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Found 14 papers, 5 papers with code
面向机器阅读理解的高质量藏语数据集构建(Construction of High-quality Tibetan Dataset for Machine Reading Comprehension)
“机器阅读理解是通过算法让机器根据给定的上下文回答问题, 从而测试机器理解自然语言的程度。其中, 数据集的构建是机器阅读理解的主要任务。目前, 相关算法模型在大多数流行的英语数据集上都取得了显著的成绩, 甚至超过了人类的表现。但对于低资源语言, 由于缺乏相应的数据集, 机器阅读理解研究还处于起步阶段。本文以藏语为例, 人工构建了藏语机器阅读理解数据集(TibetanQA), 其中包含20000个问题答案对和1513篇文章。本数据集的文章均来自云藏网, 涵盖了自然、文化和教育等12个领域的知识, 问题形式多样且具有一定的难度。另外, 该数据集在文章收集、问题构建、答案验证、回答多样性和推理能力等方面, 均采用严格的流程以确保数据的质量, 同时采用基于语言特征消融输入的验证方法说明了数据集的质量。最后, 本文初步探索了三种经典的英语阅读理解模型在TibetanQA数据集上的表现, 其结果难以媲美人类, 这表明在藏语机器阅读理解任务上还需要更进一步的探索。”
Ti-Reader: 基于注意力机制的藏文机器阅读理解端到端网络模型(Ti-Reader: An End-to-End Network Model Based on Attention Mechanisms for Tibetan Machine Reading Comprehension)
“机器阅读理解旨在教会机器去理解一篇文章并且回答与之相关的问题。为了解决低资源语言上机器阅读理解模型性能低的问题, 本文提出了一种基于注意力机制的藏文机器阅读理解端到端网络模型Ti-Reader。首先, 为了编码更细粒度的藏文文本信息, 本文将音节和词相结合进行词表示, 然后采用词级注意力机制去关注文本中的关键词, 采用重读机制去捕捉文章和问题之间的语义信息, 采用自注意力机制去匹配问题与答案的隐变量本身, 为答案预测提供更多的线索。最后, 实验结果表明, Ti-Reader模型提升了藏文机器阅读理解的性能, 并且在英文数据集SQuAD上也有较好的表现。”
Active Exploration of Multimodal Complementarity for Few-Shot Action Recognition
In meta-training, we design an Active Sample Selection (ASS) module to organize query samples with large differences in the reliability of modalities into different groups based on modality-specific posterior distributions.
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes
We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable image classifier that integrates the power of deep learning and the interpretability of case-based reasoning.
Interpretable Mammographic Image Classification using Case-Based Reasoning and Deep Learning
Compared to other methods, our model detects clinical features (mass margins) with equal or higher accuracy, provides a more detailed explanation of its prediction, and is better able to differentiate the classification-relevant parts of the image.
ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning
Specifically, we first employ the comparison module to explore the pairwise sample relations to learn rich sample representations in the instance-level graph.
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations
We propose a framework for such decisions, including a globally interpretable machine learning model, an interactive visualization of it, and several types of summaries and explanations for any given decision.
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography
Mammography poses important challenges that are not present in other computer vision tasks: datasets are small, confounding information is present, and it can be difficult even for a radiologist to decide between watchful waiting and biopsy based on a mammogram alone.
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting.
Interpretable Image Recognition with Hierarchical Prototypes
Hence, we may find distinct explanations for the prediction an image receives at each level of the taxonomy.
An Interpretable Model with Globally Consistent Explanations for Credit Risk
We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment.
This Looks Like That: Deep Learning for Interpretable Image Recognition
In this work, we introduce a deep network architecture -- prototypical part network (ProtoPNet), that reasons in a similar way: the network dissects the image by finding prototypical parts, and combines evidence from the prototypes to make a final classification.
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input.
An Optimization Approach to Learning Falling Rule Lists
A falling rule list is a probabilistic decision list for binary classification, consisting of a series of if-then rules with antecedents in the if clauses and probabilities of the desired outcome ("1") in the then clauses.
