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Found 19 papers, 9 papers with code
Hypergraph Self-supervised Learning with Sampling-efficient Signals
Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels.
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
We find that in graph anomaly detection, the homophily distribution differences between different classes are significantly greater than those in homophilic and heterophilic graphs.
Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
In this study, we present aLLM4TS, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning.
Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness
Specifically, GST initially constructs a topology & semantic anchor at a low training cost, followed by performing dynamic sparse training to align the sparse graph with the anchor.
Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding
In the experiments, we evaluate KANGAROO over various knowledge-aware and general NLP tasks in both full and few-shot learning settings, outperforming various KEPLM training paradigms performance in closed-domains significantly.
CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model
Large language models (LLMs) have demonstrated great potential in the financial domain.
CFGPT: Chinese Financial Assistant with Large Language Model
The CFAPP is centered on large language models (LLMs) and augmented with additional modules to ensure multifaceted functionality in real-world application.
CSPRD: A Financial Policy Retrieval Dataset for Chinese Stock Market
In recent years, great advances in pre-trained language models (PLMs) have sparked considerable research focus and achieved promising performance on the approach of dense passage retrieval, which aims at retrieving relative passages from massive corpus with given questions.
Semi-supervised Credit Card Fraud Detection via Attribute-driven Graph Representation
Then we pass messages among the nodes through a Gated Temporal Attention Network (GTAN) to learn the transaction representation.
Ranked #1 on
Node Classification
on Amazon-Fraud
Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction
The price movement prediction of stock market has been a classical yet challenging problem, with the attention of both economists and computer scientists.
Financial time series forecasting with multi-modality graph neural network
The heterogeneous graph network is constructed by the sources as nodes and relations in our financial knowledge graph as edges.
Defending Graph Neural Networks via Tensor-Based Robust Graph Aggregation
In this paper, we propose a tensor-based framework for GNNs to learn robust graphs from adversarial graphs by aggregating predefined robust graphs to enhance the robustness of GNNs via tensor approximation.
Graph Neural Network for Fraud Detection via Spatial-Temporal Attention
But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant fraudulent behavior patterns in the online detection system.
iConViz: Interactive Visual Exploration of the Default Contagion Risk of Networked-Guarantee Loans
Groups of enterprises can serve as guarantees for one another and form complex networks when obtaining loans from commercial banks.
Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection
But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant patterns in fraudulent behavior.
Exploiting Motion Information from Unlabeled Videos for Static Image Action Recognition
Specifically, the VRE module includes a proxy task which imposes pseudo motion label constraint and temporal coherence constraint on unlabeled videos, while the MRA module could predict the motion information of a static action image by exploiting unlabeled videos.
Image Cropping with Composition and Saliency Aware Aesthetic Score Map
Aesthetic image cropping is a practical but challenging task which aims at finding the best crops with the highest aesthetic quality in an image.
Learning from Web Data with Self-Organizing Memory Module
However, crawled web images usually have two types of noises, label noise and background noise, which induce extra difficulties in utilizing them effectively.
Ranked #14 on
Image Classification
on WebVision-1000
Credit Card Fraud Detection Using Convolutional Neural Networks
Credit card is becoming more and more popular in financial transactions, at the same time frauds are also increasing.
