Questions tagged [machine-learning]

For questions related to machine learning (ML), which is a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data). ML is usually divided into supervised, unsupervised and reinforcement learning. Deep learning is a subfield of ML that uses deep artificial neural networks.

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34 views

How would you intuitively but rigorously explain what the VC dimension is?

The VC dimension is a very important concept in computational/statistical learning theory. However, the first time you read its definition, you may not immediately understand what it really represents ...
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0answers
16 views

How to Train a Machine Learning Model with multiple X attributes and one target value (Y)?

I'm working on a machine learning problem where I need to guess which customer will churn and which of them will remain being customers. I have X0, X1, X2, X3, X4, X5 and X6 attributes representing if ...
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0answers
11 views

Is there an technique to analyse the relationship between time-series clusters?

I have two time-series datasets (temperature and speed of the vehicle). I will use Agglomerative Hierarchical Clustering and DTW to cluster both datasets. I am looking for a technique (like regression ...
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23 views

How to detect dynamic hand gestures?

I already know how to detect static hand gestures like fist, peace etc. I wonder however, how to detect dynamic hand gestures like swipe left/right or "draw" circle with hand. Is some kind ...
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0answers
12 views

Can I use the phi coefficient to compare predictions by two different classifiers?

Can I use the Matthews correlation coefficient (aka phi coefficient) to compare predictions by two different classifiers? That is, is this code correct if I want to check how diverse/correlated my two ...
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0answers
10 views

Does distribution of data augmentation parameters matter?

Idea Let's say we have simple pictures dataset containing 40x40 images of digits. We have only one image of each digit. We want to use that as training set, but we need more data, so we use data ...
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0answers
20 views

Reward firstly increase, but after more episodes, start decrease, and weights diverges

I'm making a simple deep Q learning algorithm, with cartpole-v1 env. Like you can see in chart, after many episodes the reward decrease, some possible reasons? The exploration vs axplotation algorithm ...
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2answers
25 views

Is binary classification using CNN possible if the training data only consists of one class?

Is binary classification using CNN possible if the training data only consists of one class? I am working on landslide risk assessment using Convolutional Neural Networks and I want to train a network ...
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1answer
32 views

What is the correct formula for updating the weights in a 1-single hidden layer neural network?

I'm creating a neural network with 3 layers and no bias. On internet I saw that the expression for the derivative of the weights between the hidden layer and the output layer was: $$\Delta W_{j,k} = (...
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2answers
34 views

What is the difference between applying shallow-learning methods repeatedly and deep learning?

In the book Deep Learning with Python, François Chollet writes (section 1.2.6, page 18) In practice, there are fast-diminishing returns to successive applications of shallow-learning methods, because ...
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0answers
25 views

How to understand the phrase “conditioning on an input” for a neural network?

Suppose I have a dataset $D_1$ with size $n$ and each training sample has $m$ attributes/features. So, my neural network has $m$ neurons at input layer, i.e., $D_1$ has $n$ samples of size $m$. ...
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0answers
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Preprocessing deterministic data with sklearn

I am trying to create a set of ML models that will serve as a replacement for a complex deterministic simulation. The simulation requires 4 inputs (x1, x2, x3 and x4) to determine 4 different outputs (...
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0answers
39 views

What is the name of algorithms that train by competing each other?

In some learning algorithms, we don't directly train models by datasets with labels to predict, but rather we create 2 competing models and let them fight/compete against each other. As the many ...
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1answer
34 views

Machine Learning in relation to personality and behaviors predictions

I am tasked with making a machine learning model that predicts personality traits and behaviours of children based on simple and interactive quizzes. Currently I am lost and have no idea where to ...
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0answers
29 views

How to train feedforward network to recognize images?

Context I'm trying to create network for digits recognition. All digits are the same font and size of 40x40. I know that I can use feedforward network or CNN. I'd like to use the first one. Issue I ...
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1answer
44 views

How to properly use Flatten layer?

Context I'm trying to create net that will be able to recognize printed-like digits. Something like MNIST, but only for standard printing font. Images are of the size 40x40 and I'd like to put them ...
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1answer
25 views

How can abstract graphs be recognized by neural nets?

Recognition of optical patterns (as pixel maps) by neural networks is standard. But optical patterns may be only slightly distorted or noisy, and may not be arbitrarily scrambled – e.g. by ...
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2answers
44 views

Is creating dataset only by augmentation a bad practice?

I wonder if creating data set only by augmentation base images is a bad practice. I mean the situation when you have to train net to predict really simple patterns, for example printed-like digits. ...
2
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1answer
71 views

Which ANN structure to use?

Let $\mathcal{S}$ be the training input data set where each input $u^i \in \mathcal{S}$ has $d$ features. I want to design a ANN so that the cost function below is minimized (the sum of square of ...
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0answers
46 views

Reinforcement learning for rearranging the mobile home screen icon layout: what inputs/states do I need to pass into the algorithm?

I have a problem where I need to rearrange a particular user's mobile home screen icon layout. Let's say that the social media app usage of a user is high compared to other app usage. So I need the ...
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1answer
54 views

When exactly am I overfitting — contradicting metrics

I am training an object detection machine learning pipeline. Among the many metrics provided out of the box by tensorflow object detection API, I look at total_loss and DetectionBoxes_Precision/mAP@....
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1answer
48 views

Assumptions of a Linear Regression [closed]

I was going through the concept of Linear Regression and ran into the concept of deciding whether a Linear Regression Model is the best fit for your data by 5 assumptions: Linearity Homoscedasticity ...
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2answers
116 views

Is there any specific SW framework, libraries or algorithms (supported by any theory) designed for implementing a practical AGI system? [closed]

Any (AGI)-KERAS like libraries? Any deep-learning framework to develop AGI applications? Existing frameworks/algorithms used in NN, NLP, ML, etc are not enough in my opinion. In my opinion any ...
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1answer
22 views

How do I take the correct classification predictions of an ml algo (i.e. random forest/neural net) and sort the instances in each category?

I am trying to sort the instances within each of 5 classification categories in a dataset that has been put through both a random forest classifier and a neural network with 99% accuracy on each. ...
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1answer
45 views

What would be the importance sampling ratio for off-policy TD learning control using Q values?

The off-policy TD learning control using state value function from page 34 of David Silver's RL lecture is: $$ V(S_t) \leftarrow V(S_t) + \alpha \left( \frac{ \pi(A_t|S_t)}{\mu (A_t|S_t)} (R_{t+1} + \...
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0answers
10 views

How to write activation function with a higher order tensor in Keras? [migrated]

I want to create a paricular neural network in Keras. In this neural network I use layers given by $$ f(x) = C_k(\underbrace{x,\dots,x)}_{\times k}+\phi(w^\intercal x+b) $$ The expression $\phi(w^\...
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0answers
10 views

Why is my LSTM model predicting accurately for only a few values and showing drastic aberration later?

I am training an LSTM model using stock data for time series forecasting and the results are a little confusing to me. This is the prediction I get after 5 epochs. And this after 100 epochs. Why the ...
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0answers
26 views

What is a Hebbian linear classifier?

I was reading Deep Learning of Representations for Unsupervised and Transfer Learning, and they state the following: They have only a small number of unlabeled examples (4096) and very few labeled ...
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2answers
71 views

What does “semantic gap” mean?

I was reading DT-LET: Deep transfer learning by exploring where to transfer, and it contains the following: It should be noted direct use of labeled source domain data on a new scene of target domain ...
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0answers
24 views

Identifying if a model is over or under-fitting via graphs

I am working on a Neural Network and have plotted the performance of my model. However the plots seem not to fit the "trends" (which help you identify the issue with your model) presented in ...
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0answers
23 views

Is there any way to determine/estimate the number of rounds for the whole Federated Learning process?

In Federated Learning (FL) the process ends until the model converges or reached certain accuracy. My question: Is there any way to determine/estimate the number of rounds for the whole process?
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1answer
22 views

What's the difference between a 1d tensor and a 2d tensor with 1 dimension?

I'm doing a TensorFlow tutorial, where they convert an array of the numbers [1,2,3] to a tensor like this: ...
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2answers
55 views

What are the pros and cons of using sigmoid or softmax approach when dealing with 2 classes?

I know that when using Sigmoid, you only need 1 output neuron (binary classification) and for Softmax - it's 2 neurons (multiclass classification). But for performance improvement (if there is one), ...
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0answers
29 views

Any RL approaches for this 2D space optimisation problem?

I have a list of rectangles, they are in certain order in 2D at the beginning. The task is to move them to get the boundary (rectangular) of the minimal area. It's OK to push off the dotted border as ...
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1answer
16 views

Expected behavior of adversarial attacks on deep NN?

I am trying adversarial attack (AA) for a simple CNNs. Instead of the clean image, my simple CNN is trained with attacked images as suggested by some papers. As the training goes on, I am not sure if ...
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0answers
17 views

Clonal operator in Immune Clonal Strategy

I was reading about Immune Clonal Strategy, specifically about Monoclonal operator from Immunity clonal strategies, and it goes as follows: Here $a_i $ is a point and $a_i = \{ x_1, x_2, \cdots, x_m \...
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0answers
17 views

Converting inputs as a batch for time series classification would increase accuracy?

I have sensor dataset. I have already classified these data with LSTMs.I have a dataframe with 2 features and a class column. Assume that I take every two rows(inputs) respectively and make the ...
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0answers
7 views

Split a city into a set of candidate locations (cells) via a clustering algorithm (DBSCAN or OPTICS)

The authors of the following paper predict the market attraction for restaurants based on user reviews for different locations to select an ideal location. To do so they have split the city into a set ...
7
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1answer
101 views

Why does Batch Normalization work?

Adding BatchNorm layers improves training time and makes the whole deep model more stable. That's an experimental fact that is widely used in machine learning practice. My question is - why does it ...
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0answers
10 views

How does the loss landscape look like or change when a model is overfitting?

My understanding is that when a model starts overfitting, it no longer learns useful features and starts remembering the training data set. Given enough epochs and sufficient parameters, a model can ...
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0answers
28 views

Is a true RNN auto encoder possible with Keras/TF

I want to get some encodings for temporal data (with a highly varying number of timesteps). The dataset is of the format: ...
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0answers
36 views

How many papers about AI / ML were published in the recent years?

I am trying to formulate an argument at work saying the disruption in AI/ML is very high and that it is hard to stay "state of the art". I would like to support that hypothesis by numbers. ...
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0answers
14 views

Is there any use of having connections between nodes in the output layer of a neural network?

Is there any use of having connections between nodes in the output layer of a neural network? In some cases some outputs may depend on other outputs; by this logic, is it possible to have such neural ...
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0answers
20 views

extract desire keyword/text pair

I am looking for extract keyword pair from text files. They might not be next to each other and do not have same pattern for each occurrence. And I would not think regex will works because there is no ...
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1answer
40 views

How to interpret this learning curve of my neural network?

How to interpret the following learning curves? Background: The accuracy starts at 50%, because the network has a binary output (0 or 1). I chose an exponentially decreasing learning rate of the ...
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1answer
24 views

Does adding a model complexity penalty to the loss function allow you to skip cross-validation?

It's my understanding that selecting for small models, i.e. having a multi-objective function where you're optimizing for both model accuracy and simplicity, automatically takes care of the danger of ...
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1answer
31 views

Do RNNs/LSTMs really need to be sequential?

There are many articles comparing RNNs/LSTMs and the Attention mechanism. One of the disadvantages of RNNs that is often mentioned is that while Attention can be computed in parallel, RNNs are highly ...
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1answer
30 views

What are the differences in testing between traditional software and artificial intelligence?

The testing problem in traditional software has been fully explored over the last decades, but it seems that testing in artificial intelligence/machine learning has not (see this question and this one)...
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0answers
28 views

Why does $E_q[\log p(\mathbf{w}|\mathbf{z},\beta)]=\sum_{n=1}^{N}\sum_{i=1}^{k}\sum_{j=1}^{V}\phi_{ni}w_n^j\log \beta_{ij}$ hold in LDA?

I'm having trouble understanding an equality that comes up in the original LDA paper by Blei et al.: Consider the classical LDA model, i.e. for every document $\textbf{w}=(w_1,\ldots,w_N)$ in a text ...
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0answers
21 views

How can AI algorithms be used in regards to cryptocurrency and token mining?

I am new to AI and lack the knowledge of it's capabilities. A question popped in my head, in regards to Blockchains and the mining of cryptocurrencies and tokens, how can machine learning algorithms ...

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