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]
108 questions
19
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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10
votes
3 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
- 521
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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
- 161
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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
- 61
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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
1 answer
Correcting 'bad' translations in a sequence-to-sequence neural machine translation model
In working with basic sequence-to-sequence models for machine translation I have been able to achieve decent results. But inevitably some translations are not optimal or just flat-out incorrect. I am wondering if there is some way of "correcting"…
jrthom18
- 51
- 3
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…
Ray Walker
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5
votes
1 answer
What approach should I take to model forecasting problem in machine learning?
I have a dataset which contains 4000k rows and 6 columns. The goal is to predict travel time demand of a taxi. I have read many articles regarding how to approach the problem. So, every writer tell his own way. The thing which I have concluded from…
Saeed Ahmad
- 51
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5
votes
2 answers
How to detect frauds in advertising business using machine learning?
I am very beginner to this world. I still learning the basics of Machine learning and AI but i have a problem at hand and i am not sure which technique or Algorithm can be applied on it.
I am working on Click-Fraud detection in advertising. I need…
Mirza
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4
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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