All Questions
Tagged with bdl or bayesian-deep-learning
6 questions
3
votes
2
answers
2k
views
How can supervised learning be viewed as a conditional probability of the labels given the inputs?
In the literature and textbooks, one often sees supervised learning expressed as a conditional probability, e.g.,
$$\rho(\vec{y}|\vec{x},\vec{\theta})$$
where $\vec{\theta}$ denotes a learned set of ...
- 33
1
vote
1
answer
309
views
How can a machine learning problem be reduced as a communication problem?
I once heard that the problem of approximating an unknown function can be modeled as a communication problem. How is this possible?
- 187
37
votes
6
answers
11k
views
Why do CNN's sometimes make highly confident mistakes, and how can one combat this problem?
I trained a simple CNN on the MNIST database of handwritten digits to 99% accuracy. I'm feeding in a bunch of handwritten digits, and non-digits from a document.
I want the CNN to report errors, so I ...
- 1,369
7
votes
1
answer
2k
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 ...
- 173
2
votes
0
answers
153
views
Today's Practicality of Bayesian Neural Networks
Just having heard lately about BNNs (wow, ANNs and CNNs are clear; now there's a B? What's that? Ahh, Bayesian ;-)) and quickly getting their main idea and focus, that is, weights not being pure ...
- 153
1
vote
2
answers
1k
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Why is my Keras prediction always close to 100% for one image class?
I am using Keras (on top of TF 2.3) to train an image classifier. In some cases I have more than two classes, but often there are just two classes (either "good" or "bad"). I am ...
- 165