All Questions
597 questions
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8
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How should we choose a latent space learner for reinforcement learning?
I have a few questions about Auto Encoders in World Models/Reinforcement Learning algorithms:
The DreamerV3 (world models-based) architecture uses VQ-VAE to process visual data from the agent and ...
- 1
1
vote
1
answer
24
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How do neural scaling laws explain the improvements from LSTMs to Transformer based models
I was reading about a study on neural scaling laws from 2017 and they noted this as a summary. From Hestness, Joel; Narang, Sharan; Ardalani, Newsha; Diamos, Gregory; Jun, Heewoo; Kianinejad, Hassan; ...
- 279
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0
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25
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KL annealing for a VAE does not work, what now?
I am trying to train a variational auto-encoder where x ≈ f_VAE(x) = x_hat. In my real problem, I have 100-400 dimensional data that I would like to compress to ...
- 131
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25
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Autoencoder for semi-supervised anomaly detection - a choice of loss function, scaler and activation function
I am trying to build an autoencoder for semi-supervised anomaly detection on an intrusion detection dataset (CICIDS2017). The dataset has data with very wide range (like between 0 and 1+08).
I am ...
- 1
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0
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37
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When should you use a transformer and when LSTM, GRU and other Neural Networks?
There is a lot of information on the Internet that the transformer is better than RNN in everything, but is it true?
Examples:
«What if I need to translate words?»
«Generate text, images?»
«Play a ...
1
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1
answer
51
views
Is an autoencoder model encoder-only or encoder-decoder?
I'm writing up about different model architectures used in NLP, namely encoder-only models, encoder-decoder-only models, and have come across what seems to be a naming inconsistency. For decoder-only ...
- 111
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2
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55
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Is there any actual difference between these 2 definitions of a state value function?
The definition of the value function in TRPO paper is
\begin{align}
V_\pi(s_t) &= \mathbb{E}_{a_t,s_{t+1},\ldots} \left[ \sum_{l=0}^{\infty} \gamma^l r(s_{t+l}) \right], \\[10pt]
a_t &\sim \pi(...
- 139
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0
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22
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VAE to compare two datasets
I have two cell-by-gene matrices, each representing gene counts for cells in sample 1 and sample 2. I'm interested in identifying common gene expression patterns across both samples. These patterns ...
- 111
1
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0
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29
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Any tutorials/courses to learn variational autoencoders on tabular data?
I aim to use variational autoencoders (VAE) to find interpretable latent spaces for genetic data. So, I need to understand how they work, what activation function to use, etc. But all tutorials and ...
- 111
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1
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108
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Does Machine Learning focus on discriminative AI while Deep Learning also focus on generative AI?
I know that Deep Learning is subset of Machine learning
But is it correct that classical ML algorithms mainly focus on implementing Discriminative AI while DL algorithms implement both Generative AI ...
- 181
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2
answers
107
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What do we mean by "AI is correlated"?
From Wikipedia
Causal AI is a technique in artificial intelligence that builds a causal model and can thereby make inferences using causality rather than just correlation. One practical use for ...
- 117
2
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2
answers
556
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Is it easier to use back-propagation or genetic algorithms to teach an artificial intelligence?
I am making a very simple neural network for a school project, and I would like to know what the best and easiest way to "teach" a neural network would be. From what I know, backpropagation ...
- 23
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1
answer
66
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How to reconstruct a new image using pre-trained autoencoder?
When a single image is assigned for training, an auto-encoder should be able to gradient-descend and find the full set of satisfactory weights that will reconstruct this image.
Suppose a second image ...
- 157
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1
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93
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model.fit fails using keras sequential (slice index ### of dimension 0 out of bounds) [closed]
this is the most simple model I can think of for my data yet I can't use the fit function, it gives an error.
the desired procedure is to make a simple autoencoder : from 576 nodes to 64 then back to ...
- 23
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0
answers
21
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Are there leaderboards/tables/stats that compare inference times between close-sourced LLMs such as GPT 3.5/4 and Claude?
https://huggingface.co/spaces/optimum/llm-perf-leaderboard is great to compare inference times between LLMs but it misses close-sourced LLMs such as GPT 3.5/4 and Claude.
- 3,267
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2
answers
1k
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What is the difference between densenet and resnet?
Is the only difference between the two how the skip connection is combined? Resnet combines skip connections through addition and Densenet through concatenating.
The Densenet paper appears to be ...
- 233
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0
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21
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Is it reasonable to ask for the same time-regularity of the high and low dimensional signals?
Consider we are dealing with sequential data sampled from a continuous time signal $x(t)\in \mathbb{R}^n$, so that the dataset will look like $\{x_0,x_1,…,x_n\}$, with $x_i= x(t_i)$.
Assume that we ...
- 11
1
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1
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44
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Is "The Dimpled Manifold Hypothesis" correct to say this about autoencoders?
This quite famous paper states page 3 that:
The (well-known) fact which underlies the new conceptual framework is that all the natural images are located on or near some low-dimensional manifold (as ...
- 13
2
votes
1
answer
648
views
What are alternatives to PCA for time series data?
I have some data (20 stock price time series) and want to compare different approaches for dimensionality reduction other than PCA (I want to fit only 2 variables in my AR model). I've tried ...
- 31
1
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1
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98
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Is there any advantage of genetic algorithm (or programming) over Neural Networks? [closed]
I am planning to switch from neural networks to genetic algorithms (GA) and programming (GP).
One of the main hassles of working with neural networks is that it requires a large amount of training ...
- 339
1
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1
answer
69
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Why aren't encoders decoders trivial?
If you have an encoder decoder with 10 input neurons for X then 3 hidden in one layer then another 10 in the output which are the same X is it not trivial to set the weights whatever you want and w1 ...
- 31
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22
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Does the fixed context in attention mechanism is accquired after getting the decoder hidden layer of the first hidden state?
here, the fixed context vector (ci) is used for the decoder model, why the decoder model also used by the attention weights. On the first (c1), does that mean the decoder does not have context ? (i = ...
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2
answers
61
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Should I define my problem as image segmentation or detection?
I have a problem and have to decide wether it's an object detection or object segmentation problem. I want to use Yolov8 for training. We already have hundrets of images but they aren't labeled yet. ...
- 113
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0
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29
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Is there a better way for my CNN to handle random values?
I made an autoencoder to, ideally, turn an image into seemingly random numbers(Using a loss that determines randomness) and turn those random numbers into the original image. The results were kind of ...
1
vote
1
answer
1k
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When to use Pruning, Quantization , Distillation and others when optimizing speed
I want to understand how to optimize models for inference speed and am seeking some advice and best practices for the same.
I am a little bit aware of the concepts of pruning, quantization, and ...
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4
votes
2
answers
3k
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What are the differences between seq2seq and encoder-decoder architectures?
I've read many tutorials online that use both words interchangeably. When I search and find that they are the same, why not just use one word since they have the same definition?
user78615
1
vote
1
answer
133
views
Why are these two implementations of the $\epsilon$-greedy policy different?
According to the book Reinforcement Learning An Introduction, the epsilon greedy policy can generally implemented as:
$$
\pi(a|s) =
\begin{cases}
\frac{\epsilon}{|A|} + 1 - \epsilon & \text{if } ...
- 195
2
votes
1
answer
541
views
What are the similarities between Q-learning and Value Iteration?
This is the explanation of value iteration in our notes where you keep applying bellman optimality equation till it stops changing and then acting greedily wrt the value function gives the optimal ...
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1
answer
131
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Is it possible to build a convolutional autoencoder with fully connected bottleneck with low dimension?
I want to do a project with a small size image dataset (the size is about 50*50). There's another similar dataset, and I want to prove that the datasets are different. I built a convolutional ...
- 3
1
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0
answers
81
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Replicating conv autoencoder for anomaly detection, very blurry reconstructions
I’m trying to train an autoencoder on the hazelnut dataset of MVTec AD for reconstruction to detect anomalies.
I’m am trying to replicate the results of this study:
https://arxiv.org/pdf/2008.12977....
- 165
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2
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926
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From where do the Encoders in Transformers gets Input Embedding from?
In Transformers Encoders, from where do the Encoders get Input Embedding from?
So when a sentence is given to a transformer-based model it first tokenises the sentence and each token is mapped with ...
- 101
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25
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Seeking methods to incorporate arbitrary actuator faults for Control Optimization
I am working on a problem where a control method, backed by a Neural Network (NN), dictates the movement of a 1D actuator to influence a specific process. This actuator can move linearly within a set ...
1
vote
1
answer
48
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Is there validation data in K-fold cross-validation?
We know that in machine learning the dataset is divided into 3 parts: training data, validation data and test data.
On the other hand, K-fold cross-validation is defined as follows:
the dataset is ...
- 1,115
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1
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247
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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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0
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69
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Why does each row of data have the same bottleneck features in the Autoencoder after training?
I was training an autoencoder for anomaly detection and I wish to extract the bottleneck features of the encoder for K-NN. The model architecture is as such:
...
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2
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1
answer
146
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Why can Variational Autoencoders (VAEs) approximate arbitrary distributions?
I am trying to reason to myself why is it that VAEs can approximate arbitrary probability distributions even though 𝑞𝜙(𝑧|𝑥)
and 𝑝𝜃(𝑥|𝑧)
are Gaussian.
I understand that the parameters are ...
0
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1
answer
140
views
Which search algorithm expands nodes closest to the goal?
I want to know which search algorithm among A* and Best-First Search and Greedy First Search expands nodes closest to the goal. I have three opinions about A* and Best-First Search and Greedy First ...
- 3
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1
answer
169
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What is the difference between A/B testing and Reinforcement Learning?
I was learning ML, and I learnt a new section called, Reinforcement Learning. After some research on web, I found that it is a trial and error technique by which ...
0
votes
1
answer
34
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How to learn Categorial Embeddings in Unsupervised Learning?
I want to cluster mixed-type tabular data, for the categorial columns I want to use Categorial Embeddings and then an Autoencoder Network before clustering with KMeans or similar.
Now, when I want to ...
- 1
0
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1
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192
views
How are the intuitions and mathematics of attention mechanisms related to those of PageRank?
Excuse me if you find this question too vague and not fitting to this forum and feel free to close it. The overall goal of my question is to get a better intuition of the attention concept and ...
4
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1
answer
289
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How does Monte-Carlo Tree Search Compare to MCMC?
Monte-Carlo Tree Search was the method used for AlphaGo my understanding is: it would randomly search the state space of possible moves where the probability of choosing a move was proportional to the ...
- 454
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1
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202
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For a transformer decoder, how exactly are K, Q, and V for each decoding step?
For a transformer decoder, how exactly are K, Q, and V for each decoding step?
Assume my input prompt is "today is a" (good day).
At t= 0 (generation step 0):
K, Q, and V are the projections ...
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0
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26
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How do I make an autoencoder and make it work on extracting the feature of a stationary wave?
I have a project to complete in a day, and I know that doing it in a day is a bit far-fetched. The problem is this -
"Design an autoencoder with two neurons as the constriction, multiple hidden ...
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1
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143
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Can I implement a sklearn model inside a Pytorch nn.Module? [closed]
I am making a custom Pytorch model that at some point, clusters a latent space that was created by another, previous routine of the model (Autoencoder).
In a bit more detail, my model is a regular ...
4
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1
answer
2k
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What's the difference between GPT3.5 and InstructGPT?
I read about the different model series in GPT3.5 here - https://platform.openai.com/docs/models/gpt-3-5
At the beginning of the page, it mentions to look at https://platform.openai.com/docs/model-...
- 41
1
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1
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85
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Are on-policy algorithms always better than off-policy ones?
I am studying RL and I have a question: Are on-policy algorithms always better than off-policy ones?
0
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1
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59
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Why is the variational lower bound is easier to compute than the original marginal distribution?
Why is the ELBO of $p(x)=\int p(x|z)p(z)\mathrm{d}z$ easier to compute/estimate than the expression itself? Can we compute this quantity itself through sampling in the same way? I understanding that ...
- 101
1
vote
1
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812
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Latent Diffusion Model Can't Learn the Latent Space of a VAE for the MNIST-Fashion Dataset
I'm currently playing around with LDMs on the MNIST-Fashion dataset. I thought the VQVAEs used in the original paper were a bit overkill for what I'm doing (and I don't fully understand how they ...
- 167
0
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1
answer
326
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How does mixing and matching encoders and decoders work in image segmentation?
I had a conceptual questions regarding architectures. I am using this git hub repository that allows one to quickly put together a segmentation pipeline. In reading the readme one thing that has me ...
- 103
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1
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517
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How to generate new data using VAE?
I have built the following function which takes as input some data and runs a VAE on them:
...
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