Search Results for author:
Found 22 papers, 12 papers with code
PGL at TextGraphs 2020 Shared Task: Explanation Regeneration using Language and Graph Learning Methods
To address this problem, we use a pre-trained language model to recall the top-K relevant explanations for each question.
HGAMN: Heterogeneous Graph Attention Matching Network for Multilingual POI Retrieval at Baidu Maps
To mitigate challenge \#2, we construct edges between POI and query nodes based on the co-occurrences between queries and POIs, where queries in different languages and formulations can be aggregated for individual POIs.
Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
Furthermore, these methods cannot learn arbitrary valid heterogeneous graph filters within the spectral domain, which have limited expressiveness.
Ranked #6 on
Node Property Prediction
on ogbn-mag
Label Information Enhanced Fraud Detection against Low Homophily in Graphs
Then, we combine the group aggregation and the learnable encodings into a Transformer encoder to capture the semantic information.
Layout-aware Webpage Quality Assessment
To assess webpage quality from complex DOM tree data, we propose a graph neural network (GNN) based method that extracts rich layout-aware information that implies webpage quality in an end-to-end manner.
Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods
Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the help of smart contracts.
Demystifying Bitcoin Address Behavior via Graph Neural Networks
Finally, we aggregate all graph embeddings of an address into the address-level representation, and engage in a classification model to give the address behavior classification.
ERNIE-Layout: Layout Knowledge Enhanced Pre-training for Visually-rich Document Understanding
Recent years have witnessed the rise and success of pre-training techniques in visually-rich document understanding.
Ranked #2 on
Semantic entity labeling
on FUNSD
Text detection and recognition based on a lensless imaging system
Lensless cameras are characterized by several advantages (e. g., miniaturization, ease of manufacture, and low cost) as compared with conventional cameras.
ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding
At first, a document graph is proposed to model complex relationships among multi-grained multimodal elements, in which salient visual regions are detected by a cluster-based method.
DuETA: Traffic Congestion Propagation Pattern Modeling via Efficient Graph Learning for ETA Prediction at Baidu Maps
In this work, we focus on modeling traffic congestion propagation patterns to improve ETA performance.
ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval
Our method 1) introduces a self on-the-fly distillation method that can effectively distill late interaction (i. e., ColBERT) to vanilla dual-encoder, and 2) incorporates a cascade distillation process to further improve the performance with a cross-encoder teacher.
Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning
Notably, it averagely brings about 10% relative improvement to triplet-based embedding methods on OGBL-WikiKG2 and takes 5%-83% time to achieve comparable results as the state-of-the-art GC-OTE.
ERNIE-GeoL: A Geography-and-Language Pre-trained Model and its Applications in Baidu Maps
One of the main reasons for this plateau is the lack of readily available geographic knowledge in generic PTMs.
Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems
In recent years, owing to the outstanding performance in graph representation learning, graph neural network (GNN) techniques have gained considerable interests in many real-world scenarios, such as recommender systems and social networks.
NOTE: Solution for KDD-CUP 2021 WikiKG90M-LSC
In addition to the representations, we also use various statistical probabilities among the head entities, the relations and the tail entities for the final prediction.
LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction
In this report, we (SuperHelix team) present our solution to KDD Cup 2021-PCQM4M-LSC, a large-scale quantum chemistry dataset on predicting HOMO-LUMO gap of molecules.
Molecular Representation Learning by Leveraging Chemical Information
As graph neural networks have achieved great success in many domains, some studies apply graph neural networks to molecular property prediction and regard each molecule as a graph.
Ranked #6 on
Graph Property Prediction
on ogbg-molhiv
kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification
Code switching is a linguistic phenomenon that may occur within a multilingual setting where speakers share more than one language.
Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
Graph neural network (GNN) and label propagation algorithm (LPA) are both message passing algorithms, which have achieved superior performance in semi-supervised classification.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
ERNIE at SemEval-2020 Task 10: Learning Word Emphasis Selection by Pre-trained Language Model
This paper describes the system designed by ERNIE Team which achieved the first place in SemEval-2020 Task 10: Emphasis Selection For Written Text in Visual Media.
Chinese/English mixed Character Segmentation as Semantic Segmentation
We take advantage of the successful architecture called fully convolutional networks (FCN) in the field of semantic segmentation.
