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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22 views

How to represent sentences with their dependency parses as input to an RNN?

I am working on a task embedding sentences into a lower-dimensional space according to style, both grammatical and lexical. As such, I want to have as input the linear ordering of tokens in each ...
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100 views

Clarification in model precision term for Gaussian Processes

Following the blog post, where the dropout in the deep learning models has been approximated to a Gaussian process. Research paper and its appendix. Looking at the suggestion of the author from the ...
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84 views

What is the practical difference between an neuromorphic processors and GPU's?

IBM is working on a neuromorphic computer, Synapse, intended for computation based on artificial neural networks. My question is, what makes these types of computers more efficient at processing ...
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47 views

When is currying more efficient in deep neural nets?

I'm reading a blog post on deep Q-learning, and it contrasts traditional lookup-table-based Q-learning with deep Q-learning: What I wonder about in this picture is: Why does the deep NN not also ...
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87 views

Was there a phase in Machine Learning timeline when researchers thought some Neural Networks could not be trained?

I was talking to a professor who made a comment to my question. Me: So much of quality literature around this topic ( IP Protection for Neural Weights) emanated in 1990-1991, I'm truly at loss ...
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601 views

What are the inputs to an LSTM for Slot Filling Task

I am confused on the inputs of a Long-Short Term Memory (LSTM) for the slot filling task in Spoken Language Understanding. Before I worked on this, I implemented a language model with a Recurrent ...
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712 views

Square or circle neural network detection

I am trying to use a simple perceptron to recognize if there is a square or a circle on an image. The images I generated are 300x300 px and I am having issues training the network since the images are ...
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70 views

Number of parameters to be optimized in Artificial Bee Colony

I was reading this paper - Software defect prediction using cost-sensitive neural network by Ömer Faruk Arara and Kürsat Ayan It uses Artificial Bee Colony algorithm to train the neural network. In ...
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836 views

The runtime of a neural net with given numbers of observations, features, and neurons

If I have $n$ training observations, $m$ number of features per observation, and my neural network has $x$ neurons in the 1st layer, $y$ neurons in the 2nd layer, and 1 output neuron, what is the ...
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439 views

What is the activation function, label and loss function for Hierachical Softmax

Several papers(1 (originator), 2, 3) suggest the use of Hierachical Softmax instead of softmax for classification where the number of classes is large (eg many thousand). I haven't been able to get ...
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88 views

How to select topology for neural network?

I was given a target function to design a neural network and train: $y = (x_1 \wedge x_2) \vee (x_3 \wedge x_4)$ The number of inputs and outputs seems obvious (4 and 1). And the training data can ...
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123 views

practical use of a Boltzmann machine

I am reading "Neural Networks and Learning Machines" and in Chapter 11 the book covers Boltzman machines and it is stated "the network [Boltzmann machine] can perform pattern completion", but does not ...
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33 views

Efficient top eigenvalue computation for Hessian of neural networks

I train a neural network - one of the Resnet variations ($\approx 10^7$ parameters) on the CIFAR-10 dataset - and after each epoch, I would like to find the smallest/largest eigenvalues of its Hessian....
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24 views

Simple back propagation example

Sorry if this is too simplistic of a question, but over the last couple of months I have been working through the course mathematical foundations of machine learning at my college. I think I am really ...
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34 views

How do convolution layers work?

So let's say I have a 4x4 image with 3 channels. So we have 3 4x4 matrices where each matrix represents a channel. Now let's say I also have a 3x3 kernel. I know that for convolution layers, I have to ...
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97 views

Learning the activation function in a neural network?

Neural networks use specific activation functions, commonly used ones are tanh, ReLu. I have seen that people have experimented with continuously parametrices activation functions, for instance here. ...
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33 views

Multilayer Perceptrons for solving variational problems

Can we use a multilayer perceptron to solve variational problems? By variational problem I mean something we might encounter in the calculus of variations, for example the geodesic problem: given two ...
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101 views

Approximate dot product between neural network output layer's parameter vector and input activations with winner-take-all hashing

In the paper Deep Networks with Large Output Spaces, Vijayanarasimhan et al. describe their approach to approximating the dot product between a neural network's output layer's parameter vector and ...
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27 views

How often should I read out information from an echo state recurrent neural network?

Recurrent neural networks makes it possible to implement some kind of memory, which can be very useful for a lot of tasks, incl. (but not limited to) robot control, which I am interested in. For ...
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232 views

Multilayer perceptron memory requirements

How much memory do we need to train a multilayer perceptron? I've started to figure this out myself, but I'm stuck. I have one-layer MLP. Each training example is a vector of 100 real numbers in ...
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115 views

Trying to understand Le's “cat paper”

QV Le et al. show in Building high-level features using large scale unsupervised learning (2012) how to use unsupervised learning of a deep neural net to recognize faces and cats. Working through this ...
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35 views

is mappings optimization problems onto neural networks still a thing?

Is mappings optimization problems onto neural networks still a thing? I only found papers are written prior to 1999. These old papers mostly deal with Hopfield network, which i read is obsolete and ...
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66 views

How to best model multidimensional, continuous, non-convex “shape” as neural network?

I have: set of n-dimensional points that I know are inside of the shape n >= 18, range on all dimensions has upper bound and lower bound (no axis goes to infinity). shape is pretty large in this n-...
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90 views

KDD Machine Learning using K-NN Algorithm Classification Problem

I'm trying to solve a classification problem from the KDD cup archive of 2004. Details can be found here: KDD 2004 Archive I'm only dong the particle physics part. The description of dataset is as ...
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12 views

What would happen if a multi-layer perceptron is insufficient to learn a function?

Suppose that something is wrong with its configuration (ie topology), for example there are too few neurons in a certain layer, or not enough layers. I have an intuition that some neurons' "delta" ($\...
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471 views

How to select the maximum weight value for a bias node in a neural network?

I'm programming a neural network. I know that I should initialize the network by picking random weights. How do I pick a random weight for the connections to bias nodes? What distribution should I ...
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101 views

How can you use HMMs and ANNs for on-line handwriting recognition?

On-line handwriting recognition is the task of converting a series of $(x(t),y(t))$ coordinates to symbols and words. In contrast to off-line handwriting recognition, where you only have a bitmap of ...
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1answer
19 views

Binary cross Entropy derivative?

I am just learning backpropagation algorithm for NN and currently I am stuck with the right derivative of Binary Cross Entropy as loss function. Here it is: ...
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1answer
31 views

Expressivity of neural networks, how much information can be stored

I want to know whether a given neural network (with a finite number of nodes) is able to store all injective maps f: D -> C, where D has cardinality k and C has cardinality N (so the number of maps ...
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37 views

In a machine learning system, why use differentially private SGD if our input data is already perturbed by a DP mechanism?

I'm trying to implement my own version of a deep neural network with differential privacy to preserve the privacy of the parties involved in the training dataset. I'm using the method by Abadi et al. ...
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33 views

How can Kneser-Ney Smoothing be integrated into a neural language model?

I found a paper titled Multimodal representation: Kneser-Ney Smoothing/Skip-Gram based neural language model. I am curious about how the Kneser-Ney Smoothing technique can be integrated into a feed-...
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16 views

Why do we need to change the (weight decay) regularization parameter when changing the number of inputs that neural network is being trained with?

I am currently working my way through Michael Nielsen's ebook Neural Networks and Deep Learning and I am reading about overfitting and (L2) regularization. In this subsection, the process of L2 (a.k.a ...
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8 views

Is there a way to connect a deep language model output to input?

In models like GPT-2, TXL and Grover, is there a good way to know which input weights (tokens) resulted in each token of the output?
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23 views

Value flow (and economics) in stacked reinforcement learning systems: agent as reinforcement environment for other agents?

There is evolving notion of stacked reinforcement learning systems, e.g. https://www.ijcai.org/proceedings/2018/0103.pdf - where one RL systems executes actions of the second RL system and it itself ...
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1answer
148 views

What does “Temporal extent” mean?

I am reading Long-term Temporal Convolutions for Action Recognition and under the Section 3.1, I read this: To investigate the impact of long-term temporal convolutions, we here study network inputs ...
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135 views

Converting (reverse-engineering) Turing machine into program or most concise algorithm?

It is known that every program or every algorithm can be converted to Turing machine. But what about the reverse process? Is there algorithm (or research trend that considers such algorithm) to ...
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411 views

Why is the method of im2col with GEMM is more efficient than the method of direction implementation with SIMD in CNN

The convolutional layers are most computationally intense parts of Convolutional neural networks (CNNs).Currently the common approach to impement convolutional layers is to expand the image into a ...
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149 views

Proof of perceptron convergence theorem for ZERO threshold?

The generalized perceptron convergence theorem is for a defined threshold T. When you do the maths it all comes to an upper bound and a lower bound. The lower bound looks like this! Therefore ...
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350 views

How can node2vec help find similar “roles” within a graph (nodes whose connections have similar structure within the graph)?

I have a question on the node2vec algorithm described in this paper. Node2vec is a deep learning algorithm that word2vec to graphs to learn embeddings. The authors claim that it can help find nodes ...
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28 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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33 views

How would you go about creating a algorithm that should generate a shakespearean sonnet on any given theme

I need to create an algorithm that is going to create a shakespearean sonnet for a specific theme. This theme should be generated out of twitter tweets that have some hashtag. My current idea goes ...
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615 views

Backward mapping with bilinear sampler

I have some experiences with Convolutional Neural Networks before. I have a question regarding the Bilinear Sampler used in "Unsupervised Monocular Depth Estimation With Left-Right Consistency" (the ...
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40 views

Does a deep feedforward neural net approximate a (single or multivariate)polynomial?

Does composition of several linear transformation plus non-linear activation function in each layer and different layers (as they are in feedforward neural net) represent or approximate a ...
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159 views

Recurrent neural networks (Hopfield-like) with short limit cycles

Standard Hopfield networks exhibit stable patterns (states) which are attractors of a dynamic system. I wonder how to modify standard Hopfield networks such that they exhibit stable limit cycles as ...
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22 views

How do RNN's map variable-length sequences to variable-length sequences?

According to Karpathy's blog "The Unreasonable Effectiveness of Recurrent Neural Networks", recurrent neural networks can map variable-length sequences to variable-length sequences, as shown by the ...
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201 views

Distributing Neural Network Training World-Wide Using Blockchain?

I just finished reading a white-paper from a recent AI startup. The company, Deep Brain Chain, wants to distribute neural network training over computers worldwide, using blockchain technology. Here ...
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119 views

adjusted fitness in NEAT algorithm

I'm learning about NEAT from the following paper: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf I'm having trouble understanding how adjusted fitness penalizes large species and prevents ...
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49 views

Detecting shapes using CNNs in respect to other shapes in the image

I'm solving a problem of detecting an optic disc in a retina image. As you can see from the image: the optic disc is the epicentrum of the blood vessels, has an irregular circular shape and has a ...
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152 views

Can CNN be used for “unknown correlated” data?

Now the title may be misleading, but I don't know how to give it a better name. My question is if I have data in a 2d matrix and under the assumption that: -I know for sure that there are ...
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50 views

Finding a line shape objects (bars) in video images using neural networks (instead of Hough transform)

I'm currently trying to learn about computer vision stuff and I want to try it on a simple project of real time tracking of figures of players in a foosball game while the camera can be moving a ...