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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Why can't we say that a Neural Network is a NP problem solver?

From this video lecture from MIT https://youtu.be/moPtwq_cVH8?t=1229 there is mention how NP complexity works with finding a "lucky" algorithm and luck can never be accounted for. The ...
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1answer
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Signal translation with Seq2Seq model

I'm currently doing some research on signal processing and I got a dataset which includes the signal in itself and its "translation". So I want to use a Many-to-Many RNN to translate the ...
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1answer
30 views

object image segmentation

I have 2 different datasets with similar objects, one where each object is 50 pixels wide and the other where they are 150 pixels. Each photo is 512x512 for both datasets. These two datasets have the ...
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1answer
72 views

How can we get small test error reducing only train error?

My question is about mathematical part of machine learning algorithms, especially about using it in neural networks. We train network reducing train error and I was thinking about how then test error ...
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1answer
21 views

What Happens if I swap the forget gate and update gate in LSTM model?

Consider the following eqautions used in LSTM ( taken from Andrew ng's course on Sequential model) In an LSTM model, LSTM Cell has three inputs at any time step t Input($X_t , a^{(t-1)}, C^{(t-1)})$, ...
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1answer
23 views

How can one measure the time dependency of an RNN?

Most of the discussion about RNN and LSTM alludes to the varying ability of different RNNs to capture "long term dependency". However, most demonstrations use generated text to show the ...
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1answer
36 views

How was the σ function chosen to extend the perceptron?

I am just reading about perceptrons in more depth, and now onto Sigmoid Neurons. Some quotes: A small change in the weights or bias of any single perceptron in the network can sometimes cause the ...
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2answers
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Why don't they use all kinds of non-linear functions in Neural Network Activation Functions? [duplicate]

Pardon my ignorance, but after just learning about Sigmoid and Tanh activation functions (and a few others), I am wondering why they choose functions that always go up and to the right? Why not use ...
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1answer
34 views

Why isn't there just one “keystone” activation function in Neural Networks?

This article says the following: Deciding between the sigmoid or tanh will depend on your requirement of gradient strength. I have seen (so far in my learning) 7 activation functions/curves. Each ...
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43 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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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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3answers
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Guided Backpropagation in Deep Neural Networks

I am reading about a method called guided backpropagation. https://ramprs.github.io/2017/01/21/Grad-CAM-Making-Off-the-Shelf-Deep-Models-Transparent-through-Visual-Explanations.html#deconv-and-guided-...
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1answer
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Mcculloch and Pitts Neurons: Is there errata in this article?

The paper A LOGICAL CALCULUS OF THE IDEAS IMMANENT IN NERVOUS ACTIVITY has the following description of neurons, which I question if there is errata or perhaps I need more information to understand ...
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1answer
47 views

How does image reconstruction take place in neural network?

I am reading through and thinking about how neural network works and have been reading about convolutional neural networks (CNN). I am particularly interested in image filtering (or enhancing) using ...
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1answer
61 views

Approximating Deep Neural Networks (DNNs) with Binarized Neural Networks (BNNs)

I am working currently as a research intern on Binarized Neural Networks where the weights and the activations of the network are binary. The architecture of this type of networks makes them memory ...
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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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67 views

Explain this Neural Network used for Knowledge Graph Embedding

I am interested in better understanding the neural network used in the paper LogicENN A Neural Based Knowledge Graphs Embedding Model with Logical Rules. To my knowledge this is the most advanced and ...
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1answer
120 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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1answer
96 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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Using the features embedding of the output from a transformers to represent probabilities of categorical data

I was considering using a transformer, on input data which can be represented as an embedding, so I can use the attention mechanism in the transformer architecture. As my data is of variable input and ...
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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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2answers
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Why doesn't relu cause back propegation to get stuck

Say you have a neural net that is being trained using back propagation and you are using relu activation. Say the input to a node is a weighted sum of the previous layer with a bias term and say for a ...
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1answer
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Are non-ordered (Slabbed) Neural Networks in wide modern use and what does Fiesler (1994) mean by Clamping Function and Ontogenic function?

In Fiesler (1994) Neural Network Classification and Formalization, he talks a lot about a more general version of neural networks, one that is not ordered into layers, but rather the network is called ...
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fast and stable x * tanh(log1pexp(x)) computation

$$f(x) = x \tanh(\log(1 + e^x))$$ The function (mish activation) can be easily implemented using a stable log1pexp without any significant loss of precision. Unfortunately, this is computationally ...
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34 views

Delta rule for binary step function

I have a question of understanding about the Delta Rule: $\Delta w_i = (y - \hat{y}) \times x_i$ Why does $x$ have to be multiplied again after the difference? If the input is $0$, the product of $w$...
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1answer
26 views

Performing semantic segmentation on images on word documents

At the moment, I'm making a comparative analysis between two different neural networks for a class. The problem they are both trying to solve is detecting tables on documents. Below is an example of ...
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1answer
39 views

Which algorithm for predicting the next word(s) based on previous words, given a sentence?

I want to input some words, and out comes the next word(s). Neural nets are really hot at the moment, and I'm afraid of throwing a neural net at something, when one is not really needed. Or... maybe ...
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1answer
44 views

Can you apply neural networks to design algorithms?

I’m kind of a newbie to neural networks (and CS in general) but I was wondering if there are any methods to apply them in such a way with the aim of producing algorithms that solve difficult math ...
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2answers
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Zero-Sum Games and Halting Problem

Wikipedia states on the page of the halting problem, "For any program f that might determine if programs halt, a "pathological" program g called with an input can pass its own source and its input to ...
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How to find clusters of a set of points in n-dimensional space that are furthest from unwanted points

I have a list of 25 points and their coordinates in a 512-dimensional space. I have 8 target points and 17 points I need to avoid (the 17 points to avoid also have differences in priority of how ...
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1answer
62 views

What are some complexity classes for neural networks?

Turing machines and neural networks are equivalent in their expressive powers, but as models of computation they are different. Turing machines come pre-configured with their transition functions ...
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1answer
199 views

Predicting next action to take to reach a final state

Does anyone know of an algorithm that could be used to determine the next action to take to reach a desired state when trained on time-series data? For example, a robot starts at a certain state, ...
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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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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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1answer
21 views

Learning a specific functional form with machine learning

Suppose I have only three independent features (x, y, z) as the input to some machine learning routine (e.g. neural network). From some domain knowledge, I know ...
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1answer
9 views

Intersection of two generative neural networks

I have an input x (a word), and I have a neural network Y such that Y(x), which is an image always satisfies F(x), and another neural network Z such that Z(x), which is also an image, always satisfies ...
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1answer
2k views

Understanding the Broyden–Fletcher–Goldfarb–Shanno Algorithm to Select Weights for Neural Nets

I am trying to train and implement a Neural Network. I was reading a few articles, learning about their principles and the math that goes behind them. However, while I was trying to understand the ...
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2answers
162 views

Best practices for normalizing up training, validation, and test sets

I was reading up on how to normalize my training, validation, and test sets for a neural network, when I read this snippet: An important point to make about the preprocessing is that any ...
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0answers
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Artificial Neuron: question on definition and inhibited signals

This is an artificial neuron of a NOT function from Rojas's ML book. I have a question on it's behavior. It's my understanding that the neuron produces a signal if it's inputs $x_1$ summed are $\geq ...
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1answer
52 views

Energy consumption off CNN models

I want to calculate/estimate the energy consumption for the different convolutional neural networks. Is there any possibility to measure the energy consumed by AlexNet for example with a tool or with ...
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2answers
70 views

Can neural network process randomness?

So the question is : Is it theoretically possible to feed a neural network with some random values to expect an output since randomness is a lack of knowledge in most case. For this question, I've ...
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2answers
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Being stuck and frustrated with my masters project

I'm doing a masters in CS that requires me to implement from scratch most of the neural network models and because python libraries aren't applicable to what i want. The problem is that i don't feel ...
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3answers
759 views

Support Vector Machines as Neural Nets?

This is more of a conceptual question. I have learned about Neural Nets, and I have some clue as to how Support Vector Machines work. I read somewhere however that given the appropriate kernel (is ...
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1answer
51 views

Understanding how convolution of images work in cnn

I'd like to understand convolutional neural network. Consider this quote from Stanford CS notes on Convolutional Neural Networks for Visual Recognition (CNNs/ConvNets): Example Architecture: ...
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Learning rule of hopfield network with sigmoidal activation function

I am going to build a hopfield network with continuous-valued units. In this article it is said that hopfield network units can have sigmoidal activation function (soft-limiting). If units can have ...
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2answers
31 views

Neural network game players and incremental updates

Neural networks in recent years have been successfully used for gameplaying. A difference between games and e.g. image processing is that the game boards get updated incrementally. Do any neural ...
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1answer
25 views

Using a point as an input for a perceptron

Can a point be used as the input for a perceptron/neural net? The relationship between the two numbers that make up a 2D point does not affect the output, but does this not matter when the ...
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218 views

Why does the effectiveness of my reinforcement based neural network recede after a while?

I have a reinforcement based neural network training on the OpenAI gym CartPole-v1 environment. For the structure and training algorithm, assume it is the same as the one in this article. Typically, ...
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Training a model from self-play

Say I'd like to build a Go AI. The Go AI takes in the board state and then predicts who's more likely to win from that state. When I want to make a move, I just test every next board state I could ...
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1answer
31 views

How does the forget layer of an LSTM work?

Can someone explain the mathematical intuition behind the forget layer of an LSTM? So as far as I understand it, the cell state is essentially long term memory embedding (correct me if I'm wrong), ...

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