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Found 8 papers, 1 papers with code
Incorporating Casual Analysis into Diversified and Logical Response Generation
Although the Conditional Variational Auto-Encoder (CVAE) model can generate more diversified responses than the traditional Seq2Seq model, the responses often have low relevance with the input words or are illogical with the question.
Beyond Tree Models: A Hybrid Model of KAN and gMLP for Large-Scale Financial Tabular Data
However, financial datasets in the industry often encounter some challenges, such as data heterogeneity, the predominance of numerical features and the large scale of the data, which can range from tens of millions to hundreds of millions of records.
Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling
The estimator is general for all types of outcomes, and is able to comprehensively model the treatment and control group data together to approach the uplift.
Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning
In this paper, we fill in this gap by proposing a motif-preserving Graph Neural Network with curriculum learning (MotifGNN) to jointly learn the lower-order structures from the original graph and higherorder structures from multi-view motif-based graphs for financial default prediction.
Prompt Learning on Temporal Interaction Graphs
In detail, we propose a temporal prompt generator to offer temporally-aware prompts for different tasks.
Incorporating Causal Analysis into Diversified and Logical Response Generation
Although the Conditional Variational AutoEncoder (CVAE) model can generate more diversified responses than the traditional Seq2Seq model, the responses often have low relevance with the input words or are illogical with the question.
Hierarchical Capsule Prediction Network for Marketing Campaigns Effect
In this paper, we provide an in-depth analysis of the underlying parse tree-like structure involved in the effect prediction task and we further establish a Hierarchical Capsule Prediction Network (HapNet) for predicting the effects of marketing campaigns.
Causal Triple Attention Time Series Forecasting
Time series forecasting has historically been a key area of academic research and industrial applications.
