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Found 20 papers, 9 papers with code
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation
This resolves the aforementioned disregard for target areas and redundancy.
RecPrompt: A Prompt Tuning Framework for News Recommendation Using Large Language Models
In the evolving field of personalized news recommendation, understanding the semantics of the underlying data is crucial.
Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases
To make the analysis model applicable to more environments, we propose a noise patterns transferring model, which takes the spectrum of standard water samples in different environments as cases and learns the differences in their noise patterns, thus enabling noise patterns to transfer to unknown samples.
Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems.
Topic-Centric Explanations for News Recommendation
News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests.
Enhancing Topic Extraction in Recommender Systems with Entropy Regularization
In recent years, many recommender systems have utilized textual data for topic extraction to enhance interpretability.
Learning to Generalize for Cross-domain QA
Our method has been theoretically and empirically shown to be effective in enhancing the generalization ability of both generative and discriminative models.
Industry Classification Using a Novel Financial Time-Series Case Representation
We discuss why time-series data can present some significant representational challenges for conventional case-based reasoning approaches, and in response, we propose a novel representation based on stock returns embeddings, which can be readily calculated from raw stock returns data.
A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets
Industry classification schemes provide a taxonomy for segmenting companies based on their business activities.
A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification
Many recent deep learning-based solutions have widely adopted the attention-based mechanism in various tasks of the NLP discipline.
Stock Embeddings: Learning Distributed Representations for Financial Assets
Identifying meaningful relationships between the price movements of financial assets is a challenging but important problem in a variety of financial applications.
NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting
Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail.
Pseudo-labelling Enhanced Media Bias Detection
Leveraging unlabelled data through weak or distant supervision is a compelling approach to developing more effective text classification models.
Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities
Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices.
Fact Check: Analyzing Financial Events from Multilingual News Sources
The explosion in the sheer magnitude and complexity of financial news data in recent years makes it increasingly challenging for investment analysts to extract valuable insights and perform analysis.
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis
While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data.
LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification
Besides, the model allows better interpretability for predicted labels as the token-label edges are exposed.
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification
Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intelligence.
Explainable Text-Driven Neural Network for Stock Prediction
It has been shown that financial news leads to the fluctuation of stock prices.
