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Questions tagged [models]

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.

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34 views

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 ...
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0answers
19 views

Models at RunTime and Artificial Intelligence

I'm looking for some help... Someone can refer me or recommend recent studies that relate to the models at runtime with the AI, that would really help me a lot. I also appreciate your comments on the ...
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2answers
87 views

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. ...
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3answers
81 views

Does correlation between input affect Regression model?

I'm new to data science I'm currently working on regression problem and I have 10 inputs/attributes. My question is what to do if there are correlations among different features of the input data? ...
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2answers
82 views

Pre trained model for low resolution image! How to handle?

What's the strategy if the resolution of an image is very low such as 28 x 28 or 100 x 100 or 150 x 150, for transfer learning? Pre-trained models such as Inception, Xception, VGG-16 etc are required ...
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1answer
20 views

Is there a mathemtical example for Conditional Random Fields?

I am learning about probabilistic graphical models and I was wondering if there is an example explaining the math behind Conditional random Fields. Looking solely on the formula I have no Idea what we ...
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1answer
49 views

Neural network architecture for comparison

When someone wants to compare 2 inputs, the most widespread idea is to use a Siamese architecture. Siamese architecture is a very high level idea, and can be customized based on the problem we are ...
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1answer
115 views

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 ...
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2answers
57 views

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-...
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20 views

What is meant by “model discriminability for local patches within the receptive field”?

In the Abstract section of the paper Network In Network, what does the authors actually mean to say?
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18 views

How to handle Feature changes in a model deployed ?

I implemented and deployed with Flask an XGBoost model for a classification problem. But being aware that features importance can change over time to predict probability of label for new data, I ...
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1answer
130 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,...
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1answer
51 views

How to label “other” while labeling image for object detection/classification?

I want to train a model to recognize different category of food (example: rice, burger, apple, pizza, orange,... ) After the first training, I realized that the model is detecting other object as ...
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1answer
101 views

Mathematical modelling of A.I algorithms

How does one even begin to mathematically model an A.I algorithm like alpha-beta pruning or even its thousands of variations, to determine which variation is best?
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53 views

How can i program a “Intuitive Physics Engine" for a walking simulator?

In the paper Learning Physical Parameters from Dynamic Scenes, 2018 a framework is presented to program a probabilistic physics engine for simulating the movements of a puck. A noisy-Newtonian ...
3
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1answer
84 views

Machine Learning - Is selected models combination good?

I've selected more than 10 discriminative (Classification) models, each wrapped with a BaggingClassifier object, optimized with a GridSearchCV, and all of them placed within a VotingClassifier object. ...
2
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1answer
39 views

What is the state of the art in models of how the human brain performs goal-directed decision making? Can these models' principles be applied to AI?

What is the state of the art in models of how the human brain performs goal-directed decision making? Can these models’ principles and insights be applied to the field of Artificial Intelligence, e.g. ...
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2answers
134 views

Why do we need a model of the environment in Dyna?

In chapter 8 of "Reinforcement Learning: An Introduction" by Sutton and Barto, it is stated that Dyna needs a model to simulate the environment. But why do we need a model? Why can't we just use the ...
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0answers
33 views

Data Interpretation technique

In the model generation, in machine learning (consider supervised) If some data change the previous model function drastically then we should study that data. Does it happen? How to handle such ...
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0answers
30 views

Abstracting parameters of dynamic model from output time series

I am unable to identify general temrs or specific source of information for the below proposed problem. I would appreciate if the community can guide me to journal articles/books and keywords to look ...
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2answers
369 views

Are mathematical models the answer to general artificial intelligence?

As someone who knows basics of machine learning, I have doubts that mathematical models are answer for general AI. I am not sure if it is possible to represent emotions, intuition, knowledge and so on ...
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3answers
132 views

What kinds of systems have so far failed to be modeled via supervised artificial network training?

Artificial networks model systems with a set of inputs and outputs and expected behavior. To train a network for modeling such systems, hundreds, thousands, or millions of example inputs-output pairs ...
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6answers
1k views

difference between ai-architecture and ai-model?

What is the difference between AI architecture and AI models. Are both of them same? if not please distinguish both of them and give example of each. And also suggest books/ papers that delve on AI ...
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2answers
82 views

Machine learning and machine generated content conflict problem

Machine learning and NN trainings as a part of ML is based on data that was gotten from real world and inserted into virtual space by humans. Meanwhile NN are also used for data generation. Each year ...
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1answer
45 views

What is a model and how is it designed?

I read these things on the internet like My model determines the future scope..." or My model gives accurate readings about what the score would be..." What are these models? How are they ...
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0answers
15 views

Should I be using a validation curve accuracy score in lieu of error bars for a model fit to time series data?

Normally when doing a fit to some time series data (e.g., a polynomial fit), functions will return an associated error with each fitted point. I'm now trying out scikit-learn's support vector ...
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0answers
139 views

Is iLQG a good algorithm for model-based planning with simple environments?

In their work Continuous Deep Q-Learning with Model-based Acceleration, the author demonstrate great results of applying Imagination Rollouts for model-based acceleration of learning. They test their ...
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2answers
669 views

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 ...
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2answers
91 views

Is a deep technical understanding of neural networks required outside of research?

To understand the inner workings of neural networks, a fair amount of mathematical concepts is required. Backpropagation alone is a challenging technique if you are not fluent in calculating local ...
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3answers
87 views

Why are tree-based models more widely used in Medical Diagnosis?

In Ch-14.4 @ Pattern Recognition and Machine Learning by Bishop it is mentioned that tree-based models are more widely used in Medical Diagnosis. Apart from giving better performance, is there a ...
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0answers
42 views

Could you share your model of Intelligence and/or conciousness?

I'm wondering if anyone reading this has developed a flowchart type representation of Intelligence and/or consciousness. Some examples of theories would be the Three Stratum Theory of Intelligence, ...
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1answer
67 views

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 ...
2
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1answer
100 views

Network representation for Q-Learning in carrom

I am trying to build an agent to play carrom. The problem statement is roughly to estimate three parameters (normalized) : force angle of striker position of strike Since the state and action ...
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2answers
136 views

How can one intuitively understand generative v/s discriminative models, specifically with respect to when each is useful?

I'm trying to gain some intuition beyond definitions, in any possible dimension. I'd appreciate references to read.
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1answer
235 views

State representation of position in 2D plane for Reinforcement Learning (Q Learning)

I recently finished Course on RL by David Silver (on YT) and thought about trying it out on simple application in Unity Game Engine, where I've built simple labyrint with ball and want to teach the ...
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2answers
58 views

How to distinguish AI modeling from implementation?

Quote from this Eric's meta post about modelling and implementation: They are not exactly the same, although strongly related. This was a very difficult lesson to learn among mathematicians and ...
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1answer
344 views

How does unsupervised learning model learn?

Unsupervised learning does not involve target values, so basically targets are most likely the same as the inputs (in other words, involves no target values). So how does this model learn?
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2answers
102 views

How to store datasets of lexical connections?

I'm investigating the possibility of storing the semantic-lexical connections (such as the relationships to the other words such as phrases and other dependencies, its strength, part of speech, ...
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3answers
956 views

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, ...
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3answers
415 views

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 ...
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1answer
1k views

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?