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Found 6 papers, 2 papers with code
基于多头注意力和BiLSTM改进DAM模型的中文问答匹配方法(Chinese question answering method based on multi-head attention and BiLSTM improved DAM model)
针对目前检索式多轮对话深度注意力机制模型DAM(Deep Attention Matching Network)候选回复细节不匹配和语义混淆的问题, 本文提出基于多头注意力和双向长短时记忆网络(BiLSTM)改进DAM模型的中文问答匹配方法, 该方法采用多头注意力机制, 使模型有能力建模较长的多轮对话, 更好的处理目标回复与上下文的匹配关系。此外, 本文在特征融合过程中采用BiLSTM模型, 通过捕获多轮对话中的序列依赖关系, 进一步提升选择目标候选回复的准确率。本文在豆瓣和电商两个开放数据集上进行实验, 实验性能均优于DAM基线模型, R10@1指标在含有词向量增强的情况下提升了1. 5%。
Towards Equity and Algorithmic Fairness in Student Grade Prediction
With equity of educational outcome as the aim, we trial strategies for boosting predictive performance on historically underserved groups and find success in sampling those groups in inverse proportion to their historic outcomes.
Combating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System
Collaborative filtering based algorithms, including Recurrent Neural Networks (RNN), tend towards predicting a perpetuation of past observed behavior.
Goal-based Course Recommendation
With cross-disciplinary academic interests increasing and academic advising resources over capacity, the importance of exploring data-assisted methods to support student decision making has never been higher.
Connectionist Recommendation in the Wild: On the utility and scrutability of neural networks for personalized course guidance
The aggregate behaviors of users can collectively encode deep semantic information about the objects with which they interact.
Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts
Sentiment analysis of short texts is challenging because of the limited contextual information they usually contain.
