Search Results for author: Tong Zhao

Found 62 papers, 31 papers with code

RoadBEV: Road Surface Reconstruction in Bird's Eye View

1 code implementation9 Apr 2024 Tong Zhao, Lei Yang, Yichen Xie, Mingyu Ding, Masayoshi Tomizuka, Yintao Wei

This paper uniformly proposes two simple yet effective models for road elevation reconstruction in BEV named RoadBEV-mono and RoadBEV-stereo, which estimate road elevation with monocular and stereo images, respectively.

Autonomous Driving Monocular Depth Estimation +2

Improving Out-of-Vocabulary Handling in Recommendation Systems

no code implementations27 Mar 2024 William Shiao, Mingxuan Ju, Zhichun Guo, Xin Chen, Evangelos Papalexakis, Tong Zhao, Neil Shah, Yozen Liu

This work focuses on a complementary problem: recommending new users and items unseen (out-of-vocabulary, or OOV) at training time.

Recommendation Systems

How Does Message Passing Improve Collaborative Filtering?

no code implementations27 Mar 2024 Mingxuan Ju, William Shiao, Zhichun Guo, Yanfang Ye, Yozen Liu, Neil Shah, Tong Zhao

A branch of research enhances CF methods by message passing used in graph neural networks, due to its strong capabilities of extracting knowledge from graph-structured data, like user-item bipartite graphs that naturally exist in CF.

Collaborative Filtering Recommendation Systems +1

Node Duplication Improves Cold-start Link Prediction

no code implementations15 Feb 2024 Zhichun Guo, Tong Zhao, Yozen Liu, Kaiwen Dong, William Shiao, Neil Shah, Nitesh V. Chawla

Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks.

Link Prediction Recommendation Systems

Depth-aware Volume Attention for Texture-less Stereo Matching

1 code implementation14 Feb 2024 Tong Zhao, Mingyu Ding, Wei Zhan, Masayoshi Tomizuka, Yintao Wei

Furthermore, we propose a more rigorous evaluation metric that considers depth-wise relative error, providing comprehensive evaluations for universal stereo matching and depth estimation models.

Depth Estimation Stereo Matching

LLaGA: Large Language and Graph Assistant

2 code implementations13 Feb 2024 Runjin Chen, Tong Zhao, Ajay Jaiswal, Neil Shah, Zhangyang Wang

Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis.

Graph Foundation Models

no code implementations3 Feb 2024 Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Mikhail Galkin, Jiliang Tang

Graph Foundation Model (GFM) is a new trending research topic in the graph domain, aiming to develop a graph model capable of generalizing across different graphs and tasks.

Neural Scaling Laws on Graphs

no code implementations3 Feb 2024 Jingzhe Liu, Haitao Mao, Zhikai Chen, Tong Zhao, Neil Shah, Jiliang Tang

In this work, we delve into neural scaling laws on graphs from both model and data perspectives.

Graph Classification Link Prediction +1

Graph Transformers for Large Graphs

1 code implementation18 Dec 2023 Vijay Prakash Dwivedi, Yozen Liu, Anh Tuan Luu, Xavier Bresson, Neil Shah, Tong Zhao

As such, a key innovation of this work lies in the creation of a fast neighborhood sampling technique coupled with a local attention mechanism that encompasses a 4-hop reception field, but achieved through just 2-hop operations.

Graph Learning Graph Property Prediction +3

CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models

1 code implementation6 Dec 2023 Hailin Zhang, Zirui Liu, Boxuan Chen, Yikai Zhao, Tong Zhao, Tong Yang, Bin Cui

Guided by our design philosophy, we further propose a multi-level hash embedding framework to optimize the embedding tables of non-hot features.

Feature Importance Philosophy

Density-Guided Dense Pseudo Label Selection For Semi-supervised Oriented Object Detection

1 code implementation21 Nov 2023 Tong Zhao, Qiang Fang, Shuohao Shi, Xin Xu

Therefore, we propose Density-Guided Dense Pseudo Label Selection (DDPLS) for semi-supervised oriented object detection.

Object object-detection +4

A Topological Perspective on Demystifying GNN-Based Link Prediction Performance

1 code implementation6 Oct 2023 Yu Wang, Tong Zhao, Yuying Zhao, Yunchao Liu, Xueqi Cheng, Neil Shah, Tyler Derr

Despite the widespread belief that low-degree nodes exhibit poorer LP performance, our empirical findings provide nuances to this viewpoint and prompt us to propose a better metric, Topological Concentration (TC), based on the intersection of the local subgraph of each node with the ones of its neighbors.

Link Prediction

RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving

no code implementations3 Oct 2023 Tong Zhao, Chenfeng Xu, Mingyu Ding, Masayoshi Tomizuka, Wei Zhan, Yintao Wei

This paper addresses the growing demands for safety and comfort in intelligent robot systems, particularly autonomous vehicles, where road conditions play a pivotal role in overall driving performance.

Autonomous Driving Depth Estimation +3

Revisiting Link Prediction: A Data Perspective

1 code implementation1 Oct 2023 Haitao Mao, Juanhui Li, Harry Shomer, Bingheng Li, Wenqi Fan, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang

We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity.

Link Prediction

MRA-GNN: Minutiae Relation-Aware Model over Graph Neural Network for Fingerprint Embedding

no code implementations31 Jul 2023 Yapeng Su, Tong Zhao, ZiCheng Zhang

However, previous works including CNN-based and Transformer-based approaches fail to exploit the nonstructural data, such as topology and correlation in fingerprints, which is essential to facilitate the identifiability and robustness of embedding.

Descriptive Graph Embedding +1

A Benchmark for Understanding Dialogue Safety in Mental Health Support

1 code implementation31 Jul 2023 Huachuan Qiu, Tong Zhao, Anqi Li, Shuai Zhang, Hongliang He, Zhenzhong Lan

Our study reveals that ChatGPT struggles to detect safety categories with detailed safety definitions in a zero- and few-shot paradigm, whereas the fine-tuned model proves to be more suitable.

CARL-G: Clustering-Accelerated Representation Learning on Graphs

no code implementations12 Jun 2023 William Shiao, Uday Singh Saini, Yozen Liu, Tong Zhao, Neil Shah, Evangelos E. Papalexakis

CARL-G is adaptable to different clustering methods and CVIs, and we show that with the right choice of clustering method and CVI, CARL-G outperforms node classification baselines on 4/5 datasets with up to a 79x training speedup compared to the best-performing baseline.

Clustering Contrastive Learning +4

Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?

1 code implementation NeurIPS 2023 Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang

Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs.

Node Classification

Semi-Supervised Graph Imbalanced Regression

1 code implementation20 May 2023 Gang Liu, Tong Zhao, Eric Inae, Tengfei Luo, Meng Jiang

The training data balance is achieved by (1) pseudo-labeling more graphs for under-represented labels with a novel regression confidence measurement and (2) augmenting graph examples in latent space for remaining rare labels after data balancing with pseudo-labels.

Graph Regression regression

Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation

no code implementations28 Mar 2023 Tong Zhao, Andrea Tagliabue, Jonathan P. How

We tailor our approach to the task of learning an adaptive position and attitude control policy to track trajectories under challenging disturbances on a multirotor.

Data Augmentation Imitation Learning +2

Data-Centric Learning from Unlabeled Graphs with Diffusion Model

1 code implementation17 Mar 2023 Gang Liu, Eric Inae, Tong Zhao, Jiaxin Xu, Tengfei Luo, Meng Jiang

A conventional approach is training a model with the unlabeled graphs on self-supervised tasks and then fine-tuning the model on the prediction tasks.

Denoising Graph Property Prediction +2

Link Prediction with Non-Contrastive Learning

1 code implementation25 Nov 2022 William Shiao, Zhichun Guo, Tong Zhao, Evangelos E. Papalexakis, Yozen Liu, Neil Shah

In this work, we extensively evaluate the performance of existing non-contrastive methods for link prediction in both transductive and inductive settings.

Contrastive Learning Link Prediction +2

Linkless Link Prediction via Relational Distillation

no code implementations11 Oct 2022 Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V. Chawla, Neil Shah, Tong Zhao

In this work, to combine the advantages of GNNs and MLPs, we start with exploring direct knowledge distillation (KD) methods for link prediction, i. e., predicted logit-based matching and node representation-based matching.

Knowledge Distillation Link Prediction +1

Empowering Graph Representation Learning with Test-Time Graph Transformation

1 code implementation7 Oct 2022 Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, Neil Shah

In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance.

Drug Discovery Graph Representation Learning +1

Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering

1 code implementation6 Oct 2022 Mingxuan Ju, Wenhao Yu, Tong Zhao, Chuxu Zhang, Yanfang Ye

In light of this, we propose a novel knowledge Graph enhanced passage reader, namely Grape, to improve the reader performance for open-domain QA.

Entity Embeddings Open-Domain Question Answering

Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization

1 code implementation5 Oct 2022 Mingxuan Ju, Tong Zhao, Qianlong Wen, Wenhao Yu, Neil Shah, Yanfang Ye, Chuxu Zhang

Besides, we observe that learning from multiple philosophies enhances not only the task generalization but also the single task performances, demonstrating that PARETOGNN achieves better task generalization via the disjoint yet complementary knowledge learned from different philosophies.

Link Prediction Node Classification +4

Flashlight: Scalable Link Prediction with Effective Decoders

no code implementations17 Sep 2022 Yiwei Wang, Bryan Hooi, Yozen Liu, Tong Zhao, Zhichun Guo, Neil Shah

However, HadamardMLP lacks the scalability for retrieving top scoring neighbors on large graphs, since to the best of our knowledge, there does not exist an algorithm to retrieve the top scoring neighbors for HadamardMLP decoders in sublinear complexity.

Graph Learning Link Prediction

Graph Rationalization with Environment-based Augmentations

1 code implementation6 Jun 2022 Gang Liu, Tong Zhao, Jiaxin Xu, Tengfei Luo, Meng Jiang

Rationale is defined as a subset of input features that best explains or supports the prediction by machine learning models.

Graph Regression Property Prediction +1

Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback

no code implementations18 Mar 2022 Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis

We develop and investigate a personalizable deep metric model that captures both the internal contents of items and how they were interacted with by users.

Metric Learning Recommendation Systems

Graph Data Augmentation for Graph Machine Learning: A Survey

1 code implementation17 Feb 2022 Tong Zhao, Wei Jin, Yozen Liu, Yingheng Wang, Gang Liu, Stephan Günnemann, Neil Shah, Meng Jiang

Overall, our work aims to clarify the landscape of existing literature in graph data augmentation and motivates additional work in this area, providing a helpful resource for researchers and practitioners in the broader graph machine learning domain.

BIG-bench Machine Learning Data Augmentation

RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph

no code implementations12 Feb 2022 Ruijie Wang, Zheng Li, Danqing Zhang, Qingyu Yin, Tong Zhao, Bing Yin, Tarek Abdelzaher

And meanwhile, RETE autoregressively accumulates retrieval-enhanced user representations from each time step, to capture evolutionary patterns for joint query and product prediction.

Product Recommendation Retrieval

Formal Certification Methods for Automated Vehicle Safety Assessment

no code implementations6 Feb 2022 Tong Zhao, Ekim Yurtsever, Joel Paulson, Giorgio Rizzoni

In this work, we provide both an overview of the safety verification, validation and certification process, as well as review formal safety techniques that are best suited to AV applications.

Autonomous Driving

Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence

1 code implementation1 Sep 2021 Wennan Chang, Pengtao Dang, Changlin Wan, Xiaoyu Lu, Yue Fang, Tong Zhao, Yong Zang, Bo Li, Chi Zhang, Sha Cao

Compared with existing spatial regression models, our proposed model assumes the existence a few distinct regression models that are estimated based on observations that exhibit similar response-predictor relationships.


Graph-based Multilingual Product Retrieval in E-commerce Search

no code implementations NAACL 2021 Hanqing Lu, Youna Hu, Tong Zhao, Tony Wu, Yiwei Song, Bing Yin

Nowadays, with many e-commerce platforms conducting global business, e-commerce search systems are required to handle product retrieval under multilingual scenarios.

Graph Attention Retrieval

CNNATT: Deep EEG & fNIRS Real-Time Decoding of bimanual forces

no code implementations9 Mar 2021 Pablo Ortega, Tong Zhao, Aldo Faisal

Non-invasive cortical neural interfaces have only achieved modest performance in cortical decoding of limb movements and their forces, compared to invasive brain-computer interfaces (BCIs).

Decoder EEG


no code implementations1 Jan 2021 Pengtao Dang, Wennan Chang, Haiqi Zhu, Changlin Wan, Tong Zhao, Tingbo Guo, Paul Salama, Sha Cao, Chi Zhang

In this work, we first organize the general MLLRR problem into three subproblems based on different low rank properties , and we argue that most of existing efforts focus on only one category, which leaves the other two unsolved.

Recommendation Systems

Action Sequence Augmentation for Early Graph-based Anomaly Detection

1 code implementation20 Oct 2020 Tong Zhao, Bo Ni, Wenhao Yu, Zhichun Guo, Neil Shah, Meng Jiang

With Eland, anomaly detection performance at an earlier stage is better than non-augmented methods that need significantly more observed data by up to 15% on the Area under the ROC curve.

Data Augmentation Graph Anomaly Detection

A Unified View on Graph Neural Networks as Graph Signal Denoising

1 code implementation5 Oct 2020 Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, Neil Shah

In this work, we establish mathematically that the aggregation processes in a group of representative GNN models including GCN, GAT, PPNP, and APPNP can be regarded as (approximately) solving a graph denoising problem with a smoothness assumption.


Neural Time-Dependent Partial Differential Equation

no code implementations28 Sep 2020 Yihao Hu, Tong Zhao, Zhiliang Xu, Lizhen Lin

Inspired by the traditional finite difference and finite elements methods and emerging advancements in machine learning, we propose a sequence-to-sequence learning (Seq2Seq) framework called Neural-PDE, which allows one to automatically learn governing rules of any time-dependent PDE system from existing data by using a bidirectional LSTM encoder, and predict the solutions in next $n$ time steps.

Federated Dynamic GNN with Secure Aggregation

no code implementations15 Sep 2020 Meng Jiang, Taeho Jung, Ryan Karl, Tong Zhao

Given video data from multiple personal devices or street cameras, can we exploit the structural and dynamic information to learn dynamic representation of objects for applications such as distributed surveillance, without storing data at a central server that leads to a violation of user privacy?

Federated Learning

Neural-PDE: A RNN based neural network for solving time dependent PDEs

1 code implementation8 Sep 2020 Yihao Hu, Tong Zhao, Shixin Xu, Zhiliang Xu, Lizhen Lin

Partial differential equations (PDEs) play a crucial role in studying a vast number of problems in science and engineering.

BIG-bench Machine Learning

Geometric All-Way Boolean Tensor Decomposition

1 code implementation NeurIPS 2020 Changlin Wan, Wennan Chang, Tong Zhao, Sha Cao, Chi Zhang

Boolean tensor has been broadly utilized in representing high dimensional logical data collected on spatial, temporal and/or other relational domains.

Tensor Decomposition

Denoising individual bias for a fairer binary submatrix detection

1 code implementation31 Jul 2020 Changlin Wan, Wennan Chang, Tong Zhao, Sha Cao, Chi Zhang

Low rank representation of binary matrix is powerful in disentangling sparse individual-attribute associations, and has received wide applications.

Attribute Clustering +2

MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

no code implementations22 Jun 2020 Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos

MultiImport is a latent variable model that captures the relation between node importance and input signals, and effectively learns from multiple signals with potential conflicts.

Octet: Online Catalog Taxonomy Enrichment with Self-Supervision

no code implementations18 Jun 2020 Yuning Mao, Tong Zhao, Andrey Kan, Chenwei Zhang, Xin Luna Dong, Christos Faloutsos, Jiawei Han

We propose to distantly train a sequence labeling model for term extraction and employ graph neural networks (GNNs) to capture the taxonomy structure as well as the query-item-taxonomy interactions for term attachment.

Term Extraction

Data Augmentation for Graph Neural Networks

2 code implementations11 Jun 2020 Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, Neil Shah

Our work shows that neural edge predictors can effectively encode class-homophilic structure to promote intra-class edges and demote inter-class edges in given graph structure, and our main contribution introduces the GAug graph data augmentation framework, which leverages these insights to improve performance in GNN-based node classification via edge prediction.

Data Augmentation General Classification +1

A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction

no code implementations WS 2020 Yang Zhou, Tong Zhao, Meng Jiang

Textual patterns (e. g., Country's president Person) are specified and/or generated for extracting factual information from unstructured data.

TAG Text Generation

Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text

no code implementations IJCNLP 2019 Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh Chawla, Meng Jiang

In this work, we propose a new sequence labeling framework (as well as a new tag schema) to jointly extract the fact and condition tuples from statement sentences.

TAG valid

Fast And Efficient Boolean Matrix Factorization By Geometric Segmentation

no code implementations9 Sep 2019 Changlin Wan, Wennan Chang, Tong Zhao, Mengya Li, Sha Cao, Chi Zhang

Boolean matrix factorization (BMF) aims to find an approximation of a binary matrix as the Boolean product of two low rank Boolean matrices, which could generate vast amount of information for the patterns of relationships between the features and samples.

Computational Efficiency Denoising

Saliency Detection with Spaces of Background-based Distribution

no code implementations17 Mar 2016 Tong Zhao, Lin Li, Xinghao Ding, Yue Huang, Delu Zeng

In this letter, an effective image saliency detection method is proposed by constructing some novel spaces to model the background and redefine the distance of the salient patches away from the background.

Saliency Detection

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