Removing uncertainty in neural networks

Uncertainty in Bayesian Neural Networks

Weight Uncertainty in Neural Networks

We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood. We show that this principled kind of regularisation yields...

Implicit Weight Uncertainty in Neural Networks

We interpret HyperNetworks [5] within the framework of variational inference within implicit distributions[15, 7, 12]. Our method, Bayes by Hypernet, is able to model a richer variational distribution than previous methods. Experiments show that it achieves comparable predictive performance on the MNIST classification task while providing higher predictive uncertainties compared to MC-Dropout [...

rodbar dam slope stability analysis using neural networks

در این تحقیق شبکه عصبی مصنوعی برای پیش بینی مقادیر ضریب اطمینان و فاکتور ایمنی بحرانی سدهای خاکی ناهمگن ضمن در نظر گرفتن تاثیر نیروی اینرسی زلزله ارائه شده است. ورودی های مدل شامل ارتفاع سد و زاویه شیب بالا دست، ضریب زلزله، ارتفاع آب، پارامترهای مقاومتی هسته و پوسته و خروجی های آن شامل ضریب اطمینان می شود. مهمترین پارامتر مورد نظر در تحلیل پایداری شیب، بدست آوردن فاکتور ایمنی است. در این تحقیق ...

Neural networks underlying the metacognitive uncertainty response.

Humans monitor states of uncertainty that can guide decision-making. These uncertain states are evident behaviorally when humans decline to make a categorization response. Such behavioral uncertainty responses (URs) have also defined the search for metacognition in animals. While a plethora of neuroimaging studies have focused on uncertainty, the brain systems supporting a volitional strategy s...