research
∙
09/08/2023
Unleashing the Power of Graph Learning through LLM-based Autonomous Agents
Graph structured data are widely existed and applied in the real-world a...
share
research
∙
06/15/2023
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation
Although powerful graph neural networks (GNNs) have boosted numerous rea...
research
∙
06/13/2023
Automated 3D Pre-Training for Molecular Property Prediction
Molecular property prediction is an important problem in drug discovery ...
research
∙
06/06/2023
ColdNAS: Search to Modulate for User Cold-Start Recommendation
Making personalized recommendation for cold-start users, who only have a...
research
∙
03/22/2023
Logical Expressiveness of Graph Neural Network for Knowledge Graph Reasoning
Graph Neural Networks (GNNs) have been recently introduced to learn from...
research
∙
03/01/2023
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning
Privacy and security concerns in real-world applications have led to the...
research
∙
02/17/2023
Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification
In recent years, Graph Neural Networks (GNNs) have been popular in the g...
research
∙
02/10/2023
Improving Zero-Shot Coordination Performance Based on Policy Similarity
Over these years, multi-agent reinforcement learning has achieved remark...
research
∙
11/20/2022
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach
In recent years, Graph Neural Networks (GNNs) have been popular in graph...
research
∙
10/30/2022
Search to Pass Messages for Temporal Knowledge Graph Completion
Completing missing facts is a fundamental task for temporal knowledge gr...
research
∙
09/27/2022
Searching a High-Performance Feature Extractor for Text Recognition Network
Feature extractor plays a critical role in text recognition (TR), but cu...
research
∙
08/14/2022
DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction
Spatiotemporal activity prediction, aiming to predict user activities at...
research
∙
07/24/2022
AutoWeird: Weird Translational Scoring Function Identified by Random Search
Scoring function (SF) measures the plausibility of triplets in knowledge...
research
∙
07/13/2022
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search
Aiming at two molecular graph datasets and one protein association subgr...
research
∙
05/30/2022
Learning Adaptive Propagation for Knowledge Graph Reasoning
Due to the success of Graph Neural Networks (GNNs) in learning from grap...
research
∙
05/06/2022
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
Nonconvex regularization has been popularly used in low-rank matrix lear...
research
∙
05/05/2022
KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning
While hyper-parameters (HPs) are important for knowledge graph (KG) lear...
research
∙
04/06/2022
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020
Graph structured data is ubiquitous in daily life and scientific areas a...
research
∙
12/15/2021
LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization
Federated optimization (FedOpt), which targets at collaboratively traini...
research
∙
10/30/2021
Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification
Short text classification is a fundamental task in natural language proc...
research
∙
08/24/2021
Pooling Architecture Search for Graph Classification
Graph classification is an important problem with applications across ma...
research
∙
08/20/2021
TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction
Tabular data prediction (TDP) is one of the most popular industrial appl...
research
∙
08/13/2021
Knowledge Graph Reasoning with Relational Directed Graph
Reasoning on the knowledge graph (KG) aims to infer new facts from exist...
research
∙
07/01/2021
AutoSF+: Towards Automatic Scoring Function Design for Knowledge Graph Embedding
Scoring functions, which measure the plausibility of triples, have becom...
research
∙
06/14/2021
Efficient Data-specific Model Search for Collaborative Filtering
Collaborative filtering (CF), as a fundamental approach for recommender ...
research
∙
04/22/2021
Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding
The scoring function, which measures the plausibility of triplets in kno...
research
∙
04/21/2021
Searching to Sparsify Tensor Decomposition for N-ary Relational Data
Tensor, an extension of the vector and matrix to the multi-dimensional c...
research
∙
04/20/2021
Role-Aware Modeling for N-ary Relational Knowledge Bases
N-ary relational knowledge bases (KBs) represent knowledge with binary a...
research
∙
04/14/2021
Search to aggregate neighborhood for graph neural network
Recent years have witnessed the popularity and success of graph neural n...
research
∙
01/04/2021
Tensorizing Subgraph Search in the Supernet
Recently, a special kind of graph, i.e., supernet, which allows two node...
research
∙
11/16/2020
Decoupling Representation and Classifier for Noisy Label Learning
Since convolutional neural networks (ConvNets) can easily memorize noisy...
research
∙
11/09/2020
A Survey of Label-noise Representation Learning: Past, Present and Future
Classical machine learning implicitly assumes that labels of the trainin...
research
∙
11/09/2020
Graph Neural Network with Automorphic Equivalence Filters
Graph neural network (GNN) has recently been established as an effective...
research
∙
10/24/2020
Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration
Non-local low-rank tensor approximation has been developed as a state-of...
research
∙
10/24/2020
Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding
Negative sampling, which samples negative triplets from non-observed one...
research
∙
09/07/2020
Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering
Negative sampling approaches are prevalent in implicit collaborative fil...
research
∙
08/26/2020
Simplifying Architecture Search for Graph Neural Network
Recent years have witnessed the popularity of Graph Neural Networks (GNN...
research
∙
08/14/2020
Efficient Low-Rank Matrix Learning by Factorizable Nonconvex Regularization
Matrix learning is at the core of many machine learning problems. To enc...
research
∙
07/08/2020
Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases
With the rapid development of knowledge bases (KBs), link prediction tas...
research
∙
03/14/2020
Efficient Backbone Search for Scene Text Recognition
Scene text recognition (STR) is very challenging due to the diversity of...
research
∙
11/17/2019
Neural Recurrent Structure Search for Knowledge Graph Embedding
Knowledge graph (KG) embedding is a fundamental problem in mining relati...
research
∙
11/06/2019
Searching to Exploit Memorization Effect in Learning from Corrupted Labels
Sample-selection approaches, which attempt to pick up clean instances fr...
research
∙
06/28/2019
Searching for Interaction Functions in Collaborative Filtering
Interaction function (IFC), which captures interactions among items and ...
research
∙
05/30/2019
Differentiable Neural Architecture Search via Proximal Iterations
Neural architecture search (NAS) recently attracts much research attenti...
research
∙
05/12/2019
Robust Learning from Noisy Side-information by Semidefinite Programming
Robustness recently becomes one of the major concerns among machine lear...
research
∙
04/26/2019
AutoKGE: Searching Scoring Functions for Knowledge Graph Embedding
Knowledge graph embedding (KGE) aims to find low dimensional vector repr...
research
∙
04/10/2019
Few-shot Learning: A Survey
The quest of `can machines think' and `can machines do what human do' ar...
research
∙
12/16/2018
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
Knowledge Graph (KG) embedding is a fundamental problem in data mining r...
research
∙
12/11/2018
Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising
Non-local low-rank tensor approximation has been developed as a state-of...
research
∙
11/23/2018
