Questions tagged [topologies]

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In “A Topology Layer for Machine Learning,” are the topological priors learned by the network or imposed by humans?

In this paper by Gabrielsson, Nelson, et al. the authors "present a differentiable topology layer that can, among other things, construct a loss on the output of a deep generative network to ...
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0answers
26 views

Reference request: Network/graph topology inference

I am a mathematician looking for a survey/book on methods for inference of graph/network topology (structure). Specifically, the kind of problem I am looking to study is as follows: Given a graph $...
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0answers
20 views

How to identify manifolds for an optimisation problem

I don't have much experience in topology, but I am interested to know if: • Given a particular problem and associated cost function, how would one deduce what kind of manifold this problem lies on. ...
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1answer
317 views

2 hidden layers are more powerful than 1

When searching for information on choosing the number of hidden layers in a neural network, I have come across the following table mutiple times, including in this answer: | Number of Hidden Layers ...
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1answer
96 views

Optimising neural network to prevent overfitting

I'm looking for some advice on a general approach to optimise the training of a neural network. My primary concern is to avoid over-fitting to the training data and maintain as much generality as ...
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0answers
340 views

Neural networks, mapping features to polar coordinates to deal with uncertain inputs

Let's say you've got a neural network which takes in a vector of real numbers as input. Additionally, let's say you're uncertain about the values of some components of the vector, and your level of ...
2
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1answer
67 views

Visualizing model trajectories for Neural Networks using function approximator

Erhan et al. in their 2010 paper discusses how pre-training improves deep networks: http://www.jmlr.org/papers/volume11/erhan10a/erhan10a.pdf#page=15 In there, they compare different neural network ...
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1answer
272 views

Deep learning with global connections/correlations

Deep learning for 1D/2D inputs usually assume some sort of local connections, whether it is using local filters, or recurrent connections etc. What if for our problem we think a connection between ...
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1answer
240 views

Cases where TDA outperforms public benchmarks?

Precise Question What are some specific examples where topological data analysis (TDA) outperforms other models on publicly available data? Context When new ML algorithms are developed, it seems ...
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2answers
866 views

Graphical intuition of statistics on a manifold

On this post, you can read the statement: Models are usually represented by points $\theta$ on a finite dimensional manifold. On Differential Geometry and Statistics by Michael K Murray and ...
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0answers
56 views

Difference between FFNN and NAR

What is the difference between feed forward neural network and non-linear autoregressive neural network. Do they have same structure. What is the difference in their equation
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0answers
27 views

Analysis techiques for logical topologies

I'm working in the area of analysis of logical computer systems (e.g https://goo.gl/images/KyLCCo). Specifically in the field of anomaly detection of these systems. I was thinking about the field of ...
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0answers
80 views

Topology of Confidence Intervals

I hope this is the right site to post this. The example I have in my mind is a GLMM model, where we infer random effects, and a random effect caterpillar plot (with confidence intervals): Now, ...
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1answer
204 views

Topologies for which the ensemble of probability distributions is complete

I have been struggling quite a bit with reconciling my intuitive understanding of probability distributions with the weird properties that almost all topologies on probability distributions possess. ...
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0answers
40 views

ART neural network disambiguation

I have an assignment to implement the adaptive resonance theory (ART) type network (as part of a bigger project). I have red a lot of Internet resources on the topic and I think I've got the essence ...
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1answer
198 views

Laplacian-Beltrami approximation based on an empirical sample

Given a probability measure $\nu$ on a subset $M \subseteq \mathbb{R}^N$ we construct the corresponding operator $$L^tf(x)=f(x)\int_{M} e^{-\frac{||x-y||^2}{4t}}d\nu(y)-\int_{M}f(y)e^{-\frac{||x-y||^...
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2answers
2k views

Where can I get real data of big network topology? [closed]

I want to model how traffic will flow on real networks (not just the internet, also, say, Intel's internal LAN). Is there a place I can get real network topologies data I can use?
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3answers
49k views

What's the difference between feed-forward and recurrent neural networks?

What is the difference between a feed-forward and recurrent neural network? Why would you use one over the other? Do other network topologies exist?