Questions tagged [dimensionality]
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12 questions
1 vote
1 answer
74 views
curse of dimensionality v.s. volume of n-dimensional ball v.s. n-dimensional vector embedding space
We know that the performance of machine learning model become worse if we feed the model with a few features and many features (high dimensional data). This is known as the curse of dimensionality. ...
1 vote
1 answer
88 views
What is the channel dimension other than color representation in Conv2D? Shall I use Conv3D instead?
I have grayscaled image, specifically medical data ultrasonography. In the context of medical domain, there are techniques to capture data that called "View". It's like point of views of ...
0 votes
0 answers
59 views
Do neural networks have a perception of space, regardless of dimensionality?
Suppose I have a model M which outputs a three-dimensional tensor of size 3x3x3. I have another model N which outputs a one-dimensional tensor of size 27. Train both models on some arbitrary objective ...
- 1
0 votes
1 answer
951 views
Are LLM parameters synonymous with dimensions?
For example, would a Large Language Model (LLM) with parameter size 140 Billion have 140 Billion dimensions as defined in deep learning as the number of nodes in the input layer? Another way to ask ...
- 101
2 votes
1 answer
238 views
Is there any advantage to providing multi-dimensional input to torch modules?
Most layer types in torch.nn such as torch.nn.Linear accept input with more than one dimension. Is there any advantage in doing so if you can shape your data to represent a certain arrangement in ...
- 23
0 votes
1 answer
391 views
How many directions of gradients exist for a function in higher dimensional space?
Gradients are used in optimization algorithms. Based on the values of gradients, we generally update the weights of a neural network. It is known that gradients have a direction and the direction ...
- 4,172
0 votes
2 answers
98 views
How to visually or intuitively understand single element multi-dimensional tensors?
Consider the following code in PyTorch ...
- 4,172
0 votes
1 answer
169 views
How does t-SNE preserves embedding orders?
According to the triplet loss Wikipedia page: t-SNE (t-distributed Stochastic Neighbor Embedding) preserves embedding orders via probability distributions, whereas triplet loss works directly on ...
0 votes
0 answers
143 views
Huge dimensionality of input and output — any recommendations?
At work there is an idea of solving a problem with machine learning. I was assigned the task to have a look at this, since I'm quite good at both mathematics and programming. But I'm new to machine ...
- 1
1 vote
1 answer
104 views
Can I do state space quantization using a KMeans-like algorithm instead of range buckets?
Are there any reference papers where it is used a KMeans-like algorithm in state space quantization in Reinforcement Learning instead of range buckets?
- 947
1 vote
0 answers
67 views
How to handle a high dimensional video (large number of frames per video) data for training a video classification network
I have a video dataset as follows. Dataset size: 1k videos Frames per video: 4k (average) and 8k (maximum) Labels: Each video has one label. So the size of my input will be (N, 8000, 64, 64, 3) ...
- 11
3 votes
2 answers
119 views
Problem extracting features from convolutional layer where the dimensions are big for feature maps
I have trained a convolutional neural network on images to detect emotions. Now I need to use the same network to extract features from the images and use them to train an LSTM. The problem is: the ...
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