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Found 13 papers, 5 papers with code
AcademicGPT: Empowering Academic Research
Our training corpus mainly consists of academic papers, thesis, content from some academic domain, high-quality Chinese data and others.
Privacy-preserving design of graph neural networks with applications to vertical federated learning
The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management (FRM).
Convex Optimal Power Flow Based on Power Injection-based Equations and Its Application in Bipolar DC Distribution Network
Optimal power flow (OPF) is a fundamental tool for analyzing the characteristics of bipolar DC distribution network (DCDN).
OptIForest: Optimal Isolation Forest for Anomaly Detection
Extensive experiments on a series of benchmarking datasets for comparative and ablation studies demonstrate that our approach can efficiently and robustly achieve better detection performance in general than the state-of-the-arts including the deep learning based methods.
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones.
Huatuo-26M, a Large-scale Chinese Medical QA Dataset
Moreover, we also experimentally show the benefit of the proposed dataset in many aspects: (i) trained models for other QA datasets in a zero-shot fashion; and (ii) as external knowledge for retrieval-augmented generation (RAG); and (iii) improving existing pre-trained language models by using the QA pairs as a pre-training corpus in continued training manner.
Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements and Challenges
Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting.
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention
With the frequent happening of privacy leakage and the enactment of privacy laws across different countries, data owners are reluctant to directly share their raw data and labels with any other party.
Differentially Private Learning with Per-Sample Adaptive Clipping
As one way to implement privacy-preserving AI, differentially private learning is a framework that enables AI models to use differential privacy (DP).
Inductive Matrix Completion Using Graph Autoencoder
However, without node content (i. e., side information) for training, the user (or item) specific representation can not be learned in the inductive setting, that is, a model trained on one group of users (or items) cannot adapt to new users (or items).
Ranked #3 on
Recommendation Systems
on MovieLens 1M
A Vertical Federated Learning Framework for Graph Convolutional Network
Recently, Graph Neural Network (GNN) has achieved remarkable success in various real-world problems on graph data.
Odd-even layer-number effect and layer-dependent magnetic phase diagrams in MnBi2Te4
However, the layer-dependent magnetism of MnBi2Te4, which is fundamental and crucial for further exploration of quantum phenomena in this system, remains elusive.
Materials Science
A New Local Transformation Module for Few-shot Segmentation
The result by the matrix transformation can be regarded as an attention map with high-level semantic cues, based on which a transformation module can be built simply. The proposed transformation module is a general module that can be used to replace the transformation module in the existing few-shot segmentation frameworks.
Ranked #79 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
