Search Results for author:
Found 11 papers, 3 papers with code
PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine
Moreover, to enhance stability of the prompt effect evaluation, we propose a novel prompt bagging method involving forward and backward thinking, which is superior to majority voting and is beneficial for both feedback and weight calculation in boosting.
Enhancing Personalized Ranking With Differentiable Group AUC Optimization
AUC is a common metric for evaluating the performance of a classifier.
A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model
The results show that the impact of investor sentiment on stock market liquidity and volatility is stronger.
Deep learning based Chinese text sentiment mining and stock market correlation research
We explore how to crawl financial forum data such as stock bars and combine them with deep learning models for sentiment analysis.
Research on the correlation between text emotion mining and stock market based on deep learning
This paper discusses how to crawl the data of financial forums such as stock bar, and conduct emotional analysis combined with the in-depth learning model.
Adaptive Edge Attention for Graph Matching with Outliers
To explore the potential of edges, EAGM learns edge attention on the assignment graph to 1) reveal the impact of each edge on graph matching, as well as 2) adjust the learning of edge representations adaptively.
Ranked #10 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation
Specifically, in the long-term module, we learn the temporal periodic interest of daily granularity, then utilize intra-level attention to form long-term interest.
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.
TGG: Transferable Graph Generation for Zero-shot and Few-shot Learning
A dual relation propagation approach is proposed, where relations captured by the generated graph are separately propagated from the seen and unseen subgraphs.
Visual Data Synthesis via GAN for Zero-Shot Video Classification
First, we propose multi-level semantic inference to boost video feature synthesis, which captures the discriminative information implied in joint visual-semantic distribution via feature-level and label-level semantic inference.
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification
Video representation learning is a vital problem for classification task.
