Questions tagged [deep-neural-networks]

For questions related to deep neural networks, which are artificial neural networks with "many" layers, where "many" can vary depending on the context.

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42 views

Has “deep vs. wide” been resolved?

All else being equal, including total neuron count, I give the following definitions: wide is a parallel ensemble, where good chunks of the neurons have the same inputs because the inputs are shared ...
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Deep Continuous Clustering algorithm - just one output cluster

I use the DCC algorithm to cluster some data. The whole algorithm is available here, but shortly it is: construct mkNN graph of the data points (the connected components of it are the clusters). ...
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What exactly is an interpretable machine learning model?

From this page in Interpretable-ml book and this article on Analytics Vidhya, it means to know what has happened inside an ML model to arrive at the result/prediction/conclusion. In linear regression, ...
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39 views

What are the rules behind vector product in gradient?

Let's suppose we have calculated the gradient and it came out to be $f(WX)(1-f(W X))X$, where $f()$ is the sigmoid function, $W$ of order $2\times2$ is the weight matrix, and $X$ is an input vector of ...
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57 views

What are the mathematical prerequisites needed to understand research papers on neural networks? [closed]

I know we have developed some mathematical tools to understand deep neural networks, gradient descent for optimization, and basic calculus. Recently, I encountered arxiv paper that describes higher ...
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1answer
46 views

Why does using a higher representation space lead to performance increase on the training data but not on the test data?

I read the following from a book: You can intuitively understand the dimensionality of your representation space as “how much freedom you’re allowing the model to have when learning internal ...
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28 views

How to understand this NN architecture?

I was reading a paper Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks and I was stuck understanding the deep neural network architecture that was used. The ...
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1answer
41 views

What type of model should I fit to increase accuracy?

Currently, I'm working on 6-axis IMU(Inertial Measurment Unit) dataset. This dataset contain 6 axis IMU data of 7 different drivers. The Imu sensor attached on vehicle. The drivers drives same path. ...
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1answer
23 views

How is weighted average computed in Deep Q networks

I was going through the Sutton book and they said the update formula for Q learning comes from the weighted average of the returns I.e New estimate= old estimate +alpha*[returns- old estimate] So by ...
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14 views

How can I decrease the time to compute the mask in the Mask-RCNN for human body detection?

I am using Mask-RCNN to detect human bodies in photos, to get a rough approximation of the ratio of their heights to the length of their chests. I want to decrease the time for making the mask of the ...
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9 views

Generative Adversarial Network with two images in input

I am doing an internship project regarding deep learning, and it is a totally new topic for me as I have never studied machine learning in the bachelor's degree courses. I have to implement a GAN that ...
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2answers
113 views

My Deep Q-Learning Network does not learn for OpenAI gym's cartpole problem

I am implementing OpenAI gym's cartpole problem using Deep Q-Learning (DQN). I followed tutorials (video and otherwise) and learned all about it. I implemented a code for myself and I thought it ...
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31 views

What is the state-of-the-art algorithm for neural style transfer?

I've read the paper A Neural Algorithm of Artistic Style by Gatys et. al. and I find the application of neural style transfer very fun. I also read that Exploring the structure of a real-time, ...
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31 views

Why do we use the Target Network for action evaluation in Double deep Q networks

Is there any specific reason as to why The target Network is used for evaluation and The online network Is used for selection, what would be the difference if both roles were switched, our online ...
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44 views

Is it known how neural networks work? [duplicate]

There was a chess neural network AlphaZero that taught itself how to play chess better than any chess program that has been created before in just 4 hours. I'm not asking to have it explained to me ...
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69 views

Why does not the deepAR model of Amazon require the time series being stationary, as opposed to ARMA model?

As what the title said. Does not deepAR require the time series being stationary?
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44 views

Possible reasons that validation recall is fluctuating across different epochs but the precision is stable?

I'm training a deep learning model. After each epoch I measure the performance of the model on validation set. Here is how the performance looks like while training: It's a binary classification task ...
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1answer
76 views

How does the target network in double DQNs find the maximum Q* value for each action?

I understand the fact that the neural network is used to take the states as inputs and it outputs the Q-value for state-action pairs. However, in order to compute this and update its weights, we need ...
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37 views

How can one be sure that a particular neural network architecture would work?

Traditionally, when working with tabular data, one can be sure(or at least know) that a model works because the included features could explain a target variable, say "Price of a ticket" ...
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46 views

Advantages of training Neural Networks based on analytic success criteria

What is the reason to train a Neural Network to estimate a task's success (i.e. robotic grasp planning) using a simulator that is based on analytic grasp quality metrics? Isn't a perfectly trained NN ...
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1answer
63 views

Why do we need target network in deep Q learning? [duplicate]

I already know deep RL, but to learn it deeply I want to know why do we need 2 networks in deep RL. What does the target network do? I now there is huge mathematics into this, but I want to know deep ...
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31 views

DQN Tic-Tac-Toe does not quite become optimal

I am trying to implement a DQN agent for playing standard 3x3 Tic-Tac-Toe (it is a double DQN with experience replay, and using a target network). I got the hyperparameters to the point where the ...
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22 views

Computational difference between ANN and Pattern Matching

Conceptually, if you had an internal 3d model of all objects in CV you could do a scan matching algorithm. This algorithm would be ridiculously computationally intensive, but it would have a high ...
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39 views

Why are YOLO Darknet weights so heavy?

I have been trying to understand how YOLO Darknet works, and for the most part reading the documentation and checking the code helps me understand. But when it comes to the weight file, I can't find ...
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17 views

Separated LSTMs or a global one for cluster of related features

I have an $n$-dimensional time-series to apply LSTM to, $n$ is the number of features for each time point. These features can be clustered according to their concept, for example $n_1, ..., n_4$ are ...
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1answer
73 views

Can we use a pre trained Encoder (BERT, XLM ) with a Decoder (GPT, Transformer-XL) to build a Chatbot instead of Language Translation?

I was wondering if the BART or T5 models can do the task of generating sentences in English. Most of the models I have mentioned ...
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1answer
48 views

What's the right way of building a deep Q-network?

I'm new to RL and to deep q-learning and I have a simple question about the architecture of the neural network to use in an environment with a continous state space a discrete action space. I tought ...
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81 views

Understanding the role of the target network in this DQN algorithm

I've found online this interesting algorithm: From what I understand reading this algorithm, I can't figure out why I should "perform the opposite action" and consequently storing that second ...
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26 views

How can I find the similar non-zero connections between different levels of sparsity of the same network?

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, ...
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1answer
45 views

Random value generator using a single neuron or DNN

AI is supposed to do anything human or traditional computer can do, that is what we expect AI to be. So 'generating random value' is also a task included in the scope that AI should be able to do I'...
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20 views

How to stabilize the training of a Conv-Siamese Neural Network if the results after different trainings vary relatively strongly?

I am training a neural network using MSE and ADAM optimizer. More precisely, a siamese architecture with a convolutional encoder and euclidean distance on top. I am using MSE because I have different ...
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19 views

Deep Network with constraint or auxiliary features

The target of my current neural network is to predict a label. The dataset contains some features, there is a label $y_i$ in transaction $i$, indicating its classification. There is one feature $f^{i}...
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59 views

Why Pixel RNN (Row LSTM) can capture triangular contexts?

I'm reading the paper Pixel Recurrent Neural Network. I have a question about Row LSTM. Why Row LSTM can capture triangular contexts? In this paper, the kernel of the one-dimensional convolution ...
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44 views

Are there any commonly used discontinuous activation functions?

Are there any commonly used activation functions (e.g. that take values in $(0,.5)\cup (.5,1)$)? Preferably for classification? Why? I was looking for commonly used activation functions on Google, ...
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1answer
32 views

How to use convolution neural network in Deep-Q?

I currently have a grid of pixels 20x20. Each pixel can be red green blue or black. So I have one hot-encoded the pixels giving a 20x20x4 array for each screen. For my Deep-Q Network, I have ...
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1answer
221 views

How to detect vanishing gradients?

Edit: I've reworked my question to generalize better and be more on-topic, and be mostly software implementation agnostic. Can vanishing gradients be detected by the change in distribution (or lack ...
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0answers
26 views

Steps to train and re-train a good model

I'm still a bit new to deep learning. What I'm still struggling, is what is the best practice in re-training a good model over time? I've trained a deep model for my binary classification problem (...
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2answers
53 views

Training accuracy vs validation accuracy on deep models

I'm training a deep network in Keras on some images for a binary classification (I have around 12K images). Once in a while, I collect some false positives and add ...
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0answers
22 views

Post-classification after inference

I designed a fire detection using Deep Learning based classification approach. In my training dataset, I have both fire and fire smokes are supposed to be detected (all under "fire"; mostly real fires ...
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1answer
95 views

How many layers exists in my neural network?

I have a neural network model defined as below. How many layers exist there? Not sure which ones to count when we are asked about the number. ...
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1answer
42 views

Multiple GPUs one expensive GPU, which gpu to buy for real time processing (not training) [closed]

I am trying to decide what GPU or GPUs to buy to run tf-pose pose detection and yolo3 object detection on several cameras. I need to keep an acceptable frame rate too. what kind of GPU configuration ...
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16 views

Can a trained Vid2Vid model be run on AMDs Ryzen 2700x with 32GB of RAM?

I know that training deep neural networks (DNNs) takes a lot of computational resources. This is, of course, just a generalized statement. Different networks require different resources. One that I ...
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43 views

Relationship between model complexity (depth) and dataset size

I'm new to deep learning. I was wondering what's the relationship between a deep model complexity (e.g. total number of parameters, or depth) and the dataset size? Assuming I want to do a binary ...
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104 views

Should I use deep learning to solve my task?

I need to predict the performance (CPI cycles-per-instruction) of 90 machines for the next hour (or day). Each machine has a thousand records (e.g. CPU and memory usage). Currently, I am using a ...
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2answers
110 views

what will i be able to do in the end of AI: modern approach? [closed]

i just started the book and i was wondering , what will i be able to do in AI by the end of the book ? and more particularly, what is my position with Reinforcement Learning, deep neural networks and ...
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1answer
699 views

What is the difference between Kaldi and DeepSpeech speech recognition systems in their approach?

I would like to know how do Kaldi and DeepSpeech speech recognition systems differ algorithmically? Which one would be more accurate for continuous speech in time?
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1answer
36 views

Techniques and semantics in better training of deep learning models

I'm relatively new to Deep Learning, and trying various models and datasets using Keras. I'm starting to love it! Through-out my experimentations, I have come into ...
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1answer
70 views

Can Google's patented ML algorithms be used commercially?

I just find that Google patents some of the widely used machine learning algorithms. For example: System and method for addressing overfitting in a neural network (Dropout?) Processing images using ...
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1answer
59 views

Is there anything theoretically revolutionary about Deep Neural Network?

In recent years we have seen quite a lot of impressive display of Deep Neural Network (DNN), as demonstrated most famously by AlphaGo and its cousin programs. But if I understand correctly, deep ...
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0answers
44 views

Which deep neural networks are appropriate for the detection of bombs?

This is a follow-up question from my previous post here about explosion detection. I gathered a dataset of explosions. As I'm new to Deep Learning in Keras, I'm trying to see what architecture best ...