Project Background:
The Bayesian Neural Networks (BNNs) typically delve into the motivation and context behind employing Bayesian techniques in neural networks. Traditional neural networks often lack robustness in handling uncertainty in data and model parameters, which is crucial in real-world scenarios where data might be noisy or limited. Bayesian approaches offer a principled framework for modeling uncertainty by treating model parameters as probability distributions rather than fixed values. It allows for more reliable predictions and better calibration of model confidence, which is essential for applications such as medical diagnosis, financial forecasting, and autonomous systems. Furthermore, it provides mechanisms for automatic model selection and regularization. By leveraging probabilistic inference, BNNs offer a promising avenue for improving the reliability and interpretability of neural network models across various domains.
Problem Statement
- Bayesian Neural Networks (BNNs) address the lack of uncertainty quantification in traditional neural networks.
-
BNNs aim to provide probabilistic inference on model parameters, enabling robustness in handling noisy or limited data.
-
The problem of overfitting in neural networks can be mitigated by Bayesian techniques such as automatic model selection and regularization.
- BNNs offer improved calibration of model confidence, which is crucial for medical diagnosis and financial forecasting applications.
- Leveraging probabilistic inference, BNNs enhance the reliability and interpretability of neural network predictions across diverse domains.
Aim and Objectives
- Enhance neural network robustness and reliability through probabilistic modeling.
-
Provide uncertainty quantification in predictions.
-
Mitigate overfitting through automatic model selection and regularization.
-
Improve model calibration for confident predictions.
- Enhance interpretability across various domains.
- Enable robust performance in scenarios with noisy or limited data.
Contributions to Bayesian Neural Networks
- Introducing probabilistic modeling to enhance neural network robustness.
- Developing techniques for uncertainty quantification in predictions.
- Proposing methods for mitigating overfitting through Bayesian approaches.
- Advancing model calibration for more confident predictions.
- Extending interpretability of neural network models across diverse domains.
- Providing solutions for robust performance in scenarios with noisy or limited data.
Deep Learning Algorithms for Bayesian Neural Networks
- Variational Inference
-
Monte Carlo Dropout
-
Markov Chain Monte Carlo (MCMC)
-
Stochastic Gradient Langevin Dynamics (SGLD)
-
Hamiltonian Monte Carlo (HMC)
-
Bayesian Convolutional Neural Networks (BCNNs)
-
Bayesian Recurrent Neural Networks (BRNNs)
-
Expectation Propagation
-
Bayesian Optimization
-
Gaussian Processes
Datasets for Bayesian Neural Networks
- MNIST
-
CIFAR-10
-
ImageNet
-
Fashion-MNIST
-
COCO
-
KITTI
-
CelebA
-
CIFAR-100
-
SVHN
-
LFW
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
