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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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1answer
82 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 ...
4
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2answers
654 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 ...
14
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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! ...
1
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1answer
373 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 ...
5
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2answers
241 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 ...
0
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1answer
77 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
526 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 ...
5
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1answer
645 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, ...
3
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2answers
792 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 ...
5
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2answers
172 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 ...
9
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1answer
321 views

How much of a problem is white noise for the real-world usage of a DNN?

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 ...
0
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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 ...
10
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5answers
2k views

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 ...
4
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1answer
608 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 ...
3
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2answers
73 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 ...
0
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1answer
479 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, ...
3
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1answer
608 views

Dataset containing images of varying dimensions and orientations

I am new to deep learning. I have a dataset of images of varying dimensions of a certain object. A few images of the object are also in varying orientations. The objective is to learn the features ...
2
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1answer
132 views

Network representation for Q-Learning in carrom

I am trying to build an agent to play carrom. The problem statement is roughly to estimate three parameters (normalized) : force angle of striker position of strike Since the state and action ...
12
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2answers
586 views

Should deep residual networks be viewed as an ensemble of networks?

The question is about the architecture of Deep Residual Networks (ResNets). The model that won the 1-st places at "Large Scale Visual Recognition Challenge 2015" (ILSVRC2015) in all five main tracks: ...
2
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1answer
297 views

How does deepmind's Atari game AI work?

I know that deepmind used deep Q learning (DQN) for its Atari game AI. It used a conv neural network (CNN) to approximate Q(s,a) from pixels instead of from a Q-...
4
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1answer
175 views

What can be done to correct for sampling bias introduced from (noisy) training data while training a DNN?

The obvious solution is to ensure that the training data is balanced - but in my particular case that is impossible. What corrections can one perform in such a scenario? I know that my training data ...
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1answer
223 views

How many neurons would a network have after a training of 100k small images?

Is there any way to estimate how big the neural network would be after training session of 100,000 unlabeled images for unsupervised learning (like in STL-10 dataset: 96x96 pixels and color)? Not the ...
3
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2answers
210 views

Are there any learning algorithms as powerful as “deep” architectures?

This article suggests that deep learning is not designed to produce the universal algorithm and cannot be used to create such a complex systems. First of all it requires huge amounts of computing ...
6
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1answer
140 views

Has any research been done on DNN Music?

DNNs are typically used to classify things (of course) but can we let them go wild with sounds and then tell them if we think it sounds good or not? I'd like to think after a training class has been ...
1
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1answer
51 views

Do you need single or multiple networks to detect multiple faces?

Given pictures with multiple features such as faces, can single AI algorithm detect all of them, or for better reliability is it preferred to use separate instances? In other words I'm talking about ...
8
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2answers
616 views

What's the difference between hyperbolic tangent and sigmoid neurons?

Two common activation functions used in deep learning are the hyperbolic tangent function and the sigmoid activation function. I understand that the hyperbolic tangent is just a rescaling and ...
6
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4answers
1k views

Which machine learning algorithm is used in self-driving cars?

Which deep neural network is used in Google's driverless cars to analyze the surroundings? Is this information open to the public?
8
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3answers
172 views

What is a deep neural network?

What is the definition of a deep neural network? Why are they so popular or important?
63
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9answers
5k views

How is it possible that deep neural networks are so easily fooled?

The following page/study demonstrates that the deep neural networks are easily fooled by giving high confidence predictions for unrecognisable images, e.g. How this is possible? Can you please ...
24
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2answers
811 views

How is a deep neural network different from other neural networks?

How is a neural network having the "deep" adjective actually distinguished from other similar networks?
21
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4answers
314 views

Is the pattern recognition capability of CNNs limited to image processing?

Can a Convolutional Neural Network be used for pattern recognition in a problem domain where there are no pre-existing images, say by representing abstract data graphically? Would that always be less ...
9
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1answer
163 views

What kind of problems require more than 2 hidden layers?

I've read that the most of the problems can be solved with 1-2 hidden layers. How do you know you need more than 2? For what kind of problems you would need them (give me an example)?
2
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1answer
172 views

How can generalization error be estimated?

How would you estimate the generalisation error? What are the methods of achieving this?
9
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4answers
1k views

What is the “dropout” technique?

What purpose does the "dropout" method serve and how does it improve the overall performance of the neural network?
3
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1answer
62 views

What are the methods of optimizing overfitted models?

I'm worrying that my network has become too complex. I don't want to end up with half of the network doing nothing but just take up space and resources. So, what are the techniques for detecting and ...
37
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5answers
2k views

What is fuzzy logic?

I'm new to A.I. and I'd like to know in simple words, what is the fuzzy logic concept? How does it help, and when is it used?
28
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4answers
890 views

How to find the optimal number of neurons per layer?

When you're writing your algorithm, how do you know how many neurons you need per single layer? Are there any methods for finding the optimal number of them, or is it a rule of thumb?