiu Mao, Simiao Zuo, Hongteng Xu, Xiaojing Ye, Tuo Zhao, Hongyuan Zha

Due to the combinatorial nature of the problem, most existing methods are only applicable when the sample size is small, and limited to linear regression models.

Multi-Object Tracking regression

Hierarchical Optimal Transport for Robust Multi-View Learning

no code implementations4 Jun 2020 Dixin Luo, Hongteng Xu, Lawrence Carin

Traditional multi-view learning methods often rely on two assumptions: ($i$) the samples in different views are well-aligned, and ($ii$) their representations in latent space obey the same distribution.

Clustering MULTI-VIEW LEARNING

Quaternion Product Units for Deep Learning on 3D Rotation Groups

1 code implementation CVPR 2020 Xuan Zhang, Shaofei Qin, Yi Xu, Hongteng Xu

We propose a novel quaternion product unit (QPU) to represent data on 3D rotation groups.

Graph-Driven Generative Models for Heterogeneous Multi-Task Learning

no code implementations20 Nov 2019 Wenlin Wang, Hongteng Xu, Zhe Gan, Bai Li, Guoyin Wang, Liqun Chen, Qian Yang, Wenqi Wang, Lawrence Carin

We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework.

Multi-Task Learning Type prediction

Gromov-Wasserstein Factorization Models for Graph Clustering

1 code implementation19 Nov 2019 Hongteng Xu

The model achieves a novel and flexible factorization mechanism under GW discrepancy, in which both the observed graphs and the learnable atoms can be unaligned and with different sizes.

Clustering Graph Clustering +1

Learning to Recommend from Sparse Data via Generative User Feedback

no code implementations ICLR 2020 Wenlin Wang, Hongteng Xu, Ruiyi Zhang, Wenqi Wang, Piyush Rai, Lawrence Carin

To address this, we propose a learning framework that improves collaborative filtering with a synthetic feedback loop (CF-SFL) to simulate the user feedback.

Collaborative Filtering Recommendation Systems

Zero-Shot Recognition via Optimal Transport

no code implementations20 Oct 2019 Wenlin Wang, Hongteng Xu, Guoyin Wang, Wenqi Wang, Lawrence Carin

{Specifically, we build a conditional generative model to generate features from seen-class attributes, and establish an optimal transport between the distribution of the generated features and that of the real features.}

Attribute Generalized Zero-Shot Learning

Fused Gromov-Wasserstein Alignment for Hawkes Processes

no code implementations4 Oct 2019 Dixin Luo, Hongteng Xu, Lawrence Carin

Accordingly, the learned optimal transport reflects the correspondence between the event types of these two Hawkes processes.

Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes

no code implementations20 Jun 2019 Dixin Luo, Hongteng Xu, Lawrence Carin

Instead of learning a mixture model directly from a set of event sequences drawn from different Hawkes processes, the proposed method learns the target model iteratively, which generates "easy" sequences and uses them in an adversarial and self-paced manner.

Data Augmentation

Interpretable ICD Code Embeddings with Self- and Mutual-Attention Mechanisms

no code implementations13 Jun 2019 Dixin Luo, Hongteng Xu, Lawrence Carin

The proposed method achieves clinically-interpretable embeddings of ICD codes, and outperforms state-of-the-art embedding methods in procedure recommendation.

Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

1 code implementation NeurIPS 2019 Hongteng Xu, Dixin Luo, Lawrence Carin

Using this concept, we extend our method to multi-graph partitioning and matching by learning a Gromov-Wasserstein barycenter graph for multiple observed graphs; the barycenter graph plays the role of the disconnected graph, and since it is learned, so is the clustering.

Clustering Graph Matching +1

Quaternion Convolutional Neural Networks

1 code implementation ECCV 2018 Xuanyu Zhu, Yi Xu, Hongteng Xu, Changjian Chen

Neural networks in the real domain have been studied for a long time and achieved promising results in many vision tasks for recent years.

Denoising Image Classification

Gromov-Wasserstein Learning for Graph Matching and Node Embedding

2 code implementations17 Jan 2019 Hongteng Xu, Dixin Luo, Hongyuan Zha, Lawrence Carin

A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes.

Graph Matching

PoPPy: A Point Process Toolbox Based on PyTorch

1 code implementation23 Oct 2018 Hongteng Xu

The goal of PoPPy is providing a user-friendly solution to the key points above and achieving large-scale point process-based sequential data analysis, simulation and prediction.

Point Processes

Distilled Wasserstein Learning for Word Embedding and Topic Modeling

no code implementations NeurIPS 2018 Hongteng Xu, Wenlin Wang, Wei Liu, Lawrence Carin

When learning the topic model, we leverage a distilled underlying distance matrix to update the topic distributions and smoothly calculate the corresponding optimal transports.

Mortality Prediction Word Embeddings

Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model

no code implementations5 Sep 2018 Matthew Engelhard, Hongteng Xu, Lawrence Carin, Jason A Oliver, Matthew Hallyburton, F Joseph McClernon

Health risks from cigarette smoking -- the leading cause of preventable death in the United States -- can be substantially reduced by quitting.

Learning an Inverse Tone Mapping Network with a Generative Adversarial Regularizer

no code implementations20 Apr 2018 Shiyu Ning, Hongteng Xu, Li Song, Rong Xie, Wenjun Zhang

Transferring a low-dynamic-range (LDR) image to a high-dynamic-range (HDR) image, which is the so-called inverse tone mapping (iTM), is an important imaging technique to improve visual effects of imaging devices.

inverse tone mapping Inverse-Tone-Mapping +1

Superposition-Assisted Stochastic Optimization for Hawkes Processes

no code implementations13 Feb 2018 Hongteng Xu, Xu Chen, Lawrence Carin

We consider the learning of multi-agent Hawkes processes, a model containing multiple Hawkes processes with shared endogenous impact functions and different exogenous intensities.

Sequential Recommendation Stochastic Optimization

Visually Explainable Recommendation

no code implementations31 Jan 2018 Xu Chen, Yongfeng Zhang, Hongteng Xu, Yixin Cao, Zheng Qin, Hongyuan Zha

By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner.

Explainable Recommendation Recommendation Systems

A unified framework for manifold landmarking

no code implementations25 Oct 2017 Hongteng Xu, Licheng Yu, Mark Davenport, Hongyuan Zha

Active manifold learning aims to select and label representative landmarks on a manifold from a given set of samples to improve semi-supervised manifold learning.

Benefits from Superposed Hawkes Processes

no code implementations14 Oct 2017 Hongteng Xu, Dixin Luo, Xu Chen, Lawrence Carin

The superposition of Hawkes processes is demonstrated to be beneficial for tightening the upper bound of excess risk under certain conditions, and we show the feasibility of the benefit in typical situations.

Point Processes Recommendation Systems

Learning Registered Point Processes from Idiosyncratic Observations

no code implementations ICML 2018 Hongteng Xu, Lawrence Carin, Hongyuan Zha

A parametric point process model is developed, with modeling based on the assumption that sequential observations often share latent phenomena, while also possessing idiosyncratic effects.

Point Processes

Flexible Network Binarization with Layer-wise Priority

no code implementations13 Sep 2017 Lixue Zhuang, Yi Xu, Bingbing Ni, Hongteng Xu

In this work, we reveal an important fact that binarizing different layers has a widely-varied effect on the compression ratio of network and the loss of performance.

Binarization Pedestrian Detection

THAP: A Matlab Toolkit for Learning with Hawkes Processes

1 code implementation28 Aug 2017 Hongteng Xu, Hongyuan Zha

As a powerful tool of asynchronous event sequence analysis, point processes have been studied for a long time and achieved numerous successes in different fields.

Point Processes

Learning Hawkes Processes from Short Doubly-Censored Event Sequences

1 code implementation ICML 2017 Hongteng Xu, Dixin Luo, Hongyuan Zha

Many real-world applications require robust algorithms to learn point processes based on a type of incomplete data --- the so-called short doubly-censored (SDC) event sequences.

Point Processes

A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering

1 code implementation NeurIPS 2017 Hongteng Xu, Hongyuan Zha

We propose an effective method to solve the event sequence clustering problems based on a novel Dirichlet mixture model of a special but significant type of point processes --- Hawkes process.

Bayesian Inference Clustering +1

A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories

no code implementations10 Sep 2016 Weiyao Lin, Yang Zhou, Hongteng Xu, Junchi Yan, Mingliang Xu, Jianxin Wu, Zicheng Liu

Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion pattern in a scene.

3D Action Recognition Anomaly Detection +2

Fractal Dimension Invariant Filtering and Its CNN-based Implementation

no code implementations CVPR 2017 Hongteng Xu, Junchi Yan, Nils Persson, Weiyao Lin, Hongyuan Zha

By adding a nonlinear post-processing step behind anisotropic filter banks, we demonstrate that the proposed filtering method is capable of preserving the local invariance of the fractal dimension of image.

Texture Classification

Patient Flow Prediction via Discriminative Learning of Mutually-Correcting Processes

no code implementations14 Feb 2016 Hongteng Xu, Weichang Wu, Shamim Nemati, Hongyuan Zha

By treating a sequence of transition events as a point process, we develop a novel framework for modeling patient flow through various CUs and jointly predicting patients' destination CUs and duration days.

feature selection Management

Learning Granger Causality for Hawkes Processes

no code implementations14 Feb 2016 Hongteng Xu, Mehrdad Farajtabar, Hongyuan Zha

In this paper, we propose an effective method, learning Granger causality, for a special but significant type of point processes --- Hawkes process.

Clustering Point Processes

Unsupervised Trajectory Clustering via Adaptive Multi-Kernel-Based Shrinkage

no code implementations ICCV 2015 Hongteng Xu, Yang Zhou, Weiyao Lin, Hongyuan Zha

Facing to the challenges of trajectory clustering, e. g., large variations within a cluster and ambiguities across clusters, we first introduce an adaptive multi-kernel-based estimation process to estimate the `shrunk' positions and speeds of trajectories' points.

Anomaly Detection Clustering +1

Manifold Based Dynamic Texture Synthesis from Extremely Few Samples

no code implementations CVPR 2014 Hongteng Xu, Hongyuan Zha, Mark A. Davenport

In this paper, we present a novel method to synthesize dynamic texture sequences from extremely few samples, e. g., merely two possibly disparate frames, leveraging both Markov Random Fields (MRFs) and manifold learning.

Texture Synthesis

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