Project Background:
The Evidential Deep Learning (EDL) pertains to the context and motivation behind using probabilistic and uncertainty-aware models in deep learning tasks. Traditional deep learning models often provide point estimates or deterministic predictions, without quantifying uncertainty in their predictions. However, autonomous driving, medical diagnosis, and financial risk assessment, understanding and managing uncertainty are crucial for decision-making and reliability. The emergence of EDL addresses this challenge by integrating principles from probabilistic modeling and Bayesian inference into deep learning frameworks.
The project in EDL involves recognizing the limitations of traditional deep learning models in handling uncertainty, especially in scenarios where decisions based on predictions require confidence intervals, risk assessments, or decision boundaries. By incorporating uncertainty estimation into deep learning models, EDL aims to provide more informative and reliable predictions, along with measures of uncertainty that can guide decision-making processes. This is particularly important in safety-critical applications where model errors or uncertainties can have significant consequences.
Problem Statement
- Traditional deep learning models often lack robust mechanisms for quantifying uncertainty in predictions, leading to limited insights into model confidence and reliability.
- The inability of standard deep learning models to provide uncertainty estimates hinders their application in risk assessment tasks where understanding uncertainty is crucial for risk management and mitigation strategies.
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Deep learning models may suffer from miscalibration, where predicted probabilities do not reflect actual confidence levels. This can lead to overconfidence or underestimation of uncertainty, impacting decision-making processes.
- Lack of uncertainty quantification makes it challenging for users to trust and interpret model outputs, limiting the adoption of deep learning systems in critical applications that require transparent and reliable decision support.
Aim and Objectives
- Develop EDL models that provide accurate predictions along with reliable uncertainty estimates, enhancing decision-making in uncertain environments.
- Design probabilistic deep learning architectures capable of quantifying uncertainty in predictions and propagating uncertainty through model layers.
- Explore methods for uncertainty calibration to ensure that predicted probabilities align with actual confidence levels, improving model reliability.
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Investigate techniques for uncertainty-aware training and inference, optimizing model performance under varying degrees of uncertainty.
- Evaluate the robustness and generalization of EDL models across diverse datasets and application domains, assessing their effectiveness in managing uncertainty.
- Develop strategies for interpreting and visualizing uncertainty estimates, enhancing user understanding and trust in EDL model outputs.
- Apply EDL techniques to real-world tasks such as autonomous driving, medical diagnosis, and financial risk assessment, demonstrating the practical utility and benefits of uncertainty-aware deep learning.
Contributions to Evidential Deep Learning
- Develop novel EDL architectures that accurately quantify uncertainty and provide reliable confidence intervals in predictions.
- Introduce methods for calibrating uncertainty estimates, ensuring that model predictions align with actual confidence levels and improving decision-making under uncertainty.
- Investigate techniques to enhance the robustness and generalization of EDL models across diverse datasets and application domains, improving model performance in uncertain environments.
- Develop strategies for visualizing and interpreting uncertainty estimates, enhancing user understanding and trust in EDL model outputs, and promoting adoption in critical applications.
Deep Learning Algorithms for Evidential Deep Learning
- Bayesian Neural Networks (BNNs)
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Monte Carlo Dropout
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Variational Inference
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Epistemic Uncertainty Estimation
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Aleatoric Uncertainty Estimation
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Gaussian Processes
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Ensemble Methods
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Bootstrap Aggregating (Bagging)
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Mixture Density Networks (MDNs)
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Bayesian Optimization
Datasets for Evidential Deep Learning
- MNIST (Modified National Institute of Standards and Technology)
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CIFAR-10 (Canadian Institute for Advanced Research - 10 classes)
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IMDB Movie Reviews
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Fashion-MNIST
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Adult Income Dataset
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Wisconsin Breast Cancer Dataset
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Boston Housing Prices Dataset
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COCO (Common Objects in Context)
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ImageNet
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UCI Heart Disease Dataset
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
