Questions tagged [bayesian-neural-networks]
For questions related to Bayesian neural networks, which are artificial neural networks that incorporate uncertainty in their parameters.
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How could Bayesian neural networks be used for transfer learning?
In transfer learning, we use big data from similar tasks to learn the parameters of a neural network, and then fine-tune the neural network on our own task that has little data available for it. Here, ...
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Bayesian Perceptron: How is it compatible to Bayes Theorem?
I found a very interesting paper on the internet that tries to apply Bayesian inference with a gradient-free online-learning approach: [Bayesian Perceptron: Bayesian Perceptron: Towards fully Bayesian ...
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Bayesian Perceptron: Why to marginalize over neuron's output instead of it's weights?
I found a very interesting paper on the internet that tries to apply Bayesian inference with a gradient-free online-learning approach: Bayesian Perceptron: Towards fully Bayesian Neural Networks.
I ...
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1answer
107 views
What is the intuition behind variational inference for Bayesian neural networks?
I'm trying to understand the concept of Variational Inference for BNNs. My source is this work. The aim is to minimize the divergence between the approx. distribution and the true posterior
$$\text{KL}...
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1answer
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What's the likelihood in Bayesian Neural Networks?
I'm trying to understand the concept behind BNN.
Their are based on the Bayes Theorem:
$$p(w \mid \text{data}) = \frac{p(\text{data} \mid w)*p(w)}{p(\text{data})}$$
which boils down to
$$\text{...
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Why does this formula $\sigma^2 + \frac{1}{T}\sum_{t=1}^Tf^{\hat{W_t}}(x)^Tf^{\hat{W_t}}(x_t)-E(y)^TE(y)$ approximate the variance?
How does:
$$\text{Var}(y) \approx \sigma^2 + \frac{1}{T}\sum_{t=1}^Tf^{\hat{W_t}}(x)^Tf^{\hat{W_t}}(x_t)-E(y)^TE(y)$$
approximate variance?
I'm currently reading What Uncertainties Do We Need in ...
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1answer
68 views
Why is neural networks being a deterministic mapping not always considered a good thing?
Why is neural networks being a deterministic mapping not always considered a good thing?
So I'm excluding models like VAEs since those aren't entirely deterministic. I keep thinking about this and my ...
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1answer
33 views
Why is the E step in expectation maximisation algorithm called so?
The E step on the EM algorithm asks us to set the value of the variational lower bound to be equal to the posterior probability of the latent variable, given the data points and parameters. Clearly we ...
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1answer
59 views
Is there any research on models that provide uncertainty estimation?
Is there any research on machine learning models that provide uncertainty estimation?
If I train a denoising autoencoder on words and put through a noised word, I'd like it to return a certainty that ...
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0answers
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Are bayesian neural networks suited for text (or document) classification?
I've tried to do my research on Bayesian neural networks online, but I find most of them are used for image classification. This is probably due to the nature of Bayesian neural networks, which may be ...
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1answer
631 views
How should the neural network deal with unexpected inputs?
I recently wrote an application using a deep learning model designed to classify inputs. There are plenty of examples of this using images of irises, cats, and other objects.
If I trained a data ...
