Linked Questions

2 votes
1 answer
76 views

What is the definition of a "model"? [duplicate]

What is the definition of a "model" in the discussion of a neural network? I need a canonical definition. Can you please supply me with a definition along with a reference from any book or ...
user366312's user avatar
91 votes
6 answers
98k views

What's the difference between model-free and model-based reinforcement learning?

What's the difference between model-free and model-based reinforcement learning? It seems to me that any model-free learner, learning through trial and error, could be reframed as model-based. In ...
mynameisvinn's user avatar
  • 1,011
31 votes
3 answers
15k views

Where can I find the proof of the universal approximation theorem?

The Wikipedia article for the universal approximation theorem cites a version of the universal approximation theorem for Lebesgue-measurable functions from this conference paper. However, the paper ...
Leroy Od's user avatar
  • 475
16 votes
5 answers
3k views

Why are the initial weights of neural networks randomly initialised?

This might sound silly to someone who has plenty of experience with neural networks but it bothers me... Random initial weights might give you better results that would be somewhat closer to what a ...
Matas Vaitkevicius's user avatar
16 votes
2 answers
12k views

What is the difference between Q-learning, Deep Q-learning and Deep Q-network?

Q-learning uses a table to store all state-action pairs. Q-learning is a model-free RL algorithm, so how could there be the one called Deep Q-learning, as deep means using DNN; or maybe the state-...
Dan D.'s user avatar
  • 1,283
10 votes
3 answers
3k views

What are the differences between an agent and a model?

In the context of Artificial Intelligence, sometimes people use the word "agent" and sometimes use the word "model" to refer to the output of the whole "AI-process". For ...
malioboro's user avatar
  • 2,819
6 votes
3 answers
7k views

Are neural networks statistical models?

By reading the abstract of Neural Networks and Statistical Models paper it would seem that ANNs are statistical models. In contrast Machine Learning is not just glorified Statistics. I am looking ...
Leo Gallucci's user avatar
8 votes
3 answers
4k views

What is the difference between hypothesis space and representational capacity?

I am reading Goodfellow et al Deeplearning Book. I found it difficult to understand the difference between the definition of the hypothesis space and representation capacity of a model. In Chapter 5,...
Qwarzix's user avatar
  • 83
2 votes
2 answers
1k views

What is the difference between a learning algorithm and a hypothesis?

What's the distinction between a learning algorithm $A$ and a hypothesis $f$? I'm looking for a few concrete examples, if possible. For example, would the decision tree and random forest be considered ...
Shirish Kulhari's user avatar
5 votes
2 answers
1k views

When is a knowledge base consistent?

I am studying a knowledge base (KB) from the book "Artificial Intelligence: A Modern Approach" (by Stuart Russell and Peter Norvig) and from this series of slides. A formula is satisfiable ...
theantomc's user avatar
  • 263
3 votes
3 answers
708 views

Is there a way to calculate the closed-form expression of the function that a neural network computes?

As stated in the universal approximation theorem, a neural network can approximate almost any function. Is there a way to calculate the closed-form (or analytical) expression of the function that a ...
holistic's user avatar
  • 133
3 votes
1 answer
290 views

Sutton & Barto: what are parametrized functions?

From "Reinforcement Learning: An introduction (2nd ed.)" by Richard S. Sutton and Andrew G. Barto, on page 59 Instead, the agent would have to maintain $v_\pi$ and $q_\pi$ as parameterized ...
SomeoneUnknown's user avatar
4 votes
1 answer
490 views

What is the difference between a distribution model and a sampling model in Reinforcement Learning?

The book from Sutton and Barto, Reinforcement Learning: An Introduction, define a model in Reinforcement Learning as something that mimics the behavior of the environment, or more generally, that ...
A. Pesare's user avatar
  • 141
0 votes
3 answers
65 views

In Q-Learning the Q-Table is not considered a model of the game?

In a QTable you keep states and actions for the ongoing decision making, it somehow represents the knowledge of the world and your future decisions for this and any future instance of a game. In the ...
Raul Lapeira Herrero's user avatar
1 vote
3 answers
99 views

What is the difference between q and p in Statistical Notation(used in VAE)?

I'm looking at general visuals of Variational Autoencoders and I'm seeing that the encoder is typically expressed as q(z|x) with phi as a subscript while the decoder is p(x|z) with theta as a ...
Kiran Manicka's user avatar

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