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Questions tagged [deep-network]

For questions about deep neural networks (DNNs), neural networks with multiple hidden layers between the input and output layer.

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What is the feasible neural network structure that can learn to identify types of trajectory of moving dots?

I have multiple image sequences, each of which contains an animation of two moving dots. The trajectory of the dots in a sequence is always cyclic (not necessarily circular). There are two types of ...
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How to use 0 padding with mask layer to handle variable lenght of my inputs in case of Multi-Layers Perceptrons?

We wanna build a DNN model to predict unrolling factor though our features represent variable length of inputs. Knowing that we have to give our features at once "0 padding" look like the only ...
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29 views

How to handle varying length of inputs that represent dependencies and recursivity in deep neural networks in case of regression?

I wanna solve a problem of regression to predict a factor. I decide to go with Deep Neural Networks as solution for my problem. The features in this problem represent loop characteristic such us loop ...
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29 views

Contractive auto-encoders

I am trying to implement Contractive auto-encoders in PyTorch but I don't know what I'm doing is right or not. The architecture of the auto-encoder is given below: ...
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1answer
252 views

Suitable reward function for trading buy and sell orders

I am working to build an deep reinforcement learning agent which can place orders (i.e. limit buy and limit sell orders). The actions are ...
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1answer
74 views

Alpha zero before move 8

The Alpha zero paper says that the The first set of features are repeated for each position in a T = 8-step history. So what happens before the first 8 moves? Do they just repeat the starting position?...
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1answer
144 views

Deep Q-Learning poor convergence on Stochastic Environment

I'm trying to implement a Deep Q-network in Keras/TF that learns to play Minesweeper (our stochastic environment). I have noticed that the agent learns to play the game pretty well with both small and ...
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1answer
104 views

Significance of depth of a deep neural network

How is a feed-forward neural network with few hidden layers and lots of nodes in those hidden layers different from a network with a lot of hidden layers but relatively lesser nodes in those hidden ...
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1answer
92 views

Chess policy network

I am interested in making a simple chess engine using neural networks. I already have a fairly good value network but I can't figure out how to train a policy network. I know that Leela chess zero ...
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2answers
184 views

How to create Partially Connected NNs with prespecified connections using Tensorflow?

I'd like to implement a partially connected neural network with ~3 to 4 hidden layers (a sparse deep neural network?) where I can specify which node connects to which node from the previous/next layer....
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1answer
57 views

CNN design acceleration

I'm praying to develop a CNN for image analysis, I've around 100K labeled images. I'm getting an accuracy around 85% with a val_acc arround 82%, so it looks like the model generalize better than fits....
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1answer
115 views

Target values of 0.1 for 0 and 0.9 for 1 for sigmoid

I recently read an article about neural networks saying that, when using sigmoid as activation function, it's advised to use 0.1 as target value instead of 0, and 0.9 instead of 1. This was to avoid "...
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Using two generative adversarial nets to classify articles - what is a good approach?

I'm trying to create a deep learning network to classify news article based on the text and associated image. The idea comes from a novel use of GANs to classify based on generated data. My approach ...
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51 views

Confidence interval around a DNN prediction

I am facing a problem and do not know whether it is even solvable: I want to predict the behaviour of a system using a DNN, say a CNN, in the sense that I want to predict the time and intensity of a ...
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1answer
1k views

Batch Normalization in Deep Autoencoders?

Does it make sense to use Batch Normalization in Deep (stacked) or/and Sparse Autoencoders? I cannot find any resources for that, so is it safe to assume that since it works for other DNNs it will ...
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1answer
274 views

Sparsity constraint in a deep autoencoder

Is there any way and any reason why one would introduce a sparsity constraint on a deep autoencoder? In particular, in deep autoencoders the first layer often has more units than the dimensionality ...
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114 views

Deep learning model (LSTM) with temporal and non temporal attributes

I'm working on a project to predict the usage of all the files in a filesystem in near future based on the metadata of the file system for past 6 months. I've got the following attributes about the ...
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2answers
118 views

How to build my own dataset and model for an LSTM neural network

I have a sort of mathematical problem and I'm not sure which model I should choose to make an LSTM neural network. Currently in my country, there is a system in which certain groups of researchers ...
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2answers
169 views

What do neural connection weights represent 'conceptually'?

I understand how Neural Networks work and have studied its theory well. My question is at the intricacies of Deep Neural networks and perhaps is a bit beyond common understanding (as I have been told (...
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1answer
142 views

Elon musk's comment on “non-benign AI scenarios”

I watched a youtube clip of Elon Musk talking about his view on the future of AI. He gave two examples. One of the examples was a benign scenario and the other example was a non benign scenario where ...
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1answer
109 views

Regression with more than one output, neural network

Currently in my country, there is a system in which certain groups of researchers upload information on products of scientific interest, such as research articles, books, patents, software, among ...
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1answer
322 views

what's the definition of singularity in the context of neural networks?

The following paper explains the use of skip connections to break the singularity in deep networks. But, I have not fully understood what singularity is. https://arxiv.org/pdf/1701.09175v8.pdf Any ...
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1answer
34 views

Do Le et al. (2012) train all three autoencoder layers at a time, or just one?

Le et al. 2012 use a network of 1 billion parameters to learn neurons that respond to faces, cats, pedestrians, etc. without labels (unsupervised). Their network is built with three autoregressive ...
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1answer
82 views

How can neural networks that extract many features be fooled by adversarial images?

I have been reading a bit about networks where deep layers able to deal with a bunch of features (be it edges, colours, whatever). I am wondering: how can possibly a network based on this '...
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3answers
239 views

Has anybody tried unsupervised deep learning from youtube videos?

YouTube has a huge amount of videos, many of which also containing various spoken languages. This would presumably provide something like the data that a "challenged" baby would experience - "...
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3answers
143 views

What is the most time consuming part of training deep networks?

Deep networks notoriously take a long time to train. What is the most time consuming aspect of training them? Is it the matrix multiplications? Is it the forward pass? Is it some component of the ...
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1answer
1k views

Implementing the “original” NEAT algorithm in JavaScript

I've recently read the original paper about NeuroEvolution of Augmenting Topologies by Kenneth O. Stanley and am now trying to prototype it myself in JavaScript. I stumbled across a few questions I ...
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2answers
90 views

Should the actor or actor-target model be used to make predictions after training is complete (DDPG)?

The situation I am referring to the paper T. P. Lillicrap et al, "Continuous control with deep reinforcement learning" where they discuss deep learning in the context of continuous action spaces ("...
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3answers
915 views

What activation function is not used at the final layer of super resolution neural models?

I'm trying to implement some Image super-resolution models on medical images. After reading a set of papers, I found that none of the existing models use any activation layer for the last layer. What'...
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1answer
204 views

The connection between number of layer of DNN and computational complexity of it

number of layer of DNN and computational complexity of it are correlated after optimization, but how to estimate it before designing DNN?
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25 views

Are there a finite set of computable functions constructing deep neural network which can form or implement any c.e. function or computable function?

Are there a finite set of computable functions constructing deep neural network which can form or implement any c.e. function or computable function? Or does there exist a finite set of computable ...
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65 views

How would I implement this New Type of NN

CIO NN CIO NN stands for Controller Input Output Nerual Network note due to a typo the "nearon" means "neron" For this we have to redefine the Nearon 2 Inputs 2 Outputs 4 Weights (each input and ...
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2answers
73 views

Is there any common principle/ build algorithm for deep NN structure?

I started to study NN recently. So I understand principles with which I should define input and output layers. But I can't find any guide/directions how to build hidden layers: how many layers do I ...
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63 views

input layer in deep learning

I am building model with medical dataset using deep learning methods. Medical dataset consists of both numerical data such as age, sex and images of xray scans(1024 x 1024) . Labels consists of ...
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2answers
454 views

How do I create an ai for a two players board game?

Brief idea I want to create an artificial intelligence to compete against other players in a board game. Game explanation I have a board game similar to 'snakes and ladders'. You have to get to a ...
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2answers
1k views

What's the main concept behind Capsule Networks? [duplicate]

As you might know, Capsule Networks have been recently introduced by Hinton. There also have been several heads up within his talks. As expected, the paper elaborates on the idea way theoretically! ...
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1answer
305 views

Dense-Sparse-Dense CNN training

I want to implement DSD: Dense-Sparse-Dense training for deep neural networks by Han et al. In short, the paper suggest the following training scheme to improve the network accuracy: Train as usual ...
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2answers
231 views

Is it possible to construct an ANN that is more efficient than the human brain?

Intelligence ... changes based on the environment and situation Human are now inventing machines exhibiting some features of their own Intelligence. There appears to be a possibility that, in the ...
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1answer
75 views

What are the pros and cons of using a spatial transformation network to predict the next video frame?

I've read through a few papers on next frame prediction from a sequence of frames and several of them use spatial transformations (STNs). See this as an example. I want to know what are the pros and ...
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2answers
472 views

How many GPUs can these deep learning algorithms be parallelized across (batch parallelization)?

The deep learning algorithms I would to know the limits of are: CNTK Caffe TensorFlow Torch7 Theano For example: I've heard TensorFlow is near impossible to parallelize on 8 GPUs and above. So, in ...
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1answer
561 views

Precise localization and characterization of rudimentary shapes with neural networks

I understand that there are flavors of (convolutional) neural networks that are useful for object localization and detection tasks of reasonable difficulty. In all of the examples I have seen so far, ...
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2answers
666 views

Is there any proof based literature out there on neural networks?

Is there any mathematical proof (like in proof of a theorem) based literature out there on neural networks ? Everything is empirically based but no math proof for instance on why certain parameters ...
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2answers
165 views

Neural networks efficiently solve traveling salesmen problems?

I occasionally read papers that show neural networks solving traveling salesmen problems and multi traveling salesmen problems efficiently? 1) Is there any analysis of the meaning of efficiency of ...
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1answer
291 views

Is deep neural network fooling a problem in real world?

I read that deep neural networks can be relatively easily fooled (link) to give high confidence in recognition of synthetic/artificial images that are completely (or at least mostly) out of the ...
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3answers
3k views

SSD or YOLO on arm

Is it possible to run SSD or YOLO object detection on raspberry pi 3 for live object detection (2/4frames x second)? I've tried this SSD implementation but it takes 14 s per frame. Is there anything I ...
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5answers
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Why are deep neural networks and deep learning insufficient to achieve general intelligence?

Everything related to Deep Learning (DL) and deep(er) networks seems "successful", at least progressing very fast, and cultivating the belief that AGI is at reach. This is popular imagination. DL is a ...
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1answer
582 views

Using crowdsourcing for deep learning

Most companies dealing with deep learning (automotive - Comma.ai, Mobileye, various automakers etc.) do collect large amounts of data to learn from and then use lots of computational power to train a ...
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2answers
71 views

AI that knows when its being spoken to

I am trying to make a artificial intelligent agent that is kind of like jarvis from Iron man however much less complex. One thing I want to have is I want my AI to be able to determine if I am talking ...
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1answer
411 views

Questions to the NEAT Algorithm

I have read the NEAT paper and some questions are still bugging me: When do mutations occur? Between which Nodes? When Mating what happens if 2 genes have the same connection but a different ...
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1answer
422 views

Terminology: DBN vs stacked RBM

I'm just diving in this whole new area of knowledge; i happened to lost in all the concepts a bit. What is difference between stacked RBM and deep belief network? Are they the same entity? If so, ...