All Questions

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 ...

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, ...

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 ...

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 ...

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 ...

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: ...

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: ...

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 $\...

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 ...

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?...

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 ...

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. ...

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 ...

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?

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 ...

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? ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

From : https://debuggercafe.com/implementing-deep-autoencoder-in-pytorch/ the following autoencoder is defined ...

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 ...

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. ...

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 ...

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 ...

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?

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 ...

The first Image is Bad Wall and second one is good wall example

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 ...

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 ...

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 ...

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 ...

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_{...

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 ...

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 ...

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 ...

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 ...

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 ...

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 (...

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 ...

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 (...

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, ...

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 ...

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 ...

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 ...

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 ...

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 ...