1 code implementation • 9 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.
no code implementations • 27 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.
no code implementations • 27 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.
no code implementations • 15 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.
1 code implementation • 14 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.
2 code implementations • 13 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.
no code implementations • 3 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.
no code implementations • 3 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.
1 code implementation • 18 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.
1 code implementation • 6 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.
no code implementations • 21 Nov 2023 • Tong Zhao, Qiang Fang, Shuohao Shi, Xin Xu
However, for the multi-oriented and dense objects that are common in aerial scenes, existing dense pseudo-label selection methods are inefficient and impede the performance in semi-supervised oriented object detection.
1 code implementation • 6 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.
no code implementations • 3 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.
1 code implementation • 1 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.
1 code implementation • 31 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.
no code implementations • 31 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.
no code implementations • 12 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.
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.
1 code implementation • 20 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.
no code implementations • 28 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.
1 code implementation • 17 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.
1 code implementation • 25 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.
no code implementations • 11 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.
1 code implementation • 7 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.
1 code implementation • 6 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.
1 code implementation • 5 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.
2 code implementations • 30 Sep 2022 • Xiaotian Han, Tong Zhao, Yozen Liu, Xia Hu, Neil Shah
Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming.
no code implementations • 17 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.
1 code implementation • 6 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.
Ranked #1 on Graph Regression on GlassTemp
1 code implementation • Findings (NAACL) 2022 • Yifan Gao, Qingyu Yin, Zheng Li, Rui Meng, Tong Zhao, Bing Yin, Irwin King, Michael R. Lyu
Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text.
no code implementations • 4 Apr 2022 • Jiacheng Li, Tong Zhao, Jin Li, Jim Chan, Christos Faloutsos, George Karypis, Soo-Min Pantel, Julian McAuley
We propose to model user dynamics from shopping intents and interacted items simultaneously.
no code implementations • 18 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.
1 code implementation • NAACL (DLG4NLP) 2022 • Wenhao Yu, Chenguang Zhu, Lianhui Qin, Zhihan Zhang, Tong Zhao, Meng Jiang
A set of knowledge experts seek diverse reasoning on KG to encourage various generation outputs.
1 code implementation • 17 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.
no code implementations • 12 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.
no code implementations • 6 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.
no code implementations • EMNLP 2021 • Liqiang Xiao, Jun Ma2, Xin Luna Dong, Pascual Martinez-Gomez, Nasser Zalmout, Wei Chen, Tong Zhao, Hao He, Yaohui Jin
Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers.
1 code implementation • 1 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.
1 code implementation • NeurIPS 2021 • Tong Zhao, Gang Liu, Daheng Wang, Wenhao Yu, Meng Jiang
However, the causal relationship between the two variables was largely ignored for learning to predict links on a graph.
Ranked #1 on Link Property Prediction on ogbl-ddi
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.
1 code implementation • EMNLP 2021 • Wenhao Yu, Chenguang Zhu, Tong Zhao, Zhichun Guo, Meng Jiang
Generating paragraphs of diverse contents is important in many applications.
no code implementations • 9 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).
no code implementations • 1 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.
1 code implementation • 20 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.
1 code implementation • 5 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.
no code implementations • 28 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.
no code implementations • 15 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?
1 code implementation • 8 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.
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.
1 code implementation • 31 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.
1 code implementation • 25 Jul 2020 • Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang
In this work, we present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence.
no code implementations • 24 Jun 2020 • Xin Luna Dong, Xiang He, Andrey Kan, Xi-An Li, Yan Liang, Jun Ma, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, Saurabh Deshpande, Alexandre Michetti Manduca, Jay Ren, Surender Pal Singh, Fan Xiao, Haw-Shiuan Chang, Giannis Karamanolakis, Yuning Mao, Yaqing Wang, Christos Faloutsos, Andrew McCallum, Jiawei Han
Can one build a knowledge graph (KG) for all products in the world?
no code implementations • 22 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.
no code implementations • 18 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.
no code implementations • 17 Jun 2020 • Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang
Noun phrases and relational phrases in Open Knowledge Bases are often not canonical, leading to redundant and ambiguous facts.
2 code implementations • 11 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.
Ranked #1 on Node Classification on Flickr
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.
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.
no code implementations • 9 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.
no code implementations • 26 Jun 2019 • Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang
Conditions are essential in the statements of biological literature.
no code implementations • 21 May 2019 • Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos
How can we estimate the importance of nodes in a knowledge graph (KG)?
no code implementations • 17 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.