For questions related to modelling external environment, functional models tuned through convergent methods such as artificial networks or fuzzy logic containers, loss models, semantic models, model-based reasoning, or other kinds of models used in AI research, development, or practice.
Questions tagged [models]
124 questions
20
votes
3 answers
Are there any computational models of mirror neurons?
From Wikipedia:
A mirror neuron is a neuron that fires both when an animal acts and when the animal observes the same action performed by another.
Mirror neurons are related to imitation learning, a very useful feature that is missing in current…
rcpinto
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14
votes
4 answers
What is the relevance of AIXI on current artificial intelligence research?
From Wikipedia:
AIXI ['ai̯k͡siː] is a theoretical mathematical formalism for artificial general intelligence. It combines Solomonoff induction with sequential decision theory. AIXI was first proposed by Marcus Hutter in 2000[1] and the results…
rcpinto
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11
votes
4 answers
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 examples: "RL agents" and "deep learning models".
Are the two words…
malioboro
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9
votes
1 answer
What causes a model to require a low learning rate?
I've pondered this for a while without developing an intuition for the math behind the cause of this.
So what causes a model to need a low learning rate?
JohnAllen
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8
votes
2 answers
What are the real world uses for SAT solvers?
Why somebody would use SAT solvers (Boolean satisfiability problem) to solve their real world problems?
Are there any examples of the real uses of this model?
kenorb
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7
votes
3 answers
To what does the number of hidden layers in a neural network correspond?
In a neural network, the number of neurons in the hidden layer corresponds to the complexity of the model generated to map the inputs to output(s). More neurons creates a more complex function (and thus the ability to model more nuanced decision…
SeeDerekEngineer
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6
votes
2 answers
Are there any pretrained models for human recognition from all angles?
I need to be able to detect and track humans from all angles, especially above.
There are, obviously, quite a few well-studied models for human detection and tracking, usually as part of general-purpose object detection, but I haven't been able to…
T3db0t
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6
votes
0 answers
What is meant by "model discriminability for local patches within the receptive field"?
In the abstract of the paper Network In Network, the authors write
We propose a novel deep network structure called "Network In Network"(NIN) to enhance model discriminability for local patches within the receptive field
What does the part in bold…
harsh kumar Chourasia
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6
votes
2 answers
Rollout algorithm like Monte Carlo search suggest model based reinforcement learning?
From what I understand, Monte Carlo Tree Search Algorithm is a solution algorithm for model free reinforcement learning (RL).
Model free RL means agent doesnt know the transition and reward model. Thus for it to know which next state it will observe…
user21872
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5
votes
1 answer
How do I predict if it is rainy or not?
I'm building a weather station, where I'm sensing temperature, humidity, air pressure, brightness, $CO_2$, but I don't have a raindrop sensor.
Is it possible to create an AI which can say if it's raining or not, with the help of the given data…
Ribisl
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5
votes
1 answer
Which model would recognize the rotated version of its input without explicit training during inference?
Training an MNIST classifier with a regular ANN will make the model recognize its unrotated version.
But is there such a model where I train the unrotated version as usual, but it also recognizes its rotated version, e.g., the 90-degree version,…
Muhammad Ikhwan Perwira
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5
votes
2 answers
Machine learning with graph as input and output
In my application, I have inputs and outputs that could be represented as graphs. I have a number of acceptable pairs of input and output graphs. I want to use these to train a model.
I am looking for pointers where simple examples of learning…
Suresh
- 159
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5
votes
4 answers
What is the fundamental difference between an ML model and a function?
A model can be roughly defined as any design that is able to solve an ML task. Examples of models are the neural network, decision tree, Markov network, etc.
A function can be defined as a set of ordered pairs with one-to-many mapping from a domain…
hanugm
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5
votes
1 answer
How does an unsupervised learning model learn?
How does an unsupervised learning model learn, if it does not involve any target values?
kenorb
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5
votes
3 answers
Isn't a simulation a great model for model-based reinforcement learning?
Most reinforcement learning agents are trained in simulated environments. The goal is to maximize performance in (often) the same environment, preferably with a minimum amount of interactions. Having a good model of the environment allows to use…
Rustam
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