Search Results for author:
Found 62 papers, 30 papers with code
RoadBEV: Road Surface Reconstruction in Bird's Eye View
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.
How Does Message Passing Improve Collaborative Filtering?
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.
Improving Out-of-Vocabulary Handling in Recommendation Systems
This work focuses on a complementary problem: recommending new users and items unseen (out-of-vocabulary, or OOV) at training time.
Node Duplication Improves Cold-start Link Prediction
Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks.
Depth-aware Volume Attention for Texture-less Stereo Matching
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.
LLaGA: Large Language and Graph Assistant
Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis.
Neural Scaling Laws on Graphs
In this work, we delve into neural scaling laws on graphs from both model and data perspectives.
Graph Foundation Models
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.
Graph Transformers for Large Graphs
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.
CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models
Guided by our design philosophy, we further propose a multi-level hash embedding framework to optimize the embedding tables of non-hot features.
Adaptive Dense Pseudo Label Selection for Semi-supervised Oriented Object Detection
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.
A Topological Perspective on Demystifying GNN-Based Link Prediction Performance
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.
RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving
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.
Revisiting Link Prediction: A Data Perspective
We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity.
A Benchmark for Understanding Dialogue Safety in Mental Health Support
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.
MRA-GNN: Minutiae Relation-Aware Model over Graph Neural Network for Fingerprint Embedding
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.
CARL-G: Clustering-Accelerated Representation Learning on Graphs
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.
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
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.
Semi-Supervised Graph Imbalanced Regression
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.
Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation
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-Centric Learning from Unlabeled Graphs with Diffusion Model
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.
Link Prediction with Non-Contrastive Learning
In this work, we extensively evaluate the performance of existing non-contrastive methods for link prediction in both transductive and inductive settings.
Linkless Link Prediction via Relational Distillation
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.
Empowering Graph Representation Learning with Test-Time Graph Transformation
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.
Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering
In light of this, we propose a novel knowledge Graph enhanced passage reader, namely Grape, to improve the reader performance for open-domain QA.
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
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.
MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization
Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming.
Flashlight: Scalable Link Prediction with Effective Decoders
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 Rationalization with Environment-based Augmentations
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
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training
Keyphrase generation is the task of automatically predicting keyphrases given a piece of long text.
Coarse-to-Fine Sparse Sequential Recommendation
We propose to model user dynamics from shopping intents and interacted items simultaneously.
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback
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.
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts
A set of knowledge experts seek diverse reasoning on KG to encourage various generation outputs.
Graph Data Augmentation for Graph Machine Learning: A Survey
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.
RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph
And meanwhile, RETE autoregressively accumulates retrieval-enhanced user representations from each time step, to capture evolutionary patterns for joint query and product prediction.
Formal Certification Methods for Automated Vehicle Safety Assessment
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.
End-to-End Conversational Search for Online Shopping with Utterance Transfer
Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers.
Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence
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.
Learning from Counterfactual Links for Link Prediction
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
Graph-based Multilingual Product Retrieval in E-commerce Search
Nowadays, with many e-commerce platforms conducting global business, e-commerce search systems are required to handle product retrieval under multilingual scenarios.
Sentence-Permuted Paragraph Generation
Generating paragraphs of diverse contents is important in many applications.
CNNATT: Deep EEG & fNIRS Real-Time Decoding of bimanual forces
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).
RETHINKING LOCAL LOW RANK MATRIX DETECTION:A MULTIPLE-FILTER BASED NEURAL NETWORK FRAMEWORK
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.
Action Sequence Augmentation for Early Graph-based Anomaly Detection
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.
A Unified View on Graph Neural Networks as Graph Signal Denoising
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
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
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?
Neural-PDE: A RNN based neural network for solving time dependent PDEs
Partial differential equations (PDEs) play a crucial role in studying a vast number of problems in science and engineering.
Geometric All-Way Boolean Tensor Decomposition
Boolean tensor has been broadly utilized in representing high dimensional logical data collected on spatial, temporal and/or other relational domains.
Denoising individual bias for a fairer binary submatrix detection
Low rank representation of binary matrix is powerful in disentangling sparse individual-attribute associations, and has received wide applications.
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs
In this work, we present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence.
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Can one build a knowledge graph (KG) for all products in the world?
MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals
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
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.
Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network
Noun phrases and relational phrases in Open Knowledge Bases are often not canonical, leading to redundant and ambiguous facts.
Data Augmentation for Graph Neural Networks
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
A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction
Textual patterns (e. g., Country's president Person) are specified and/or generated for extracting factual information from unstructured data.
Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text
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.
Fast And Efficient Boolean Matrix Factorization By Geometric Segmentation
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.
Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements
Conditions are essential in the statements of biological literature.
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
How can we estimate the importance of nodes in a knowledge graph (KG)?
Saliency Detection with Spaces of Background-based Distribution
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.
