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8 views

Use expert data in replay buffer used by deep deterministic policy gradient algorithm to converge faster

I am working on a restricted reinforcement learning environment i.e.: the environemt breaks very often. So, it is getting extremenly difficult for me to continue training the environment. The state [...
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0answers
5 views

Gradual decrease in performance of a DDPG agent

I'm trying to solve the OpenAI's CarRacing-v0 environment with the DDPG algorithm. I've observed that after a period of learning, the agent's performance starts to deteriorate slowly. For some ...
2
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0answers
12 views

Why are most commonly used activation functions continuous?

I have come to notice that the most commonly used activation functions are continuous. Is there any specific reason behind this? Results such as this paper have worked on training networks with ...
2
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0answers
24 views

How can I go from $R(s)$ to $R(s,a)$ in this specific MDP?

Given $\mathcal{X}$ as the set of states of an MDP and $\mathcal{A}$ as the set of actions of an MDP. Supposing I have four states ($1$,$2$,$3$,$4$), two actions $a$ and $b$ and a reward function $R: \...
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0answers
11 views

Yolo from scratch dataset and output

Hi I coded a YOLO model from scratch and just came to realise that my dataset does not fit the models output. This is what I mean: The model outputs a ...
1
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0answers
15 views

Using LSTM model to train spatial inputs

I have an $x$-$y$ plane, inside that plane I have 9 paths $(p_1, p_2, \dots, p_3)$. Each path is classified into one of the three classes $(c_1, c_2, c_3)$. Each path has 100 coordinates points i.e $((...
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0answers
18 views

Accuracy goes straight for about 200 epochs then start increasing

Can anyone explain the following observation? Why did the accuracies keep to be a straight line with a very smooth decrease of loss? Is this because of the learning rate or other reasons?
2
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1answer
21 views

House price inflation modelling

I have a data set of house prices and their corresponding features. Among these features, I have the sold date. The aim is to create a model that can use the historic data to estimate the price a ...
2
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0answers
36 views

Are there any approaches to AGI that will definitely not work?

Is there empirical evidence that some approaches to achieving AGI will definitely not work? For the purposes of the question the system should at least be able to learn and solve novel problems. Some ...
1
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0answers
6 views

How to predict multiple set of coordinates (of bounding boxes) for signboards text localization through neural network?

I am creating a signboard translation application from scratch. I have images of signboards where there are multiple texts and I have the corresponding set of coordinates of bounding boxes for ...
2
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0answers
32 views

Estimating dimensions to reduce input image size to in CNNs

Considering input images to a CNN that have a large dimension (e.g. 256X256), what are some possible methods to estimate the exact dimensions (e.g. 16X16 or 32X32) to which it can be condensed in the ...
2
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0answers
18 views

What are some of the main high level approaches to applying ML on kinematic sensor data?

I've just started a project which will involve having to detect certain events in a stream of kinematic sensor data. By searching through the literature, I've found a lot of highly specific papers, ...
2
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0answers
40 views

What is the dimensionality of these derivatives in the paper “Active Learning for Reward Estimation in Inverse Reinforcement Learning”?

I'm trying to implement in code part of the following paper: Active Learning for Reward Estimation in Inverse Reinforcement Learning. I'm specifically referring to section 2.3 of the paper. Let's ...
2
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2answers
54 views

Reinforcement Learning algorithm with rewards dependent both on previous action and current action

Problem description: Suppose we have an environment, where a reward at time step $t$ is dependent not only on the current action, but also on previous action in the following way: if current action ==...
5
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0answers
31 views

Why is my GAN more unstable with bigger networks?

I am working with generative adversarial networks (GANs) and one of my aims at the moment is to reproduce samples in two dimensions that are distributed according to a circle (see animation). When ...
0
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0answers
16 views

How to enforce symmetry loss in CNN

I have a task of extremely sparse binary segmentation, I use Focal Loss to address the sparseness (which is equivalent in my case to imbalances). I have another piece of information that I want to ...
0
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0answers
13 views

How does the embeddings work in vision transformer from paper?

I get the part from paper where image is split into P say 16x16 (smaller images) patches and then you have to ...
0
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1answer
34 views

Factors that causing totally different outcomes from an exactly same model and datasets

Here is a model that trains time series data in (batch, step, features) way. I have kept the random state for train test split function the same. Every parameter below the same, running the model ...
1
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0answers
41 views

Why don't those developing AI Deepfake detectors use two detectors at once so as to catch deepfakes in one or the other?

Why don't those developing AI Deepfake detectors use two differently trained detectors at once that way if the Deepfake was trained to fool one of the detectors the other would catch it and vice-versa?...
3
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0answers
37 views

How should I implement the state transition when it is a Gaussian distribution?

I am reading this paper Anxiety, Avoidance and Sequential Evaluation and is confused about the implementation of a specific lab study. Namely, the authors model what is called the Balloon task using a ...
1
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0answers
30 views

How can I model this problem as optimization problem that can be solved with ACO?

I have the following homework problem, where I need to explain how to model a certain problem as an optimization problem and how I can solve it with ACO. You have 6 portable media players $(P_i, ...
1
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0answers
16 views

Underfitting a single batch: Can't cause autoencoder to overfit multi-sample batches of 1d data. How to debug?

TL;DR I am unable to overfit batches with multiple samples using autoencoder. Fully connected decoder seems to handle more samples per batch than conv decoder, but then also fails when number of ...
2
votes
1answer
43 views

Aren't scores in the Wasserstein GAN probabilities?

I am quite new to GAN and I am reading about WGAN vs DCGAN. Relating to the Wasserstein GAN (WGAN), I read here Instead of using a discriminator to classify or predict the probability of generated ...
1
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1answer
151 views

What is a “learned policy” in Q-learning?

I am completing an assignment at the moment. One of the assignment questions asks how you identified the learned policy and how you obtained it. The question is a reinforcement learning question, and ...
0
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0answers
25 views

Can object detection approaches be used to solve text/detection problems?

I have been working on text detection and recognition for almost two months and new on this field. So far, I have fine-tuned, tested, and trained several text detection/recognition methods, such as ...
1
vote
1answer
40 views

What is the difference between out of distribution detection and anomaly detection?

I'm currently reading the paper Likelihood Ratios for Out-of-Distribution Detection, and it seems that their problem is very similar to the problem of anomaly detection. More precisely, given a neural ...
0
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1answer
48 views

Relationship between Rewards and Q Value (Graph between Q(s, a) vs episodes)

I'm employing the Actor-Critic algorithm. The critic network approximates the action-value function, i.e. $Q(s, a)$, which determines how good a particular state is, when provided with an action. $Q(s,...
3
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1answer
39 views
+50

How should we regularize an LSTM model?

There are five parameters from an LSTM layer for regularization if I am correct. To deal with overfitting, I would start with reducing the layers reducing the hidden units Applying dropout or ...
0
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0answers
20 views

Is using a LSTM, CNN or any other neural network model on top of a Transformer(using hidden states) overkill?

I have recently come across transformers, I am new to Deep Learning. I have seen a paper using CNN and BiLSTM on top of a transformer, the paper uses a transformer(XLM-R) for sentiment analysis in ...
0
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1answer
38 views

Is the working of RNNs, LSTM and GRU sequential or parallel?

You take any blog or any example and all they tell you about is the given picture below. It has 4 different matrices and 3 of whose weights are shared. So, I'm wondering how is this achieved in ...
2
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0answers
35 views

Reinforcement learning and Graph Neural Networks: Entropy drops to zero

I am currently working on an experiment to link reinforcement learning with graph neural networks. This is my architecture: Feature Extraction with GCN: there is a fully meshed topology with ...
4
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0answers
23 views

Do AlphaZero/MuZero learn faster in terms of number of games played than humans?

I don't know much about AI and am just curious. From what I read, AlphaZero/MuZero outperform any human chess player after a few hours of training. I have no idea how many chess games a very talented ...
1
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1answer
29 views

Can cryptocurrency charts be estimated using neural networks?

If I were to make a neural network that predicts the value of e.g. Bitcoin tomorrow based on the chart of the last month, would that work? Of course, 100% accuracy cannot be reached, but a success ...
2
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0answers
17 views

What should the initial UCT value be with MCTS, when leaf's simulation count is zero? Infinity?

I am implenting a Monte Carlo Tree Search algorithm, where the selection process is done through Upper Confidence Bound formula: ...
0
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0answers
13 views

How to deal with dynamically changing input tensor in neural networks without padding?

I have a dataset about the monitored health/growth of a community of people. The dataset has tensor shaped (batch_size, features, person, window), where: person==...
1
vote
2answers
34 views

How to recognize sequence of digits in an image

I am learning to program neural networks and others, and I would like to know how I can get the numbers that are in an image, for example if I pass an image that has 123 written, get with my model ...
0
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0answers
39 views

What would be the vectorized version of the gradient and loss of the cross-entropy formula?

I am looking for the vectorized version of these formulas: The cross-entropy loss $$L(D) = \sum^N_{n=1}\sum^K_{k=1}t_{kn} \ln(y_{\vec{w_k}}(\vec{x_n}))$$ The gradient of the cross-entropy $$\nabla ...
0
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0answers
33 views

Alpha Zero does not converge for Connect 6, a game with huge branching factor - why?

I have a problem with applying alpha zero self-play to a game (Connect 6) with a huge branching factor (30,000 on average). I have implemented the MCTS as described but I found that during the MCTS ...
2
votes
1answer
49 views

What do the variables in the cross-correlation formula mean?

I understand what cross-correlation does given a kernel and an input image, but the formula confuses me a little. Given here in Goodfellow's Deep Learning (page 329), I can't quite understand what $m$ ...
0
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0answers
9 views

AI generator for imaginary street maps?

Similar to This person does not exist or This artwork does not exist, how might I go about creating a This street map does not exist, including choosing an appropriate AI model and scoping features? ...
1
vote
1answer
30 views

How to build a test set for a model in industry?

Most of the tutorials only teach us to split the whole dataset into three parts: training set, develop set, and test set. But in the industry, we are kind of doing test-driven development, and what ...
-2
votes
0answers
21 views

Is there a treatise that contains the following formulas? [closed]

Is there a treatise that contains the following formulas? ark_value = w/n + cpuct * p * √t/(1+n) It didn't exist in alphazero's treatise, so please let me know if ...
-2
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0answers
19 views

What is the difference between a network framework and an architechture? [closed]

I wonder about the difference between the two terms, as they seem to be used interchangeably.
2
votes
1answer
44 views

Are Genetic Algorithms suitable for problems like the Knuth problem?

We all know that Genetic Algorithms can give an optimal or near-optimal solution. So, in some problems like NP-hard ones, with a trade-off between time and optimal solution the near-optimal solution ...
2
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0answers
16 views

For binary classification learning problems, how should I label instances where I'm only 60% sure?

I've come across a few binary classification problems lately where the labelling was challenging even for an expert. I'm wondering what I should do with this. Here are some of my suggestions to get ...
2
votes
0answers
18 views

Is it possible to ensure the convergence when training a RNN weight on its SVD decomposition?

I'm reading the following paper in which the author seems to do 2 things interesting: The hidden-to-hidden weight matrix of the RNN is SVD decomposed and train separately. Each orthogonal part of the ...
-1
votes
0answers
14 views

Improvements to the neat algorithm [closed]

https://www.youtube.com/watch?v=ihX3-WDua2I In the video, he mutate weights and structures, and in fact, the human brain has an algorithm that determines weights and structures. Can't you represent ...
1
vote
0answers
16 views

CSP heuristic to simultaneously reduce conflicts and find near optimal assignment

I am trying to design a good heuristic to solve a constraint satisfaction problem (CSP). I think that a possible heuristic to use is $$h_1(\text{state}) = \text{number of conflicts in state}$$ However,...
3
votes
0answers
25 views

Is non-negative matrix factorization for machine learning obsolete?

I am taking a course about using matrix factorization for machine learning. The first thing that came into my mind is by using the matrix factorization we are always limited to linear relationships ...
8
votes
2answers
742 views

What is the difference between Q-learning, Deep Q-learning and Deep Q-network?

Q-learning uses a table to store all state-action pairs. Q-learning is a model-free RL algorithm, so how could there be the one called Deep Q-learning, as deep means using DNN; or maybe the state-...

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