All Questions

Filter by
Sorted by
Tagged with
1
vote
0answers
15 views

What is a example showing that the tree-based variant for the greedy best-first search is incomplete?

I understand that a tree-based variant will have nodes repeatedly added to the frontier. How do I craft an example where a particular goal node is never found. Is this example valid. On the other ...
1
vote
0answers
18 views

How can I find the similar non-zero connections between different levels of sparsity of the same network?

I am pruning a neural network (CNN and Dense) and for different sparsity levels, I have different sub-networks. Say for sparsity levels of 20%, 40%, 60% and 80%, I have 4 different sub-networks. Now, ...
0
votes
0answers
10 views

How to install tensorflow-gpu 2.2 with Nvidia George GTX 1650 (notebook) [closed]

I want to know how do we install tensorflow 2.2 GPU version with NVidia GTX 1650 laptop version graphics card on Windows 10. I want to install in anaconda and I am having a trouble in making a new ...
0
votes
0answers
13 views

How to save and load a Q-Learning Agent [migrated]

I know this may sound nooby, but how do I save a Deep Q-Learning agent's progress? I mean when I close at i.e. episode 500 when my agent is trained and I restart (in my case a pygame) my agent is ...
1
vote
1answer
25 views

What are the main differences between sparse autoencoders and convolution autoencoders?

What are the main differences and similarities between sparse autoencoders and convolution autoencoders? When should one be preferred over the other? What are their applications? (References are ...
0
votes
0answers
6 views

VAE generates blue images [migrated]

I'm trying to train a VAE to generates celebA faces. the problem I'm facing is that the model only generates blue faces and I'm not sure why and how to fix it. The encoder: ...
0
votes
0answers
19 views

How can I increase the exploration in the Proximal Policy Optimation algorithm?

How can I increase the exploration in the Proximal Policy Optimation reinforcement learning algorithm? Is there a variable assigned for this purpose? I'm using the stable-baseline implementation: ...
1
vote
1answer
56 views

Can deep reinforcement learning algorithms be deterministic in their reproducibility in results?

I ran a deep q learning algorithm (DQN) for $x$ number of epochs and got policy $\pi_1$. I reran the same script for the same $x$ number of epochs and got policy $\pi_2$. I expected $\pi_1 $ and $\...
0
votes
0answers
8 views

Is there a difference between using 1d conv layers and 2d conv layers with kernel with size of 1 along other than time dimension?

Let's assume I use convolutional networks for time-series prediction. Data I feed to the network have 1 channel depth, height of number of periods and number of features is the width, so the frame ...
0
votes
0answers
15 views

My LSTM text classification model seems not learn anything in early epochs

I am trying to use LSTM to do text classification and monitor the training process with tensorboard. But it seems that this model doesn't learn anything in early epochs. Is it normal for LSTM networks?...
0
votes
1answer
18 views

Where to go next in AI? [closed]

So i have learned calculus 1 and calculus 2 Python, java, and c++ Some statistics However, since i am in a 2 year college, and the college doesn't have any classes on AI Development I don't know ...
1
vote
1answer
27 views

What's the difference between LSTM and GRU?

The main difference between these two structures lies in the number of gates and their specific roles. The role of the Update gate in the GRU is very similar to the Input and Forget gates in the LSTM. ...
4
votes
2answers
285 views

How can we compute the ratio between the distributions if we don't know one of the distributions?

Here is my understanding of importance sampling. If we have two distributions $p(x)$ and $q(x)$, where we have a way of sampling from $p(x)$ but not from $q(x)$, but we want to compute the expectation ...
0
votes
1answer
21 views

What are the most common feedforward neural networks?

What are the most common feedforward neural networks? What kind of inputs do they receive? For example, do they receive binary numbers, real numbers, vectors, or matrics? Is there such a taxonomy?
1
vote
0answers
37 views

How do I set up rewards to account for unmanned aerial vehicle crashes?

I am working on a project to implement a collision avoidance algorithm on a real unmanned aerial vehicle (UAV). I'm interested in understanding the process to set up a negative reward to account for ...
0
votes
1answer
28 views

How to add more than 1 agent in one generation with Q Learning

Sometimes the agent learns a bit slow and you want to have multiple agents in one generation. And at each episode you'll draw on the screen only the best of them or all of them. How is that possible? ...
0
votes
1answer
21 views

How to add a pretrained model to my layers to get embeddings?

I want to use a pretrained model found in [BERT Embeddings] https://github.com/UKPLab/sentence-transformers and I want to add a layer to get the sentence embeddings from the model and pass on to the ...
1
vote
0answers
7 views

How can Cat Swarm Algorithm (CSO) used for feature selection?

Cat swarm optimization (CSO) is a novel metaheuristic for evolutionary optimization algorithms based on swarm intelligence which proposed in 2006. See Feature Selection of Support Vector Machine Based ...
0
votes
0answers
10 views

How to set only the modified weights for each convolutional layers? [migrated]

I am currently doing some experiments on modifying the weights and not of the bias for each convolutional layers of a model. For each of the layers of the model, I used ...
1
vote
1answer
33 views

How can I model and solve the Knight Tour problem with reinforcement learning?

I've read about the Knight Tour problem. And I wanted to try to solve it with a reinforcement learning algorithm with OpenAI's gym. So, I want to make a bot that can move on the chess table like the ...
1
vote
0answers
22 views

If the output of a model is a ridge function, what should the activation functions at all the nodes be?

I have the following assignment. I can't understand the b part of this question in my assignment. I have completed the 1st part and understand the maths behind it, but the 2nd part has me stumped. I ...
0
votes
0answers
33 views

What are some references that discuss questions asked during deep learning interviews?

I was wondering whether anyone could point me to some references that discuss Deep Learning interview questions for Deep Learning Engineer Role, from the most basic through the more advanced. Answers ...
0
votes
1answer
23 views

What is the advantage of using Google's Coral over Nvidia's Xavier?

I was reading about the possibility of using Google's Coral for deep learning-based object detection and image classification. I heard it has a good speed in terms of frames/sec. I also read that ...
1
vote
0answers
26 views

Does the reduction of the dimensions over multiple layers allow more details to be stored within the final representation?

From : https://debuggercafe.com/implementing-deep-autoencoder-in-pytorch/ the following autoencoder is defined ...
1
vote
0answers
29 views

What should the action space for the card game Crib be?

I'm working on creating an environment for a card game, which the agent chooses to discard certain cards in the first phase of the game, and uses the remaining cards to play with. (The game is Crib if ...
1
vote
0answers
24 views

Applications of polar decomposition in Machine Learning

Assume there exists a new and very efficient algorithm for calculating the polar decomposition of a matrix $A=UP$, where $U$ is a unitary matrix and $P$ is a positive-semidefinite Hermitian matrix. ...
3
votes
1answer
63 views

How does publishing in the deep learning world work, with respect to journals and arXiv?

Let's say I implemented a new deep learning model that pushed some SOTA a little bit further, and I wrote a new paper about for publication. How does it work now? I pictured three options: Submit it ...
1
vote
0answers
21 views

How estimate the minimum size of an autoencoder to overfit the training data?

Given e.g. $1$M vectors of $1000$ floating points each, where every point in vectors is sampled from a uniform distribution between $-1$ to $1$, how to estimate the minimum network size required ...
0
votes
0answers
16 views

Do think forging results in Deep Learning papers is a widespread phenomena as some people have claimed lately? [closed]

I've been hearing lately claims that Deep Learning has become a hoax and that many papers forge results. Do you agree with such claims?
1
vote
1answer
30 views

Is it possible to have the latent vector of an auto-encoder with size 1?

Given e.g. 1M vectors of $1000$ floating points each, where every point in vectors is sampled from a uniform distribution between $-1$ to $1$: Is it possible to have the bottleneck of the AE network ...
0
votes
0answers
8 views

How to distinguish below good/VS bad wall with image good and bad wall classification [closed]

The first Image is Bad Wall and second one is good wall example
3
votes
1answer
92 views

Why is update rule of the value function different in policy evaluation and policy iteration?

In the textbook "Reinforcement Learning: An Introduction", by Richard Sutton and Andrew Barto, the pseudo code for Policy Evaluation is given as follows: The update equation for $V(s)$ comes from the ...
1
vote
1answer
25 views

What are mono-variable and multi-variable neural networks?

In this document, the terms "Redes Neuronales estáticas monovariables" and "Redes Neuronales estáticas multivariables" are mentioned. What are mono-variable and multi-variable neural networks? Is it ...
0
votes
1answer
28 views

Can I find a mapping that minimizes the maximum distance ratio of certain vectors?

Let's say we have several vector points. My goal is to distinguish the vectors, so I want to make them far from each other. Some of them are already far from each other, but some of them can be ...
1
vote
0answers
18 views

How do we choose the filters for the convolutional layer of a convolution neural network?

Since the hidden layers of a CNN work as a trainable feature extractor, more detailed content based on a larger number of pixels shall require bigger filter sizes. But for cases where localized ...
2
votes
1answer
66 views

How do I derive the gradient with respect to the parameters of the softmax policy?

The gradient of the softmax eligibility trace is given by the following: \begin{align} \nabla_{\theta} \log(\pi_{\theta}(a|s)) &= \phi(s,a) - \mathbb E[\phi (s, \cdot)]\\ &= \phi(s,a) - \sum_{...
3
votes
1answer
30 views

If agent chooses an action that the environment can't operate, how should I handle this situation?

I'm building a really simple experiment, letting an agent move from the bottom-left corner to the upper-right corner on a 3x3 squared paper. I plan to use DQN to do this. I'm having trouble handling ...
2
votes
0answers
16 views

Can you use transformer models to do autocomplete tasks?

I've researched online and seen many papers on the use of RNNs (like LSTMs or GRUs) to autocomplete for, say, a search engine, character by character. Which makes sense since it inherently predicts ...
2
votes
0answers
18 views

Is there any published research on the information-carrying capacity of the human face?

Is there any published research on the information-carrying capacity of the human face? Here I mean "how much information can be conveyed via facial expressions & micro-expressions". This is a ...
0
votes
0answers
16 views

How can feedforward neural networks act as contraction maps?

In graph neural networks, the Banach fixed-point theorem and Jacobi method it is described that the transition from one state to another be defined by a contraction map with a fixed-point. The autor ...
4
votes
1answer
44 views

How does the repetition of features across states at different time steps affect learning?

Let's say you are training a neural network in an RL setting, where the state (i.e. features/input data) can be the same for multiple successive steps (~typically around 8 steps) of an episode. For ...
2
votes
0answers
16 views

How does sampling works in case of imbalanced image datasets?

I am solving a problem of image classification of the image dataset for 3 classes. Dataset is highly imbalanced. How will sampling (either over- or under-sampling) work in that case? Should I remove (...
1
vote
0answers
8 views

How do CNNs or RNNs “stack the feature of nodes by a specific order”?

I am trying to understand the following statement taken from the paper Graph Neural Networks: A Review of Methods and Applications (2019). Standard neural networks like CNNs and RNNs cannot handle ...
6
votes
2answers
91 views

What is the difference between a Bayesian Network and a Markov Chain?

I am trying to understand the difference between a Bayesian Network and a Markov Chain. When I search for this one the web, the unanimous solution seems to be that a Bayesian Network is directional (...
1
vote
1answer
31 views

How do you manage negative rewards in policy gradient reinforcement learning?

The same basic question here, but 3 years old and no definitive answer: Negative reward (penalty) in policy gradient reinforcement learning The question is, if I'm doing policy gradient in keras, ...
3
votes
1answer
56 views

What is the difference between on-policy and off-policy for continuous environments?

I'm trying to understand RL applied to time series (so with infinite horizon) which have a continous state space and a discrete action space. First, some preliminary questions: in this case, what is ...
0
votes
0answers
11 views

Methods in training models to minimize distance between statistical summaries of data and samples from model, to get a better approximation function

Introduction: A big problem with deep learning methods involving neural networks is that they tend to do really poorly outside the boundaries of the approximation it has learned from the data it is ...
1
vote
0answers
17 views

Does the concept of validation loss apply to training deep Q networks?

In deep learning, the concept of validation loss is to ensure that the model being trained is not currently overfitting the data. Is there a similar concept of overfitting in deep q learning? Given ...
1
vote
0answers
14 views

What are the differences between a deep belief network, a restricted Boltzmann machine and a deep Boltzmann machine?

Can anyone list the differences between deep Belief network (DBN), restricted Boltzmann machine (RBM), deep Boltzmann machine (DBM) using simple examples? Links to other resources are also ...
1
vote
1answer
28 views

How to best make use of learning rate scheduling in reinforcement learning?

How to best make use of learning rate scheduling in reinforcement learning? To me, a low learning rate towards the end to fine-tune what you've learned with subtle updates makes sense. But I don't ...

15 30 50 per page