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.

Filter by
Sorted by
Tagged with
19 votes
3 answers
2k 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, ...
user avatar
  • 1,999
14 votes
4 answers
5k 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 ...
user avatar
  • 1,999
9 votes
3 answers
1k 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 ...
user avatar
  • 2,551
8 votes
1 answer
544 views

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?
user avatar
  • 207
7 votes
3 answers
240 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 ...
user avatar
6 votes
2 answers
1k 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. ...
user avatar
6 votes
2 answers
5k 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?
user avatar
  • 10k
6 votes
2 answers
2k 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-...
user avatar
  • 161
5 votes
1 answer
107 views

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 ...
user avatar
5 votes
2 answers
1k 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 ...
user avatar
  • 159
5 votes
2 answers
213 views

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 ...
user avatar
  • 53
5 votes
3 answers
173 views

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 ...
user avatar
5 votes
0 answers
63 views

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 ...
user avatar
5 votes
1 answer
100 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 ...
user avatar
4 votes
1 answer
1k views

Is there a machine learning model that can be trained with labels that only say how "right" or "wrong" it was?

I'm trying to find the name for a model that is used to output a decision (maybe something like right, left, or ...
user avatar
4 votes
1 answer
514 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 ...
user avatar
  • 61
4 votes
1 answer
142 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 ...
user avatar
  • 41
3 votes
4 answers
893 views

How to embed/deploy an arbitrary machine learning model on microcontrollers?

Say I have a machine learning model trained on a laptop and I then want to embed/deploy the model on a microcontroller. How can I do this? I know that TensorflowLite Micro generates a C header to be ...
user avatar
  • 91
3 votes
2 answers
2k views

What is the "thing" which is trained in AI model training [closed]

I am a newbie in the fantastic AI world, I have started my learning recently. After a while, my understanding is, we need to feed in tremendous data to train a or many models. Once the training is ...
user avatar
  • 181
3 votes
4 answers
941 views

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 ...
user avatar
  • 3,099
3 votes
1 answer
584 views

How does an unsupervised learning model learn?

How does an unsupervised learning model learn, if it does not involve any target values?
user avatar
  • 10k
3 votes
2 answers
92 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 ...
user avatar
  • 10k
3 votes
1 answer
119 views

What is the difference between parametric and non-parametric models?

A model can be classified as parametric or non-parametric. How are models classified as parametric and non-parametric models? What is the difference between the two approaches?
user avatar
  • 93
3 votes
2 answers
315 views

Is there a simple way of classifying images of size differing from the input of existing image classifiers?

Most image classifiers like Inception-v3 accept images of about size 299 x 299 x 3 as input. In this particular case, I cannot resize the image and lose resolution. Is there an easy solution of ...
user avatar
3 votes
2 answers
121 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 ...
user avatar
  • 1,644
3 votes
1 answer
210 views

What is the difference between model and data distributions?

Is there any difference between the model distribution and data distribution, or are they the same?
user avatar
3 votes
1 answer
193 views

How does one even begin to mathematically model an AI algorithm?

How does one even begin to mathematically model an AI algorithm, like alpha-beta pruning or even its thousands of variations, to determine which variation is best?
user avatar
  • 457
3 votes
2 answers
353 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.
user avatar
  • 71
3 votes
0 answers
20 views

How should I compare multiple machine learning models to be (generally) fair to all models?

I am testing multiple models on IBM HR Analytics Attrition Dataset (1470 lines) and HR Analytics dataset (15000 lines) for a research project. The models include traditional models (Naive Bayes, SVM), ...
user avatar
3 votes
1 answer
48 views

Is there a complement to GPT/2/3 that can be trained using supervised learning methods?

This is a bit of a soft question, not sure if it's on topic, please let me know how I can improve it if it doesn't meet the criteria for the site. GPT models are unsupervised in nature and are (from ...
user avatar
3 votes
0 answers
18 views

What is a good model for regression problem with binary features and small data?

I am trying to predict the solution time for riddles in which matchsticks are combined into digits and operators. An example of a matchstick riddle is 4-2=8. The solution for this riddle would be ...
user avatar
3 votes
0 answers
77 views

Designing state representation for board game

I am trying to write self-play RL (NN + MCTS http://web.stanford.edu/~surag/posts/alphazero.html) to "solve" a board game. However, I got stuck in designing boardgame same (input layer for NN). 1) ...
user avatar
  • 31
2 votes
1 answer
1k views

After a model has been trained, how do I use it to address the real-world problems?

I understand the way we build and train a model, but all of the online courses I've found end with this. I can't find any course explaining the process of utilizing the trained model to address the ...
user avatar
2 votes
2 answers
250 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 ...
user avatar
2 votes
1 answer
121 views

Why is the hypothesis function $h_{\theta}(x)$ equivalent to $E[y | x; \theta]$ in generalised linear models?

Reading through the CS229 lecture notes on generalised linear models, I came across the idea that a linear regression problem can be modelled as a Gaussian distribution, which is a form of the ...
user avatar
  • 1,211
2 votes
1 answer
59 views

Neural networks with internal dynamics in the state-space form

Neural networks with feedback (Hopfield, Hamming, etc.) differ from ordinary neural networks (multilayer perceptrons, etc.), which turns them into a dynamic element with its own internal dynamics (if ...
user avatar
  • 227
2 votes
1 answer
67 views

What is the current state-of-the-art in unsupervised cross-lingual representation learning?

What is the current state-of-the-art in unsupervised cross-lingual representation learning?
user avatar
  • 1,243
2 votes
1 answer
27 views

Should I model a problem with quantised output as classification or regression?

Say I have some data I am trying to learn, and I'm aware that the output is quantised in some way, e.g. I can get only get discrete values (0.1, 0.2, 0.3...0.9) in a finite range. Would you treat ...
user avatar
2 votes
1 answer
25 views

Is it mostly the case to train with available models

I quite often find projects using pre-trained model and using them as a starting point for their new model that learns something novel from thier dataset or on-live learning process - e.g. using a ...
user avatar
  • 348
2 votes
1 answer
171 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 ...
user avatar
  • 361
2 votes
0 answers
42 views

Can the environment change even without the intervention of the agent in Reinforcement Learning?

I'm modeling a problem using Reinforcement Learning (RL). Formally, I have two agents: one of them is the one that I have to program and model, the other one is unpredictable (random). With ...
user avatar
2 votes
0 answers
48 views

How and why do state-of-the-art models in medical segmentation differ from general segmentation models?

I am just getting into medical image segmentation and have been able to understand the state-of-the-art architectures, like Double UNet, UNet++, and Multiresunet. What I haven't understood yet: Why ...
user avatar
2 votes
1 answer
339 views

What is the difference between FC and MLP in as used in PointNet?

I am trying to understand the PointNet network for dealing with point clouds and struggling with understanding the difference between FC and MLP: "FC is fully connected layer operating on each ...
user avatar
  • 21
2 votes
0 answers
148 views

What are the advantages and disadvantages of extrinsic and perplexity model evaluation in NLP?

In the video Evaluation and Perplexity by Dan Jurafsky, the author talks about extrinsic and perplexity evaluation in the context of natural language processing (NLP). What are the advantages and ...
user avatar
  • 1,243
2 votes
0 answers
52 views

Using ML for Enemy Generation in Video Games

I am attempting to make a 2-D platformer game where the player traverses through an evil factory that is producing killer robots. The robots spawn at multiple specific locations in each level and ...
user avatar
2 votes
0 answers
681 views

Why isn't there a model playing FPS like CoD or Battlefield already existing?

Assuming we had an unlimited time to train a model and a very powerful machine to use our model in real-time (hello quantum computer), I'd like to know why no one could achieve to build an AI able to ...
user avatar
  • 121
2 votes
0 answers
58 views

Correlating two models to predict the output of one that corresponds to an output of the other

I am currently working on a problem and now got stuck to implement one of it's steps. This is a simple attempt to explain what I am currently facing, which is something that I am aiming to implement ...
user avatar
2 votes
0 answers
52 views

Which deep neural networks are appropriate for the detection of bombs?

This is a follow-up question from my previous post here about explosion detection. I gathered a dataset of explosions. As I'm new to Deep Learning in Keras, I'm trying to see what architecture best ...
user avatar
  • 953
2 votes
0 answers
32 views

A NN based model of a Cattle for 'Heat Detection'

I am very new to AI/ML but have lot of interest in these. I am trying to understand how this gadget works. So far I have understood that a NN model of the cattle is generated by offline ...
user avatar
  • 121
2 votes
0 answers
46 views

How to know when a Environment will yield a deterministic model

Given enough experiment data on time taken for objects to fall to earth from different heights, one can create various models that will accurately predict the time it will take for an object falling ...
user avatar
  • 348