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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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What is the ideal GPU/CPU requirment for albert xxlargev1 squad2? [closed]

I am quite new to AI and Deep learning but I have specific question about what is the requiments to speed/create a conversational AI based on ALbert model. https://huggingface.co/ahotrod/...
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Which algorithm can find the best combination of players to maximize the chance of getting a high score?

I am looking for the right terminology for this problem, so I know what to learn about. Imagine a population of 100 people in a town. The town has a sport team with 10 positions that play in ...
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Why my classification results are correlated with the proportionality of my data?

I'm facing a problem. I'm working on mixed data model with NN (MLP & Word Embedding). My results are not pretty good. And I observed that the proportionality of my data are corelated with my ...
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2 votes
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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 ...
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1 vote
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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 ...
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Deep Learning Architecture where outputs from two different inputs are used for error calculation

Is there a deep learning architecture where outputs of the same model with two different inputs are used for error calculation (backpropagation)? Workflow: Input1 -----> Model ------> Output1 ...
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Does a complex problem need a complex model?

I ask in general if a complex problem needs a complex model, more concrete: The spread of corona in our society is a complex problem, which depends on several parameters (even parameters as education)....
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model and trained model parameters on CIFAR-10 [closed]

I'm looking for different models (specifically ResNet18/20, ResNet32/34, VGG16, MobileNet and SqueezeNet) and their parameters after training (i.e., .pth file) that ...
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searching for finding some mental disorder simulation by AI models

I have tried to find some AI models which could create one sense of the autism simulated video on the below questions: Searching for finding the similarity of the Autism verbal brain functionality ...
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About trying simulating the Autism brain activities by AI Models

I am trying to simulate the below Autism simulation's video by AI models, the scenario could be seen in the below sense of the video: In my hypothesis the visual part of the brain is bombarded by the ...
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1 vote
1 answer
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Are there any animation tools available to visualise and simulate deep neural networks? [closed]

Deep learning researchers have to work with a lot of models. The models may include different types of Layers: They include convolutional neural network layers, recurrent neural network layers, batch ...
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Why is the validation accuracy lower in case of CNN?

I fed the same set of 1.4 million data to two different models: MLP CNN model In both cases, I used the same parameters and hyperparameters. The CNN is showing comparatively lower accuracy (80%) ...
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How to ensemble two different computer vision models?

I have prepared two distinct models: Representing contour of the image Representing edges of the image. I would like to create a model which can take advantage of both models in predicting data. May ...
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Non-locally Electrically Programmable Logic Gates - Technological Advances Progress

Preface: I’d like to clarify that I understand what a relay is and that a PLC uses a fairly conventional microprocessor that only digitally establishes logical logic gate configuration as a digitally ...
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How to generate text descriptions from keywords?

I wonder how can I build a neural network which will generate text description from given tag/tags. Let's assume I have created such data structure: ...
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What is a model lineage?

We know about the lineage of datasets. Is there anything called "(ML) model lineage". What are all the works that had been remarkable regarding "model lineage"? There are few links ...
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Which approach best suits vector encodings?

I want to build a model that when given two vectors, outputs the probability of one vector being the encoded form of the other. I have 2 strategies for this: (Dataset available) I can directly feed ...
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4 votes
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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 ...
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8 votes
3 answers
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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 ...
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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 ...
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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 ...
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3 votes
4 answers
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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 ...
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Aside from specific training sets, what distinguishes the capabilities of different AI implementations?

(Disclaimer: I don't know much about ML/AI, besides some basic ideas behind it all.) It seems like ML/AI models can often be boiled down to statistics, where certain levers (weights) get fine-tuned ...
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3 votes
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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 ...
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3 votes
1 answer
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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?
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Literature on computational modelling involving neuronal ensemblies

Straying from the current trends in deep learning, there is an, arguably, interesting idea of neuronal ensembles possibly providing an alternative to the current "layered feature detectors" ...
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1 vote
1 answer
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What are some examples of functions that machine learning models compute?

My simple understanding of AI is that it is based on a mathematical model of a problem. If I understood correctly, the model is a polynomial equation and its weights are calculated by training the ...
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How to combine specific CNN models that work better at slightly different tasks?

I'm not sure how to describe this in the most accurate way but I'll give it a shot. I've developed a Inception-Resnet V2 model for detecting audio signals via spectrogram. It does a pretty good job ...
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How can I be sure that the final model, trained on all data, is correct?

The 'by the book' method of delivering final machine learning models is to include all data in the final training (including validation and test sets). To check robustness of my model I use randomly ...
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Is it good practice to save NLP Transformer based pre-trained models into file system in production environment

I have developed a multi label classifier using BERT. I'm leveraging Hugging Face Pytorch implementation for transformers. I have saved the pretrained model into the file directory in dev environment. ...
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1 answer
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Detect data in tables of roughly the same structure

I would like to train a model that serializes a table of nutrition facts into it's values. The tables can vary in form and colour, but always contain the same set of keys (e.g. carbs, fats). Examples ...
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1 vote
1 answer
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What are the main differences between a language model and a machine translation model?

What are the main differences between a language model and a machine translation model?
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2 votes
1 answer
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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 ...
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1 vote
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Training a model for text document transformation?

I have a bunch of text documents, split into source documents and transformed documents. These text documents have multiple lines and are edited at specific locations, in a specific way. I make use ...
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1 vote
1 answer
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How to work on different models for a given problem?

I am working on the MNIST data on my own. The idea is to use different values for the number of hidden layers, number of nodes in a given layer, etc. How do you organize these things while you are ...
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2 votes
1 answer
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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 ...
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2 votes
1 answer
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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 ...
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5 votes
3 answers
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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 ...
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3 votes
1 answer
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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?
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2 votes
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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 ...
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2 votes
1 answer
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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?
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2 votes
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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 ...
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1 answer
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YOLOv3 Model Structure: Why is filters = (classes + coords + 1) * num?

Here's a tutorial about doing custom training of YOLO (Darknet): https://medium.com/@manivannan_data/how-to-train-yolov3-to-detect-custom-objects-ccbcafeb13d2 The tutorial guides how to set values in ...
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What models will you suggest to use in Industrial Anomaly Detection and Predictive analysis on live streamed data?

I have been working on industrial data, that is fed live, I want to explore a few models which might suit for this the best. The data are KPI data from the manufacturing Industry.
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Will AI always depend on models and thus approximations?

In section 3 of the paper The Limits of Correctness (1985) Brian Cantwell Smith writes When you design and build a computer system, you first formulate a model of the problem you want it to solve, ...
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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 ...
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What are the properties of a model that is well suited for for high performance real-time inference

What are general best practices or considerations in designing a model that is optimized for real-time inference?
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2 votes
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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 ...
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3 votes
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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 ...
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2 votes
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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 ...
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