1,503 research outputs found
Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization
One obstacle that so far prevents the introduction of machine learning models
primarily in critical areas is the lack of explainability. In this work, a
practicable approach of gaining explainability of deep artificial neural
networks (NN) using an interpretable surrogate model based on decision trees is
presented. Simply fitting a decision tree to a trained NN usually leads to
unsatisfactory results in terms of accuracy and fidelity. Using L1-orthogonal
regularization during training, however, preserves the accuracy of the NN,
while it can be closely approximated by small decision trees. Tests with
different data sets confirm that L1-orthogonal regularization yields models of
lower complexity and at the same time higher fidelity compared to other
regularizers.Comment: 8 pages, 18th IEEE International Conference on Machine Learning and
Applications (ICMLA) 201
Attention Visualizer Package: Revealing Word Importance for Deeper Insight into Encoder-Only Transformer Models
This report introduces the Attention Visualizer package, which is crafted to
visually illustrate the significance of individual words in encoder-only
transformer-based models. In contrast to other methods that center on tokens
and self-attention scores, our approach will examine the words and their impact
on the final embedding representation. Libraries like this play a crucial role
in enhancing the interpretability and explainability of neural networks. They
offer the opportunity to illuminate their internal mechanisms, providing a
better understanding of how they operate and can be enhanced. You can access
the code and review examples on the following GitHub repository:
https://github.com/AlaFalaki/AttentionVisualizer.Comment: 12 pages, 15 figure
Algorithm Auditing: Managing the Legal, Ethical, and Technological Risks of Artificial Intelligence, Machine Learning, and Associated Algorithms
Algorithms are becoming ubiquitous. However, companies are increasingly alarmed about their algorithms causing major financial or reputational damage. A new industry is envisaged: auditing and assurance of algorithms with the remit to validate artificial intelligence, machine learning, and associated algorithms
A attention way in Explainable methods for infant brain
Deploying reliable deep learning techniques in interdisciplinary applications
needs learned models to output accurate and ({even more importantly})
explainable predictions. Existing approaches typically explicate network
outputs in a post-hoc fashion, under an implicit assumption that faithful
explanations come from accurate predictions/classifications. We have an
opposite claim that explanations boost (or even determine) classification. That
is, end-to-end learning of explanation factors to augment discriminative
representation extraction could be a more intuitive strategy to inversely
assure fine-grained explainability, e.g., in those neuroimaging and
neuroscience studies with high-dimensional data containing noisy, redundant,
and task-irrelevant information. In this paper, we propose such an explainable
geometric deep network dubbed.Comment: Some parts of the thesis are still being revise
Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification
Convolutional neural networks excel in histopathological image
classification, yet their pixel-level focus hampers explainability. Conversely,
emerging graph convolutional networks spotlight cell-level features and medical
implications. However, limited by their shallowness and suboptimal use of
high-dimensional pixel data, GCNs underperform in multi-class histopathological
image classification. To make full use of pixel-level and cell-level features
dynamically, we propose an asymmetric co-training framework combining a deep
graph convolutional network and a convolutional neural network for multi-class
histopathological image classification. To improve the explainability of the
entire framework by embedding morphological and topological distribution of
cells, we build a 14-layer deep graph convolutional network to handle cell
graph data. For the further utilization and dynamic interactions between
pixel-level and cell-level information, we also design a co-training strategy
to integrate the two asymmetric branches. Notably, we collect a private
clinically acquired dataset termed LUAD7C, including seven subtypes of lung
adenocarcinoma, which is rare and more challenging. We evaluated our approach
on the private LUAD7C and public colorectal cancer datasets, showcasing its
superior performance, explainability, and generalizability in multi-class
histopathological image classification
Explainability of Vision Transformers: A Comprehensive Review and New Perspectives
Transformers have had a significant impact on natural language processing and
have recently demonstrated their potential in computer vision. They have shown
promising results over convolution neural networks in fundamental computer
vision tasks. However, the scientific community has not fully grasped the inner
workings of vision transformers, nor the basis for their decision-making, which
underscores the importance of explainability methods. Understanding how these
models arrive at their decisions not only improves their performance but also
builds trust in AI systems. This study explores different explainability
methods proposed for visual transformers and presents a taxonomy for organizing
them according to their motivations, structures, and application scenarios. In
addition, it provides a comprehensive review of evaluation criteria that can be
used for comparing explanation results, as well as explainability tools and
frameworks. Finally, the paper highlights essential but unexplored aspects that
can enhance the explainability of visual transformers, and promising research
directions are suggested for future investment.Comment: 20 pages,5 figure
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