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 task/model to use to locate textblock in pdf that matches regulation rules
I have a PDF written to disclose different corporate policies and governance implementations. It's lot more but you get the gist.
On the other hand, I have a set of rules and regulations that dictates ...
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Neural network dynamic input shape vs fixed input shape in input layer for handling NULL values
I have binary classification problem, we know that the output layer will be scalar or dense a.k.a. 1 unit neuron with sigmoid as function activation. 1 means is the subject will die, while 0 means ...
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Can I do incremental learning with different loss function in neural networks?
I have a saved tensorflow neural network model. I was wondering if it's possible to incrementally train the model but with different nt loss function.
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Which kind of model should I be looking at if I'm trying to estimate volume for 3D objects?
I'm trying to navigate the world of LLMs and understand which classes of models are ideal for which use case. So I'm wondering, for problems related to 3D volume estimation, which class of models are ...
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Transfer learning using pretrained tensorflow object detection model [closed]
I am new to AI/ML and wanted to seek guidance as I am totally lost. I will simplify my issue as follows:
Let's say I would like to detect apples and oranges in images.
I would like to leverage a pre-...
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Seeking Advice on Local AI Model for Analyzing Personal Files
I'm embarking on a project to develop an application for personal use that can assist me in retrieving specific information from my files. I'd like to be able to ask questions like "What was my ...
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What is the difference between Machine Learning model, algorithm and hypothesis?
I'm fairly new to Machine Learning field and still to grasp the basics, so this question may seem very stupid, but what is the difference between Machine Learning model, algorithm and hypothesis?
Like ...
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What is the best Text-to-speech model available open-source?
I tried a couple of different websites and libraries. Also found this topic from 3.5 years ago - What are the current open source text-to-audio libraries?
It looks like nobody published anything in ...
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Stable Diffusion: Is it possible to merge specific token weights from one model into another?
I have three models: A, B, C
I merged A+C and B+C together.
A+C gives me great results for certain things but is not so good with specific other things. B+C is great with things that A+C is not, but ...
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Can I combined the trained model between different source but same model structure?
Here I got two different deep-learning models that were trained by LSTM and time-series data.
The data is the usage percentage of CPU from two different computers.
Each computer job was the same. It ...
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What is actually being saved in the file when you save a model? For example a Tensorflow SavedModel file [closed]
I'm building a feature for my application that requires reading the properties of a saved ML model file (after it's trained). However, as I am pretty new to this field, I don't really understand the ...
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Is it possible to write/build an AI model without using Frameworks? [closed]
I'm a relatively newbie in this world of Artificial Intelligence, although I am able to use frameworks such as Tensorflow and also understand the general concepts behind training weights and ...
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What is the relation between any suitable measure of model complexity, number of training examples and network size in deep learning?
What is the relation between any suitable measure of model complexity, number of training examples and network size in deep learning?
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For given units of a measure of model complexity, how many examples do we need to train a network to get the model right and generalize?
For given units of a measure of model complexity, how many examples do we need to train a network to get the model right and generalize?
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Change in the Structure of Deep Associative Networks since their Inception
I am trying to understand how far associative networks have evolved from the Hopfield network. A lot of the research is only available to institutions which is why I deferred to this stack exchange. ...
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When retraining a model are you adding a layer or changing existing node values?
I am a little confused, lets take Stable Diffusion for example. I download the SD model and I then retrain for a specific art style. Am I adding a layer of nodes on top of SD or am I changing all the ...
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Why do transformers have a fixed input length?
From what I understand, Transformer Encoders and Decoders use a fixed number of tokens as input, e.g., 512 tokens. In NLP for instance, different text sentences have a different number of tokens, and ...
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Model Suggestions for Real Life local Hospital Data [closed]
I'm doing a machine learning project and was looking for suggestions. It's meant to get the date, household, age, sex, doctor, date of the medical appointment, and type of medical appointment of a ...
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Why "Good Model" that performs great on holdout validation data fails on production data
I have this binary regression model that has ~500 futures with an unbalanced dataset with the following results.
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How to handle out-of-bound values in Production data?
So I have this model but the data may vary. And it is virtually impossible to always have the values in bounds. If I do I`d have to use larger period leading to concept shift which is worse.
The ...
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What is the next step in top-down brain simulation after spiking neural networks?
This paper from Yamazaki et al. describes a 68 billion spiking neural network model of the cerebellum. The simulation was about 600 times slower than real time, and the cerebellum is perhaps one of ...
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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), ...
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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