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Questions tagged [deep-learning]

For questions related to deep learning, which refers to a subset of machine learning methods based on artificial neural networks (ANNs) with multiple hidden layers. The adjective deep thus refers to the number of layers of the ANNs. The expression deep learning was apparently introduced (although not in the context of machine learning or ANNs) in 1986 by Rina Dechter in the paper "Learning while searching in constraint-satisfaction-problems".

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1answer
8 views

Relu vs Liniar activation when to use one over another?

I have read this post: How to choose an activation function? There is enough literature about activation functions about what it is. My question would be when should I use a liniar activation instead ...
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0answers
12 views

What is the underlying model of IBM Watson Assistant and Microsoft LUIS?

As I stated in my question, I would like to know the underlying pipeline and machine learning models that are used to classify intents and identify entities in IBM Watson Assistant and Microsoft LUIS ...
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0answers
11 views

Deep learning based architecture for digit localizing

I'm new to object detectors and segmentation. I want to localize digits in a plate as fast as possible. All images of dataset are normalized to 300x60. There are ...
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1answer
46 views

What would be the most effective self-learning algorithm for a 7 player social deduction game?

There's this 7 player social deduction game called Secret Hitler, and I have been trying to find a self-learning AI algorithm to learn how to play this game for a while. Basically, four players are ...
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0answers
25 views

Reinforcement learning number of episodes per epoch not matching with paper

I am trying to reproduce results presented in this paper. On page 4, the authors state: ... we train for 50 epochs (one epoch consists of 19*2*50 = 1900 full episodes), which amounts to a total ...
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3answers
63 views

How can neural networks be used to generate rather than classify?

In my experience with Neural Nets, I have only used them to take input vectors and return binary output. But, here in a video, https://youtu.be/ajGgd9Ld-Wc?t=214, Kai Fu Lee, renowned AI Expert shows ...
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0answers
24 views

Assigning Weighting Factors

I have a hypothetical example that closes to my research problem: Assume you are a boss and you have different types of tasks that you need to assign to your employee. Sensitive task (very classified)...
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1answer
53 views

Emotional Speech Synthesis

We are a team of computer science our graduation project about EmotionalSpeech Synthesis. We've found valuable information like research papers and WaveNet, Tacotron. A website (https://www.voicery....
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1answer
25 views

Doubt in Deep-Q learning with sparse rewards

I am working on a deep reinforcement learning problem, when I got stuck at the following questions. They are rather general and not specific to my specific problem. The solution uses a sparse reward ...
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0answers
10 views

Which metrics of COCO API are suitable for object detection

These days I train a person detector and I use COCO API to evaluate my model. It uses Recall and Precision to evaluate a detection task. It's output is something like this: ...
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1answer
34 views

What are sim2sim, sim2real and real2real?

Recently, I always hear about the terms sim2sim, sim2real and real2real. Will anyone explain the meaning/motivation of these terms (in DL/RL research community)? What are the challenges in this ...
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1answer
30 views

Why is image classification tasks are dominated by minimizing cost function instead of maximizing ones?

I was watching a video of policy gradient by Andrej Karpathy at 10:00 there shows an equation for supervised learning for image classification. $max\sum _{i}log \:p(y_i|x_i)$ I have worked with ...
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0answers
24 views

Are there methods that allow deep networks to learn object categorization in a self-supervised way?

When training a deep network to learn object classification from a set like ImageNet, we minimize the cross entropy between the ground truth and the predicted categories. This is done in a supervised ...
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0answers
19 views

Why hasn't deep learning been used for word level alignment?

I've been exploring word-level alignments tools such as MGIZA and it seems to me that there hasn't been any new tool for this problem. Are neural networks not suitable to solve this problem or simply ...
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1answer
23 views

Is this learning rate schedule increasing the learning rate?

I was reading a PyTorch code then I saw this learning rate scheduler: ...
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3answers
112 views

Is machine learning required for deep learning?

The answers to this Quora question say it's OK to ignore machine learning and start right away with deep learning. Is machine learning required or is useful for understanding (theoretically and ...
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0answers
8 views

Can someone please explain the basics of a procedural reason system?

I’m a complete beginner so any help is appreciated
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2answers
50 views

Can multiple activation functions be replaced with a single activation function?

I'm just started to learn deep learning and I have a question about this neural network: I think $h_1$, $h_j$ and $h_n$ are perceptrons. So, if they are perceptrons, all of them will have an ...
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0answers
13 views

How to choose the suitable Neural Network Architecture for Regression Tasks

so I'm working on a Project where I want to predict the Vehicle Position from the Vehicle Data like speed, acceleration etc.. now the data that I have comes also with a timestamp for each sample ( I ...
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0answers
57 views

How to develop face recongiton program using CNN to obtain more than 95% accuracy? [closed]

I want to develop face recognition program using convolutional neural network. Can some one tell me steps to follow to do the same? I am new to deep learning. I want to develop it on windows using ...
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0answers
8 views

How does keras `train_on_batch` return value work?

From the doc, train_on_batch() will return a scalar representing the loss and the metric. I want to know whether the loss/metric is evaluated before the weight is ...
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1answer
27 views

How to use a deep learning network on new data-set?

I am trying to use a network for classification. This network works very well on the author's example data, but doesn't work on new data. Currently, I am using the popular EEG Motor Movement/Imagery ...
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1answer
31 views

Can A.I/Deep Learning use one image as content for another image?

Is it possible for A.I to redraw an image in a new context? More specifically, suppose I take a photo of a notebook. I get the angle, lighting and perspective perfect. Now I copy an image I found ...
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2answers
64 views

What is the benefit of using identity mapping layers in deep neural networks like ResNet?

As I understand Resnet has some identity mapping layers that their task is to create the output as the same as the input of the layer. the resnet solved the problem of accuracy degrading. But what is ...
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0answers
12 views

Choosing best combinations from all possible combination expressions based few variables, unary operators, binary operators

I have a few financial variables of a stock universe like OHLC prices, volume, and other fundamentals with varying time-frequency. Using this set I'm creating an expression that gives the weights to ...
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21answers
13k views

Can digital computers understand infinity?

As a human being, we can think infinity. In principle, if we have enough resources (time etc.), we can count infinitely many things (including abstract, like numbers, or real). For example, at least, ...
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1answer
41 views

“Outside-in” versus “Inside-out” machine learning

A little background... I’ve been on-and-off learning about data science for around a year or so, however, I started thinking about artificial intelligence a few years ago. I have a cursory ...
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0answers
7 views

Looking or the simplest framework to train keypoint detector

I currently use an object detector to detect an object and specific parts of it (a crop and its stem). Such detector is not the best choice for detecting parts that could be represented by a point (...
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0answers
23 views

How to calculate covariance matrix of the mini-batch in the k-th layer using Python?

I am a beginner in Python. I want to calculate the covariance matrix of a mini-batch in a given hidden layer.
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2answers
43 views

Why is embedding important in NLP, and how does autoencoder work?

People say embedding is necessary in NLP because if using just the word indices, the efficiency is not high as similar words are supposed to be related to each other. However, I still don't truly get ...
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1answer
38 views

Why do very deep non resnet architectures perform worse compared to shallower ones for the same iteration? Shouldn't they just train slower?

My understanding of the vanishing gradient problem in deep networks is that as backprop progresses through the layers the gradients become small, and thus training progresses slower. I'm having a hard ...
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1answer
43 views

Is it possible to do K-nearest-neighbours before training DNN

The following X-shape alternated pattern can be separated quite well and super fast by K-nearest Neighbour algorithm (go to http://ml-playground.com to test it): However, DNN seems to face great ...
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0answers
7 views

Fusing label distribution and on-hot encoded labels

A while ago I came across a paper for image classification that utilized both label distribution and one-hot encoded labels to classify images. An image has a label distribution for all classes (4 ...
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0answers
9 views

What is best dataset for person reidentification?

There are many datasets for person reidentification. I want to train a robust person re-identification neural network. Therefore I want to ask about best person re-identification dataset.
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0answers
61 views

Will parameter sweeping on one split of data followed by cross validation discover the right hyperparameters?

Let's call our dataset splits train/test/evaluate. We're in a situation where we require months of data so we prefer to use the evaluation dataset as infrequently as possible to avoid polluting our ...
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0answers
23 views

Vector normalization by a neural network

I'm wondering if there is a NN that can achieve the following task: Output a unit vector that is parallel to the input vector. i.e., input a vector $\mathbf{v}\in\mathbb{R}^d$, output $\mathbf{v}/\|\...
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1answer
189 views

What are all the different kinds of neural networks used for?

I found the following neural network cheat sheet (Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data). What are all these different kinds of neural networks used ...
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0answers
7 views

How is Average Recall (AR) calculated for an object detection model?

After looking around the internet (including this paper, I cannot seem to find a satisfactory explanation of the Average Recall (AR) metric. On the COCO website, it describes AR as: "the maximum ...
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0answers
17 views

Better versions of LSTM

I know recurrent neural networks for example LSTM is one of them. After LSTM different variants of LSTM have come for example GRU. I don't know about newer RNN like LSTM and GRU, so I decided to ask ...
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0answers
31 views

Is there a GAN that can be used for sequence prediction?

I want to use a GAN for sequence prediction, in a similar way that we use RNNs for sequence prediction. I want to test its performance in comparison with RNNs. Is there a GAN that can be used for ...
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0answers
9 views

what is the best dataset (or combination of datasets) for training a person detector?

I want to train a person detector for MOTChallenge dataset, but I want to avoid using the dataset as training set for my detector. Therefore, I want to use another dataset or combination of several ...
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0answers
13 views

Numbers to image regression

I would like to create a machine learnig framework that could predict the 3D heat distribution of a room(of size 120x120x120) , given multiple parameters(position of the heater, orientation, power of ...
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2answers
55 views

LSTM network doesn't converge, what should be changed? [closed]

I'm testing out TensorFlow LSTM layer text generation task, not classification task; but something is wrong with my code, it doesn't converge. What changes should be done? Source code: ...
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0answers
24 views

How to get same accuracy with identical models in Keras and Tensorflow?

As we all know Keras backend uses Tensorflow and so it should give out same kind of results when we provide same parameters, hyper-parameters, weights and biases initialisation at each layer, but ...
3
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1answer
31 views

Query on another perspective on Deep Learning

At least at some level, maybe not end-to-end always, but Deep Learning always learns a function, essentially a mapping from a Domain to a Range. The Domain and Range, at least in most cases, would be ...
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1answer
29 views

DQN regarding fitting off actions

While studying DQN, I rarely if ever see an explanation of what to fit off actions to. Meaning, if the highest valued action of 3 choices if the first, when training, what do we do with the other two ...
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1answer
29 views

Tensorflow and Keras model having same parameters, hyperparameters, weight and bias initialization give different accuracy? [closed]

I have made sure that layers,parameters, hyperparameters,kernel_initialization, bias_initialization, seed and dataset are all equal. But still the output for both the models are different. ...
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1answer
29 views

How to map X to Y for TensorFlow RNN training data

Usually for DNN, I have the training data of matching X (2D) to Y (2D), for example, XOR data: X = [[0,0],[0,1],[1,0],[1,1]]; Y = [[0], [1], [1], [0] ]; ...
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0answers
26 views

Neural Network training on one example to try overfitting leads to strange predictions

tldr; if I train the network on 1 training example, the outcome sometimes makes no sense at all, sometimes is as expected. If I train it on more examples and higher iterations, the network, which ...
3
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1answer
22 views

Image classification with an associated matrix

I have a dataset of images with 9 different classes. However, there are different categories with the same type of associated image and only can be differentiated with an associated matrix in my ...