All Questions

I’ve created an agent using MCTS to play Connect Four. It wins against humans pretty well, but I’d like to improve upon it. I decided to add domain knowledge to the MCTS rollout stage. My evaluation ...

I've been trying to train a snake for the snake game in DQN. Which the snake can essentially just move up, down, left and right. I'm having a hard time getting the snake to stay alive longer. So my ...

I have two different implementations with PyTorch of the Atari Pong game using A2C algorithm. Both implementations are similar, but some portion are different. https://colab.research.google.com/drive/...

I have trained a CNN network using dataset = tf.data.Dataset.from_tensor_slices((data, label)) to create the dataset. training went well but evaluating the model on ...

Good day, it's a pleasure having joined this Stack. In my master thesis I have to expand a Deep Reinforcement Learning Network, to be precise a Deep Q-Network, which is used to control machines in an ...

I'm implementing strided 2D convolution. My formula looks like this: $$y_{i, j} = \sum_{m=0}^{F_h - 1}\sum_{n=0}^{F_w - 1} x_{s\cdot i + m, s\cdot j + n}\,f_{m, n}, \tag{1}$$ where $s$ is the stride (...

I’ve coded a simple ELIZA chatbot for a high school coding competition. The chatbot is part of an app that’s designed to help its user cope with depression, anxiety, and similar mental health ...

In my discussion over my question on Math SE, I explained to a user, how I think AI works, I wrote that with the sigmoid(logistic) function, features of a data set are identified, many such iterations ...

I've been reading on Tacotron-2, a text-to-speech system, that generates speech just-like humans (indistinguishable from humans) using the GitHub https://github.com/Rayhane-mamah/Tacotron-2. I'm very ...

Here is the code written by Maxim Lapan. I am reading his book (Deep Reinforcement Learning Hands-on). I have seen a line in his code which is really weird. In the accumulation of the policy gradient $...

I have a dataset with missing values, I would like to use machine learning methods to fill. In more detail, there are $n$ individuals, for which up to 10 properties are provided, all numerical. The ...

Q-learning is a temporal-difference method and Monte Carlo tree search is a Monte Carlo method. In what category is MiniMax?

I am reading the documentation on tf.lattice (https://www.tensorflow.org/lattice/overview) I am wondering how the training data was created/trained. Is it represented by (x,y) intpositions and some ...

I have read several papers about super-resolution with CNNs, where a low-resolution image is reconstructed to a high-resolution image. What I don't understand is, why it is necessary to interpolate ...

If I replicated the results of a machine learning paper, is there a website that I can publish my code to that collects informal replication attempts? https://paperswithcode.com/ allows anyone to ...

I want to feed the Neural Net with examples of three images: 2 input and one output-result. Net shold learn by those examples and then generate the appropriate "mixes" for new pairs of pics. Is there ...

I'm a novice researcher, and as I started to read papers in the area of deep learning I noticed that the implementation is normally not added and is needed to be searched elsewhere, and my question is ...

What is the difference between the term 'prediction'/value estimation in RL as compared to the 'control' problem? Are there scenarios in RL where the problem cannot be distinctly categorised into ...

I want to build a reinforcement learning model, which takes a camera picture as input, that learns online (in terms of machine learning). Based on the position of an object on the camera, I want the ...

I am actually working with the iris dataset from sklearn and try to understand the ANFIS-Package for python. But that does not really matter! I have a more general question. During thinking about ...

I have implemented a simple Q-learning algorithm to minimize a cost function by setting the reward to the inverse of the cost of the action taken by the agent. The algorithm converges nicely but there ...

I have, like you see, just a general question about the combination of fuzziness and neural networks. I understood it as follows Fuzzy neural networks as a hybrid system: the neural network helps me ...

So far I have seen TD3 and DDPG benchmarks on Pybullet environments, but I am looking for SAC benchmarks on Pybullet too, anyone can help?

I have a dataset with MRI of patients with a specific disease that affects the brain and another dataset with MRI of healthy patients. I want to create a classifier (using neural networks) to ...

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine ...

Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. SI-based algorithms, ...

I have retrospective data for a sort of "behaviour policy" which I will use to train a deep q network to learn a target greedy policy. After learning the Q values for this target policy, can we make ...

So I understand that L1 and L2 regularization helps to prevent overfitting. My question is then, does that mean they also help a neural network learn faster as a result? The way I'm thinking is that ...

Running on tensorflow, the moment that I try and run two jobs in parallel with any gpu related jobs, it flags and gives me an error regarding cuDNN module being not available. Is there any method ...

In the decision tree algorithm, why do we use a weighted average of child entropies when we calculate information gain? What is wrong about using the arithmetic mean of entropies?

I have a quick question regarding the use of different latent spaces to represent a distribution. Why is it that a Gaussian is usually used to represent the latent space of the generative model ...

One way of understanding the difference between value function approaches, policy approaches and actor-critic approaches in reinforcement learning is the following: A critic explicitly models a value ...

When papers talk about the "test time", does this mean the phase when the model is passed with new data instances to derive the accuracy of the test data set? Or is "test time" the phase when the ...

I am trying to build a model for extractive text summarization using keras sequential layers. I am having a hard time trying to understand how to input my x data. Should it be an array of documents ...

According to Brian Cantwell Smith no calculation without representation Therefore, computers depend on models. So, we can say that AI is limited internally by the model and external by the ...

I came across these slides Natural Language Processing with Deep Learning CS224N/Ling284, in the context of natural language processing, which talk about the Jacobian as a generalization of the ...

I have a neural network that connects $N$ input variables to $M$ output variables (qoi). By default, neural networks just give out point estimations. Now, I want to plot some of the quantity of ...

I'm struggling with calculating accuracy when I do cross-validation for a deep learning model. I have two candidates for doing this. 1. Train a model with 10 different folds and get the best accuracy ...

One of the approaches to improving the stability of the Policy Gradient family of methods is to use multiple environments in parallel. The reason behind this is the fundamental problem we ...

In the paper "Residual Energy-Based Models for Text Generation" (arXiv), on page 5, they write that equation 5 is an instance of importance sampling. Equation 5 is: $$ P(x_t \mid x_{<t}) = P_{LM}(...

The Deep RL bootcamp on policy gradient techniques gives the update equation for the policy network in A3C as $\theta_{i+1} = \theta_i + \alpha \times 1/m \sum_{k=1}^m\sum_{t=0}^{H-1}\nabla_{\theta}...

How do reinforcement learning and collaborative learning overlap? What are the differences and similarities between these fields? I feel like the results I get via google do not make the distinction ...

I can't figure out what preprocessing of the image is needed before feeding it into the convolutional neural network. For example, I want to recognize circles on a 1000 by 1000 px photo. The learning ...

Pieter Abbeel in his deep rl bootcamp policy gradient lecture derived the gradient of the utility function with respect to $\theta$ as $\nabla U(\theta) \approx \hat{g} = 1/m\sum_{i=1}^m \nabla_\...

I am trying to input text into my word processor to be split into sentences first and then into words. An example paragraph: ...

I know this is a basic problem, but still could not find answer, I feel like most books/tutorials avoid talking about scaling the output feature instead they just mention scaling input features. So ...

I'm Spanish and I don't understand the meaning of "non-held-out". I have tried Google Translator and online dictionaries like Longman but I can't find a suitable translation for this term. You can ...

MuZero seems to use two different methods to encode actions into planes for Atari games: For the input action to the representation function, MuZero encodes historical actions as simple bias planes, ...

Suppose $x_{t+1} \sim \mathbb{P}(\cdot | x_t, a_t)$ denotes the state transition dynamics in a reinforcement learning (RL) problem. Let $y_{t+1} = \mathbb{P}(\cdot | x_{t+1})$ denote the noisy ...

I'm predicting the used 9 pictures to predict the last picture so (40,40,9) -> unet -> (40,40,1) but as you see the predict picture It's not just a mask(0or 1) its float so which loss function ...