Questions tagged [neural-networks]

Network structure inspired by simplified models of biological neurons (brain cells). Neural networks are trained to "learn" by supervised and unsupervised techniques, and can be used to solve optimization problems, approximation problems, classify patterns, and combinations thereof.

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31 views

How do we fix area of detectors in object detection?

I have gone through various articles on medium and also some from other sites trying to understand SSD. I am able to figure out most of the things from articles except this one. They always say that ...
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10 views

Architecture of ANN to combine images

I am interested in ANNs that combines two images in single as it shown below: But I don't know how it's called and can't find any papers or tutorials about the architecture of these ANNs. Can ...
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56 views

Crossover topologically identical neural networks

I have recently learned about artificial neural networks (very interesting) and genetic algorithms (also very interesting). I have read some suggestions concerning how to crossover two parent neural ...
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38 views

Finding subgraph in multigraph using deeplearning

I have big multigraph each node represent entity with 0..n attributes(e.g. name, address, salary). My problem is: I will get for example 10 subgraph selected from user and these subgraphs represent ...
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76 views

What is the practical limit to how many object classes you can detect with Faster RCNN?

I am trying to follow this tutorial where the Faster-RCNN-Inception-V2-COCO model from TensorFlow's model zoo is used to detect playing cards. I was wondering what is the practical limit to the number ...
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103 views

How to calculate the weight between neurons in ANN?

I am currently learning Supervised ANN training using Backpropogation and I am stuck in this exercise. I calculated the δA using the equation at the bottom of the screenshot, however, I am unable to ...
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40 views

Formal definition of loss surface of multi-layered networks

Let $\mathcal{L}$ be a loss function associated with a multi-layered neural network. So it seems almost everyone in AI/ML community is interested in the Hessian $H=\partial^2 \mathcal{L}$ of $\...
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65 views

Hessian in reinforcement learning

The Hessian of multi-layered network exhibits known behaviour at critical points as shown in [1]. The tools of random matrix theory allow [2] to deduce the asymptotic distribution of the eigenvalues ...
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50 views

Adding and removing output layer units of a neural network

I'm fairly new to deep learning, so if terminology makes no sense, please let me know so I can clarify what I mean. We're working with a neural network for applying classes to inputs. That is, each ...
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26 views

Classify manifolds with neural networks

Can a neural network be used to find the genus of a 2-manifold given for instance as a CW complex?
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162 views

Reverse engineering a neural network

Suppose we have a neural network (such as google uses for instance) which detects an object in images, which could be a cat or car. Suppose that it is instead an alien artifact that we dont have ...
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32 views

Gradient Descent in MLPs using Computational Graphs

I'm working through Deep Learning by Goodfellow et al. The textbook introduces backpropagation for MLPs in page 203 (http://www.deeplearningbook.org/contents/mlp.html). However, it does not expand ...
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52 views

Machine learning for recommendation systems (feed forward and recurrent neural networks)

I recently started to learn about machine learning. I have created a feed forward neural network (ffnn) and a recurrent neural network (rnn) to predict user ratings of movies. I am using a subset of ...
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1k views

How to feed videos to a neural network

I have been coding and testing Neural Networks for a while but as of now I have only used IMAGE datasets. (i.e. I have M training images and N testing images). Some datasets are video datasets. The ...
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98 views

Convolutional Neural Network for Insects

I've recently started learning CNN's. I need a CNN that is specialized for insects detection. Dead insects will be put on a piece of paper / container, then images will be taken from a same distance, ...
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1answer
746 views

Amount of classes for a semantic segmentation neural network

I am currently working on implementation of semantic segmentation of images neural network, and try to implement one of the already existing solution such as Fully Convolutional Neural Network 1. ...
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182 views

Output distribution of neural network with normally distributed weights

I stumbled upon a statement like the following, in the context of prediction (classification) with a neural network: "If we predict the class of a given data point $x$ with random weights, the output ...
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70 views

Is there something as good as a GRU or LSTM but simpler?

I was just reading this paper: Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks Rahul Dey and Fathi M. Salem It seems to me that perhaps the architecture of LSTMs and GRUs are overly ...
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121 views

RNN learning an iterative algorithm

How do I solve the following problem with a recurrent neural network (RNN)? What architecture should I use for the (conv)-RNN? Let $s \in \mathbb{R}^N$ be a musical signal. We corrupt it with ...
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167 views

Neural Network Inputs For Video Game use (DotA 2)

I am currently working on a project to build and train a neural network to play DotA 2 (at least be able to play like a really bad human). The thing is all the neural network I have built before were ...
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38 views

neural net architecture fails to approximate hybrid functions

I adopted a feedforward net to approximate the following hybrid function: $$ output= \begin{cases} f(x,y) & \text{if}~~~ z=1\\ g(x,y) & \text{if}~~~ z=2 \end{cases} $$ where $x \in R,y \in R,z ...
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63 views

Neural networks fed with training data on demand?

What I want to achieve is to build a neural network with Keras which will have to stabilize a quadcopter I've built. The network would have three inputs: pitch, roll, yaw, acceleration x, accel y and ...
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205 views

How are punishment and negative reinforcement handled in reinforcement learning using neural net function approximation?

Punishment is reducing a behaviour due to bad outcome e.g. A cow stops touching an electric fence because it gets a shock. Negative reinforcement is increasing a behaviour that reduces a bad outcome. ...
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135 views

Reversible 2D Shape Descriptor

This is a research problem and I am just wondering if there is any already existing answer in any computer vision related paper that may have skipped my notice since this is not my active area of ...
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771 views

Can I use machine learning/ANNs to predict the outcome of a simulated horse-racing game based in probability?

I know ML is used in real horse-racing and other sports, where team/player history matters and can be used as a predictor for future games. What about for "simulated" games, where the outcome is ...
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116 views

Replace EKF by Neural Networks

I made a SLAM (Simultaneous Localization And Mapping) using Extended Kalman Filter (EKF) and it works really good, but I want to see if it works better using Neural Networks. For the EKF I used an ...
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1answer
44 views

Is it Possible to Get Maximum Weighted Input Value in a Neural Network?

Let's say that I have a standard feedforward neural network which has $M$ inputs, some number of hidden layers $N$, and a single neuron in the output. Is it possible to construct a network such that ...
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45 views

What is a Neural attention model and how can it be used for text summarization?

We are doing a research on multi-document (one document max 100 words) text summarization. We are looking into abstractive text summarization methods. I need following things to be clarified. Please ...
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299 views

RNN input shape for sequence generation on Tensorflow

I would like to train a RNN with LSTM cells in Tensorflow to predict the next word of a sequence. Words are N-length vectors of 0s and 1s. By looking at different tutorials, I saw that the input ...
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371 views

What is role of parameter learning rate, lr, and momentum constant, mc in Neural Networks?

can anyone describes the more simplified mathematical formulation of learning rate, lr, and momentum constant, mc in Neural Networks while training the data?
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32 views

Something wrong when recognizing data set using Binary Artificial Neural Network

I have learnt Neural Network for a couple weeks. I just met a problem when I tried to use ANN to recognize a dataset. So I'm gonna describe the problem: Set A has 15 members which are in set {1, 1.5, ...
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34 views

How would physical neural nets learn?

I understand how neural networks that are implemented in software learn. You simply change the synaptic weights in the program. But how would you get a hardware implementation of neural networks to ...
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76 views

DFAs can be encoded as input/output for a neural network?

I would encode DFAs (Deterministic Finite State automata) as output (or input) of a neural network for a supervised learning; it is well-known [1] that efficacy of neural network training strongly ...
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59 views

Understanding This Graphical Depiction of a Radial-Basis Function Network

Let $f$ be a Radial-Basis Function Network: $$ f(X) = \sum_{i=1}^N a_i p( \lvert \lvert b_i X - c_i \lvert \lvert) $$ From Artificial Intelligence for Human Beings, the following depicts $f$: In ...
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45 views

choosing right neural network architecture and input features

As a short example suppose we have a kind of pollution sensor and a jet fan in a tunnel. Jet fan turn on/off according to the automatic scenario based on pollution sensor value. Pollution itself ...
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57 views

understanding kohonen self organized feature maps

I was learning self organizing feature maps the other day. I want to intuitively understand it because I'm not that good at math. But I still am not very clear about it. I can easily understand ...
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363 views

Difference between Elman, Hopfield & Hemming Recurrent Neural Networks

What is the main difference between Elman, Hopfield & Hemming Recurrent Neural Networks? Python Neurolab Library examples: Elman Recurrent Neural Network Hopfield Recurrent Neural Network ...
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66 views

Reference for Elliptical Basis Function Neural Networks

I found the following assertion in a neural networks FAQ: Radial networks typically have only one hidden layer, but it can be useful to include a linear layer for dimensionality reduction or ...
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120 views

Lattice of a fuzzy number?

Can somebody define what is a fuzzy lattice? How to compute lattice of a fuzzy number? Please try to be generic and basic in the explanation as I'm a beginner in studying fuzzy theory and soft ...
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72 views

Where to start studying about HTM?

I am looking for references (pedagogic and beginner friendly!) to these two topics, hierarchical temporal memory algorithms applied to deep planning problems (multi-layer) neural networks trained ...
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320 views

Backpropagation Through Time Recursive Algorithm

Would it be plausible to write a recursive version of backpropagation through time for recurrent neural network training? I've only found the iterative version: http://en.wikipedia.org/wiki/...
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1answer
63 views

Is it okay to select the best performers of test cases for scientific publication in neural network machine learning [closed]

If I split my data properly into 75% train, 15% test, and 15% validation, and there are over 100,000 samples, is it appropriate for me to train 100s of neural networks then select only a couple based ...
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2answers
84 views

Different weight sets for same problem?

Considering the case that we have a fixed set of training examples and a fixed ANN (i.e same number of input,output and intermediate layers). Is it possible that there exists more than one set of ...
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1answer
682 views

create a ANN with more than one neuron output

I start to learn artificial neural network and all introduction that I have found on internet present architecture with a number of input variable, so the input layer has the same number of "neurons" ...
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1answer
519 views

Are basic CS algorithms used in machine learning?

I have read some articles which state that basic algorithms such as dynamic programming , graph algorithms etc are not required int machine learning fields such as deep learning , reinforcement ...
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2answers
909 views

Perceptron learning rule for classification

That's the problem $$y=(x,w,\rho) = \begin{cases} 1 & \sum_{i=1}^3 w_ix_i >\rho\\ 0 & \text{otherwise} \end{cases},$$ where $x=\{x_1,x_2,x_3\}$ are inputs, $w=\{w_1,w_2,w_3\}$ are ...
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3answers
386 views

How might neural networks use a symbolic A.I approach?

I’m fairly new to this area so my understanding is pretty basic. Am I correct in saying that symbolic a.i would take data and use logic to search through and find the right answer. An example is a ...
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78 views

Is a Neural Network's weight selection dependent on its architecture? [closed]

Would an optimum combination of weights for a given topology necessarily be the the optimum for a different topology ?
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140 views

In what way can Google deepdream be extended? Is there an image dataset which can use hallucinations produced? Any deepdream useful application? [closed]

Is there a particular section of image data which when trained on deepdream algorithm and given some input image produce a resulting image from which we can conclude that the deepdream can be used for ...
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1answer
140 views

Neural Network Design Challenge

i'm studying for PHD Entrance Exam on Stanford. one of previous material exam designed very challenging. i want to design a NN for classifying following 2-class problem. 1) output should be -1 or ...