# Tag Info

Accepted

### Why do CNN's sometimes make highly confident mistakes, and how can one combat this problem?

The concept you are looking for is called epistemic uncertainty, also known as model uncertainty. You want the model to produce meaningful calibrated probabilities that quantify the real confidence of ...
• 566

### Why do CNN's sometimes make highly confident mistakes, and how can one combat this problem?

Your classifier is specifically learning the ways in which 0s are different from other digits, not what it really means for a digit to be a zero. Philosophically, you could say the model appears to ...
Accepted

### How should the neural network deal with unexpected inputs?

This is a very important problem that is usually overlooked. In fact, when training a neural network, there's often the implicit assumption that the data is independent and identically distributed, i....
• 33.8k

### Why do CNN's sometimes make highly confident mistakes, and how can one combat this problem?

Broken assumptions Generalization relies on making strong assumptions (no free lunch, etc). If you break your assumptions, then you're not going to have a good time. A key assumption of a standard ...
• 853
Accepted

### How can supervised learning be viewed as a conditional probability of the labels given the inputs?

This formulation/interpretation can indeed be confusing (or even misleading), as the output of a neural network is usually deterministic (i.e. given the same input $x$, the output is always the same, ...
• 33.8k
Accepted

### What is the intuition behind variational inference for Bayesian neural networks?

Your description of what is going on is more or less correct, although I am not completely sure that you have really understood it, given your last question. So, let me enumerate the steps. The ...
• 33.8k

### Why do CNN's sometimes make highly confident mistakes, and how can one combat this problem?

Apollys, That's a very well thought out response. Particularly, the philosophical discussion of the essence of "0-ness." I haven't actually performed this experiment, so caveat emptor... I wonder ...
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### Is there any research on models that provide uncertainty estimation?

Yes, there is some research on this topic. It's often called Bayesian machine learning or Bayesian deep learning (but I don't think this is a good name because there are models that aren't really ...
• 33.8k
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### How can a machine learning problem be reduced as a communication problem?

Information-theoretic view of Bayesian learning I once heard that the problem of approximating an unknown function can be modeled as a communication problem. How is this possible? Yes, this is ...
• 33.8k

### Why do CNN's sometimes make highly confident mistakes, and how can one combat this problem?

I'm an amateur with neural networks, but I will illustrate my understanding of how this problem comes to be. First, lets see how trivial neural network classifies 2D input into two classes : But ...
• 121
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### Why is my Keras prediction always close to 100% for one image class?

Traditional neural networks can be over-confident (i.e. give a probability close to $0$ or $1$) even when they are wrong, so you should not interpret the probability that it produces as a measure of ...
• 33.8k
1 vote

### What's the likelihood in Bayesian Neural Networks?

The likelihood depends on the task that you are solving, so this is similar to traditional neural networks (in fact, even these neural networks have a probabilistic/Bayesian interpretation!). For ...
• 33.8k
1 vote
Accepted

### Why is neural networks being a deterministic mapping not always considered a good thing?

Your intuition is right. The main reason why a deterministic function can be undesirable (or even dangerous, as I will explain below with an example) is that we may not have enough data to learn the ...
• 33.8k
1 vote

### Why is my Keras prediction always close to 100% for one image class?

Without more details about the nature of the dataset, it is impossible to know for sure. However, here are a few likely causes: You were calling predict on training data, not testing data. The ...

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