Questions tagged [deep-learning]
a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hidden layers), but also with some sort of Probabilistic Graphical Models.
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How to get the relationship between a result and a data
I have big data (at least 10 variables each row and millions of lines) and result (for example if y=0.2 and x=0.4 and z=0.9 so there is a system failure).
I need to find the relationship between the ...
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How should I proceed with this type of dataset and requirement?
Suppose I have a dataset where I want to give input of up-to 30 lines (can be variable with min 5 available) these lines are timestamps of every minute and want my model to predict output of next 5 ...
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What preprocessing should I do to a multiclass segmentation mask?
I am working on a segmentation problem. My masks are tensors with a shape of (4767, 192, 192, 1) --> (num_img, height, width, number of channels). Each mask contains 13 different pixel values (0, 1,...
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Why are normal distributions so important in deep learning?
I am currently reading on normalization/standardization techniques as well as batch normalization in deep learning and I don't really understand why normal distributions are so important inside deep ...
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How can I interpret the results of my loss functions?
I use yolov8 for object detection. The results for my training look like this:
As you can see in general my validation losses are quite higher than my training losses. Here the comparison of box_loss ...
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Investigating the Impact of Additive Gaussian Noise on EEG Signal Classification: Analyzing the Relationship between Augmented and Original Data
Definition: I have conducted research on EEG signal classification, specifically focusing on distinguishing between two different classes using raw EEG signals. Data availability poses a significant ...
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Why apply min-max normalization to each individual mel spectrogram for a training set?
I am watching a tutorial on using mel spectrograms to classify the audio's genre via CNN. My question is why apply local min-max normalization to each individual mel spectrogram? What I mean by local ...
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Time series prediction is not working at the first step of test data
I am doing some time series prediction. I am using the historical data and temperature to predict the energy for the next three days. The way I create the training data is same as LSTM, i.e I'm using ...
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Intuition behind Jensen Shannon divergence between deep features of two images?
Jensen Shanon divergence is mainly used to determine the divergence between two probability distributions. Can it be used to calculate the difference between the deep feature vectors of two images?
I ...
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Is this time-series problem more a multi-classification or regression problem?
I have 188 different drilling datasets with two columns temperature and the target column thermal conductivity respectively. The drilling process includes drilling on four different materials on top ...
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Labelling spectrograms
Currently I'm working on a ML project, just need an information, is there any tool that is present that can load audios file and generates spectrograms as well as an option to annotating/ label the ...
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Seeking Feedback on Methodology for Implementing Supervised Classification ML Algorithm for Customer Satisfaction Prediction
I'm currently designing a methodology for implementing a supervised classification ML algorithm and seeking guidance to ensure I'm heading in the right direction.
The problem I'm addressing involves ...
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Can DeepSort be made to track objects beside people?
As far as my understanding goes, the model used for feature extraction in DeepSort is specified as the first argument of the function create_box_encoder in the file ...
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Dueling DQN with varying number of actions
I have an RL problem, where the number of actions depends on the state. Furthermore, each action-value computation requires action information in the form of a high-dimensional, continuous vector in ...
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I'm looking for a dataset on enviroment monitoring
I'm looking for a dataset related to environmental monitoring, made up of values obtained from various types of sensors (such as temperature, pressure, CO2...etc) for the purpose of a classification ...
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About the last decoder layer in transformer architecture
So, in the decoder layer of transfomer, suppose I have predicted 3 words till now, including the start token then the last decoder layer will produce 3 vectors of size d-model, and only the last ...
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Sentiment Analysis on the first 100 words of a very large essay of 500/700 words
Are there any potential issues on performing sentiment analysis using the first 100 words of a very large essay that is of 500 to 700 words. I am having to do this because since most transformer ...
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How can i understand multiple lstm cells by unrolling?
I do not Unterstand the concept of multiple units in lstm.
If i have an lstm layer with 64 cells, how would be the cells applied to each time step by unrolling.
My understanding is that each time step ...
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Exploring the Concept of Gradient Flow
Understanding the concept of "Gradient Flow" can be quite difficult as there is a lack of widely recognized and clearly defined resources that provide a comprehensive explanation. Although ...
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What exactly is Gradient norm?
I found that there is no common resource and well defined definition for "Gradient norm", most search results are based on ML experts providing answers which involves gradient norm or papers ...
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Different validation sets give very different results. What can be the reason?
I have ~78k microscopy images of single cells, where the task is to classify for cancer (binary classifier). The images are labeled according to which patient the data came from. I do the train-val ...
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torchmetrics BinaryMatthewsCorrCoef outputs 0 if target and prediction contains only one case either positive or negative case
I stared using MCC(Matthew's correlation coefficient) metric. But getting unexpected values when the given target and pred contains only one case either positive or negative (case - 1), The output of ...
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How to monitor training of text generation models?
I'm finetuning a pretrained Huggingface model based on Transformers for a downstream Text Generation task, but I have doubts on how the fine-tuning process should be monitored:
In classification, I ...
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How to deal with systemic gaps in timeseries data
To be clear this question is not how to input missing data, but how to treat an exchange dataset that will not ever have data on weekends and occasionally on market holidays. I'm working with the ...
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Modeling uncertainty from known physics
I have an equation given by:
$$
\frac{\mathrm{d} s}{\mathrm{d} t}=4a−2s+\lambda(s)
$$
where, $a$ is an input constant and $\lambda$ is a non-linear term that depends on $s$.
I know that the true ...
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Extract phrases/keywords that are SIMILAR to a python list of keyword/phrases, from a document
EDIT : If I had to match single worded phrases, I could first tokenize the text from the document and then calculate the cosine similarity of all the tokens with all the keywords from the ...
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In recommendation systems, for methods that use backproporgation to get user feature, do they need to retrain the whole model when a new user is added
I'm tying to learn about recommendation systems recently. I have some deeplearning background so I focused more on machine learning based methods for recommendation systems. I see that a lot of paper ...
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val_accuracy and val_loss not changing while training transformer
recently i have been trying to learn transformer and using it in caption-generator model.
While training for 4 hours val_loss and val_accuracy did not change. loss and accuracy for train_data was ...
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Best practice labeling grouped anomalies for object detection
I would like to train object detection model (e.g. YOLO) for images that contain anomalies. The anomalies are essentially the holes in a surface of different sizes. How do I label correctly such ...
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High accuracy on test and validation data but still can not predict on real data
Hello i am having a classification between two classes A and B and i have trained CNN model. I have high accuracy on all three set of data i.e training (98.7%) validation (99.3%) and test(98%) but ...
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How do I interpret GRAD-CAM's feature attribution to time series zero-padding in a CNN classifier?
Problem setting: MTS Classification with CNN architecture
I have a multivariate time series (MTS) dataset that contains 30 features. The goal is to solve a classification problem on this MTS dataset. ...
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Importance of sentinel token placement in T5
There is this paper that I have been trying to reproduce (https://arxiv.org/pdf/2205.11482.pdf) as part of my master's thesis. It uses T5 to learn facts from the training set where either the object ...
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Influence functions on neural networks: Help with understanding of result and derivation
I'm working through a paper titled "Understanding Black-box Predictions via Influence Functions" where they introduce the notion of influence functions from robust statistics to approximate ...
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How can I prevent mobilenetv3 from overfitting with less data?
So I have around 462 images and I can't really get more images. I am using a pretrained model of MobileNetV3 with the respective weights. I am facing a huge problem of overfitting and no real solution ...
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How big is the threshold that is usually used in determining the convergence of loss values in deep learning?
In deep learning, one way to determine whether the training has converged is to observe the movement of the loss values over iterations or epochs. One can choose any $\epsilon$ threshold and any ...
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MobileNet validation loss not decreasing over time
I am trying to train a MobileNetV2 on a custom dataset, to image Classification task.
Cardinality is 864 images, split in 70%/20%/10%, balanced between the 3 different classes.
Weights are pre-loaded ...
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Embedding vector of MaskRCNN (Resnet with FPN)
I have a MaskRCNN model for instance segmentation with Resnet 50 - FPN backbone trained in detectron2. And I want to extract the embedding/feature vectors for visualizing input and hopefully detecting ...
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Unable to reproduce result for CIFAR10 with ResNet50 backbone and SIMCLR self-supervised algorithm
I am recently working on self-supervised learning, particularly simclr paradigm. I found it hard to reproduce results on cifar10 with linear evaluation. I now get round 60%, while the paper reports 80%...
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pillow cannot import / conda unexpected error
I created a conda environment previously and it worked fine with python and tensorflow. At that stage I used anaconda.
On a fresh install I am using miniconda since I now understand the conda commands ...
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Transformers doubt
Basically here the $Q$,$K$ and $V$ are passed through a linear layer to obtain the actual $Q$,$K$ and $V$ for self attention mechanism and then we concatenate all of it.
My doubt is, I thought the $Q$,...
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Understanding the ResNet paper
I am having trouble understanding the mathematical meanings behind the notations in the ResNet paper:
I believe that the function we are trying to optimize is the residual which is denoted as $\...
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can training for too long lead to overfitting? I am not sure about the specifics of this
does training for a large number of epochs lead to overfitting? I am concerned about this as I am getting an accuracy of nearly 1 on val and training dataset when I am training for 50 epochs
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How does variational autoencoders actually work in comparison to GAN?
I want to know about how variational autoencoders work. I am currently working in a company and we want to incorporate variational autoencoders for creating synthetic data. I have questions regarding ...
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2 basic doubts on time series
Suppose say, I have to predict the cost of stock market. I have previous data and I have made it into the following Structure :
(Xt-3,Xt-2,Xt-1)--->(Xt=Yt)
Now the order of the above data points if ...
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Working with time series data with several times stamps on a dates, and implementing machine learning
I'm trying to implement predictive analytics on a production data. my goal is to predict next downtime, it's reason and issues.
My data looks like below;
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Help with trying to reimplement ADEC paper (clustering using AE and GAN). Encoder loss is not decreasing
I am trying to reimplement the ADEC paper (https://arxiv.org/abs/1909.11832) which mixes an autoencoder with a GAN network, but I am facing the issue that the encoder loss does not decrease.
I have ...
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Given a 4d tensor for time series predicition
I have multiple time series datasets, which i want to train to an lstm model.
The shape of the training data is (735,2,5,4). 735 are the time steps, 2 are the two time Series datasets, 5 are the ...
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what is the error here ? time series sliding windows
Given a list where each element is a dataframe, i want to create sliding windows in order to train a lstm model, but the problem is an error occurs.
Each dataframe is a time series with the 4 columns ...
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Unsupervised SimCSE not Learning Good Representations
I'm using the unsupervised SimCSE approach to train a semantic embedding model on a corpus of tag documents. That is, ordered lists of tags concatenated into white-space seperated strings. I use a ...
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What is the definition of convergence in the context of deep neural networks?
Suppose I have a feed forward neural network which approximates a value, say $Y_0$. The analytical value of $Y_0$ is given. The plot of the network approximation of $Y_0$ each step is given as follows....
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