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Google Keyword Rankings for : local minima problem neural network
1
Is the Local Minima a real issue in deep neural learning?
https://medium.com/@pranabbhadani/is-the-local-minima-a-real-issue-in-deep-neural-learning-6d812b28d684
Theoretically, local minima can create a significant issue, as it can lead to a suboptimal trained model. But in practice, how common are the ...
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https://medium.com/@pranabbhadani/is-the-local-minima-a-real-issue-in-deep-neural-learning-6d812b28d684
Theoretically, local minima can create a significant issue, as it can lead to a suboptimal trained model. But in practice, how common are the ...
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2
Is the Local Minima a real issue in Artificial Neural Network
https://www.fromthegenesis.com/is-the-local-minima-a-real-issue-in-artificial-neural-network/
The primary challenge in optimizing deep learning models is that we are forced to use local information to infer the global structure of the ...
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https://www.fromthegenesis.com/is-the-local-minima-a-real-issue-in-artificial-neural-network/
The primary challenge in optimizing deep learning models is that we are forced to use local information to infer the global structure of the ...
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3
How to overcome a local minimum problem in neural networks
https://www.quora.com/How-do-I-overcome-a-local-minimum-problem-in-neural-networks
First, deep neural networks have plenty of local minima owing to their extremely non-convex loss function. And virtually all neural nets are optimized by some ...
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https://www.quora.com/How-do-I-overcome-a-local-minimum-problem-in-neural-networks
First, deep neural networks have plenty of local minima owing to their extremely non-convex loss function. And virtually all neural nets are optimized by some ...
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4
Elimination of All Bad Local Minima in Deep Learning
http://proceedings.mlr.press/v108/kawaguchi20b/kawaguchi20b.pdf
Given their recent empirical success, a question remains whether practical deep neural net- works can be theoretically guaranteed to avoid poor local minima.
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http://proceedings.mlr.press/v108/kawaguchi20b/kawaguchi20b.pdf
Given their recent empirical success, a question remains whether practical deep neural net- works can be theoretically guaranteed to avoid poor local minima.
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5
Definition: Local Minimum (at Stand Out Publishing)
https://standoutpublishing.com/g/local%20minimum.html
Define Local Minimum - A state that a learning neural network sometimes gets into, where the weight adjustments for one or more training patterns simply ...
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https://standoutpublishing.com/g/local%20minimum.html
Define Local Minimum - A state that a learning neural network sometimes gets into, where the weight adjustments for one or more training patterns simply ...
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6
Neural networks - are local minima bad? - Cross Validated
https://stats.stackexchange.com/questions/108631/neural-networks-are-local-minima-bad
You can consider local minima L bad if a) your model does not overfit on L and b) there's some other minima L' which has significantly lower ...
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https://stats.stackexchange.com/questions/108631/neural-networks-are-local-minima-bad
You can consider local minima L bad if a) your model does not overfit on L and b) there's some other minima L' which has significantly lower ...
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7
Local & Global Minima Explained with Examples
https://vitalflux.com/local-global-maxima-minima-explained-examples/
The point where function takes the minimum value is called as global minima. Other points will be called as local minima. At all the minima ...
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https://vitalflux.com/local-global-maxima-minima-explained-examples/
The point where function takes the minimum value is called as global minima. Other points will be called as local minima. At all the minima ...
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8
Adaptively Solving the Local-Minimum Problem for Deep ...
https://arxiv.org/abs/2012.13632
It is widely believed that training of deep models using gradient methods works so well because the error surface either has no local minima, or ...
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https://arxiv.org/abs/2012.13632
It is widely believed that training of deep models using gradient methods works so well because the error surface either has no local minima, or ...
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9
LOCAL MINIMA IN TRAINING OF DEEP NETWORKS
https://openreview.net/pdf?id=Syoiqwcxx
MLP with a single linear intermediate layer has no local minima, ... neural network, providing theoretical arguments for the error surface becoming ...
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https://openreview.net/pdf?id=Syoiqwcxx
MLP with a single linear intermediate layer has no local minima, ... neural network, providing theoretical arguments for the error surface becoming ...
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10
On the problem of local minima in recurrent neural ... - PubMed
https://pubmed.ncbi.nlm.nih.gov/18267788/
As in the case of feedforward networks, however, these learning algorithms may get stuck in local minima during gradient descent, thus discovering sub-optimal ...
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https://pubmed.ncbi.nlm.nih.gov/18267788/
As in the case of feedforward networks, however, these learning algorithms may get stuck in local minima during gradient descent, thus discovering sub-optimal ...
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11
Non-attracting Regions of Local Minima in Deep and Wide ...
https://www.jmlr.org/papers/volume22/19-586/19-586.pdf
Keywords: Deep learning, neural network, local minima, global minima, path. 1. Introduction. At the heart of most optimization problems lies the search for ...
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https://www.jmlr.org/papers/volume22/19-586/19-586.pdf
Keywords: Deep learning, neural network, local minima, global minima, path. 1. Introduction. At the heart of most optimization problems lies the search for ...
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12
Solving local minima problem with large number of hidden ...
https://www.sciencedirect.com/science/article/pii/S0925231208002002
A local minimum is a suboptimal equilibrium point at which system error is non-zero and the hidden output matrix is singular [12]. The complex problem which has ...
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https://www.sciencedirect.com/science/article/pii/S0925231208002002
A local minimum is a suboptimal equilibrium point at which system error is non-zero and the hidden output matrix is singular [12]. The complex problem which has ...
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13
Deep Learning without Poor Local Minima - NIPS papers
http://papers.neurips.cc/paper/6112-deep-learning-without-poor-local-minima.pdf
squared loss function of deep linear neural networks with any depth and any ... global minimum of a general non-convex function is an NP-complete problem ...
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http://papers.neurips.cc/paper/6112-deep-learning-without-poor-local-minima.pdf
squared loss function of deep linear neural networks with any depth and any ... global minimum of a general non-convex function is an NP-complete problem ...
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14
Effect of Depth and Width on Local Minima in Deep Learning
https://lis.csail.mit.edu/pubs/kawaguchi-nc2019.pdf
minima of neural networks are theoretically proven to be no worse than ... problems, because global optimization methods can efficiently approximate.
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https://lis.csail.mit.edu/pubs/kawaguchi-nc2019.pdf
minima of neural networks are theoretically proven to be no worse than ... problems, because global optimization methods can efficiently approximate.
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15
Intro to optimization in deep learning: Gradient Descent
https://blog.paperspace.com/intro-to-optimization-in-deep-learning-gradient-descent/
Local minimum are called so since the value of the loss function is minimum at that point in a local region. Whereas, a global minima is called so since the ...
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https://blog.paperspace.com/intro-to-optimization-in-deep-learning-gradient-descent/
Local minimum are called so since the value of the loss function is minimum at that point in a local region. Whereas, a global minima is called so since the ...
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16
Avoiding Local Minima in Feedforward Neural Networks by ...
https://link.springer.com/content/pdf/10.1007/978-3-540-76928-6_12.pdf
However, weight adjusting with a gradient descent may result in the local minimum problem. Repeated training with random starting weights is among the popular ...
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https://link.springer.com/content/pdf/10.1007/978-3-540-76928-6_12.pdf
However, weight adjusting with a gradient descent may result in the local minimum problem. Repeated training with random starting weights is among the popular ...
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17
Deep Learning without Poor Local Minima - NIPS papers
https://papers.nips.cc/paper/6112-deep-learning-without-poor-local-minima
... address an open problem announced at the Conference on Learning Theory (COLT) 2015. For an expected loss function of a deep nonlinear neural network, ...
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https://papers.nips.cc/paper/6112-deep-learning-without-poor-local-minima
... address an open problem announced at the Conference on Learning Theory (COLT) 2015. For an expected loss function of a deep nonlinear neural network, ...
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18
On the problem of local minima in recurrent ... - IEEE Xplore
https://ieeexplore.ieee.org/iel4/72/6922/00279182.pdf
schemes, for learning the weights of recurrent neural networks. ... ing conditions that guarantee local minima free error surfaces.
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https://ieeexplore.ieee.org/iel4/72/6922/00279182.pdf
schemes, for learning the weights of recurrent neural networks. ... ing conditions that guarantee local minima free error surfaces.
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19
Local minima in training of neural networks - arXiv Vanity
https://www.arxiv-vanity.com/papers/1611.06310/
One hypothesis for why learning is well behaved in neural networks is put forward in Dauphin et al. (2013) . We will refer to it as the “no bad local minima” ...
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https://www.arxiv-vanity.com/papers/1611.06310/
One hypothesis for why learning is well behaved in neural networks is put forward in Dauphin et al. (2013) . We will refer to it as the “no bad local minima” ...
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20
Why Does Deep Learning Not Have a Local Minimum?
https://www.kdnuggets.com/2017/06/deep-learning-local-minimum.html
On the stationary points of the TAP free energy · Critical Points in High Dimensional Landscapes · Deep Learning without Poor Local Minima.
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https://www.kdnuggets.com/2017/06/deep-learning-local-minimum.html
On the stationary points of the TAP free energy · Critical Points in High Dimensional Landscapes · Deep Learning without Poor Local Minima.
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21
Distribution of Local Minima in Deep Neural Networks
https://www.adventuresinwhy.com/post/local-minima-distribution/
The “unreasonable effectiveness of deep learning” has been much discussed. Namely, as the cost function is non-convex, any optimization ...
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https://www.adventuresinwhy.com/post/local-minima-distribution/
The “unreasonable effectiveness of deep learning” has been much discussed. Namely, as the cost function is non-convex, any optimization ...
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22
How to overcome a local minima problem in neural networks?
https://www.kaggle.com/questions-and-answers/228156
There is no single strategy against combating local minima, except for optimizers. Utham BathojuTopic Author • 2 years ago. keyboard_arrow_up.
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https://www.kaggle.com/questions-and-answers/228156
There is no single strategy against combating local minima, except for optimizers. Utham BathojuTopic Author • 2 years ago. keyboard_arrow_up.
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23
Adding One Neuron Can Eliminate All Bad Local ... - NSF PAR
https://par.nsf.gov/servlets/purl/10201450
whether the non-convexity of the neural network is really an issue. It has been widely conjectured that all local minima of the empirical loss lead to ...
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https://par.nsf.gov/servlets/purl/10201450
whether the non-convexity of the neural network is really an issue. It has been widely conjectured that all local minima of the empirical loss lead to ...
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24
[PDF] Avoiding the Local Minima Problem in Backpropagation ...
https://www.researchgate.net/publication/220237893_Avoiding_the_Local_Minima_Problem_in_Backpropagation_Algorithm_with_Modified_Error_Function
One critical "drawback" of the backpropagation algorithm is the local minima problem. We have noted that the local minima problem in the ...
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https://www.researchgate.net/publication/220237893_Avoiding_the_Local_Minima_Problem_in_Backpropagation_Algorithm_with_Modified_Error_Function
One critical "drawback" of the backpropagation algorithm is the local minima problem. We have noted that the local minima problem in the ...
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25
Suboptimal Local Minima Exist for Wide Neural Networks with ...
https://pubsonline.informs.org/doi/abs/10.1287/moor.2021.1228
In this paper, we give a largely negative answer to this question. Specifically, we prove that, for neural networks with generic input data and ...
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https://pubsonline.informs.org/doi/abs/10.1287/moor.2021.1228
In this paper, we give a largely negative answer to this question. Specifically, we prove that, for neural networks with generic input data and ...
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26
Optimizing a Neural Network Part 2 - DPhi
https://dphi.tech/learn/getting-started-with-deep-learning/optimising-a-neural-network/263/optimizing-a-neural-network-part-2
At the minima point, the model has optimized the weights such that they minimize the cost function. image.png. The 'local minima' problem. We try to reduce to ...
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https://dphi.tech/learn/getting-started-with-deep-learning/optimising-a-neural-network/263/optimizing-a-neural-network-part-2
At the minima point, the model has optimized the weights such that they minimize the cost function. image.png. The 'local minima' problem. We try to reduce to ...
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27
How can you avoid local minima to achieve the minimized ...
https://www.i2tutorials.com/how-can-you-avoid-local-minima-to-achieve-the-minimized-loss-function/
This sometimes takes us away from a nearby local minimum, and can have the effect of preventing us from getting trapped in small local minimum. Batch size in ...
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https://www.i2tutorials.com/how-can-you-avoid-local-minima-to-achieve-the-minimized-loss-function/
This sometimes takes us away from a nearby local minimum, and can have the effect of preventing us from getting trapped in small local minimum. Batch size in ...
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28
Global Optimality in Neural Network Training - JHU Vision Lab
http://www.vision.jhu.edu/assets/HaeffeleCVPR17.pdf
(f,h) Local minima. To address the issue of non-convexity, a common strat- egy used in deep learning is to initialize the network weights, {Wk}, at random, ...
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http://www.vision.jhu.edu/assets/HaeffeleCVPR17.pdf
(f,h) Local minima. To address the issue of non-convexity, a common strat- egy used in deep learning is to initialize the network weights, {Wk}, at random, ...
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29
Convergence and Local Minima
https://www.cs.auckland.ac.nz/~pat/706_98/ln/node158.html
the error surface in multilayer neural networks may contain may different local minima where gradient descent can become trapped · but Backpropagation is a ...
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https://www.cs.auckland.ac.nz/~pat/706_98/ln/node158.html
the error surface in multilayer neural networks may contain may different local minima where gradient descent can become trapped · but Backpropagation is a ...
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30
Why Training a Neural Network Is Hard
https://machinelearningmastery.com/why-training-a-neural-network-is-hard/
1. Local Minima ... Local minimal or local optima refer to the fact that the error landscape contains multiple regions where the loss is ...
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https://machinelearningmastery.com/why-training-a-neural-network-is-hard/
1. Local Minima ... Local minimal or local optima refer to the fact that the error landscape contains multiple regions where the loss is ...
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31
Gradient Descent, Global Local Minima | Explained with 3-D ...
https://www.youtube.com/watch?v=NA8jPdQ_vBM
When Maths Meet Coding
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https://www.youtube.com/watch?v=NA8jPdQ_vBM
When Maths Meet Coding
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32
Global Search Methods for Neural Networks
https://neuron.eng.wayne.edu/tarek/MITbook/chap8/ch8-5.html
In Chapter 4, learning in neural networks was viewed as a search mechanism for a minimum of a multi-dimensional criterion function or error function.
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https://neuron.eng.wayne.edu/tarek/MITbook/chap8/ch8-5.html
In Chapter 4, learning in neural networks was viewed as a search mechanism for a minimum of a multi-dimensional criterion function or error function.
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33
SOLVING LOCAL MINIMA PROBLEM IN BACK ...
https://www.worldscientific.com/doi/pdf/10.1142/S2010194512005533
Keywords: back propagation; gain; momentum; learning rate. 1. Introduction. Artificial Neural Network (ANN) is computational models whose architecture and.
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https://www.worldscientific.com/doi/pdf/10.1142/S2010194512005533
Keywords: back propagation; gain; momentum; learning rate. 1. Introduction. Artificial Neural Network (ANN) is computational models whose architecture and.
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34
[Solved] Discuss the problem of local minima in neural ...
https://www.studocu.com/en-us/messages/question/2715171/discuss-the-problem-of-local-minima-in-neural-network-training-and-techniques-to-ensure
Machine Learning · Local minima are a real obstacle in neural networks when one deals with complex input-output relationships. · The value of the loss function is ...
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https://www.studocu.com/en-us/messages/question/2715171/discuss-the-problem-of-local-minima-in-neural-network-training-and-techniques-to-ensure
Machine Learning · Local minima are a real obstacle in neural networks when one deals with complex input-output relationships. · The value of the loss function is ...
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35
Challenges in neural network optimization - CEDAR
https://cedar.buffalo.edu/~srihari/CSE676/8.2%20NNOptimization.pdf
Deep Learning. Srihari. 2. Local Minima. • In convex optimization, problem is one of finding a local minimum. • Some convex functions have a flat region ...
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https://cedar.buffalo.edu/~srihari/CSE676/8.2%20NNOptimization.pdf
Deep Learning. Srihari. 2. Local Minima. • In convex optimization, problem is one of finding a local minimum. • Some convex functions have a flat region ...
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36
On the problem of local minima in recurrent neural networks
https://www.semanticscholar.org/paper/On-the-problem-of-local-minima-in-recurrent-neural-Bianchini-Gori/71e2ad0ffd39f7d5e117b49272a8f598130b07d7
This paper analyses the problem of optimal learning in recurrent networks by proposing conditions that guarantee local minima free error ...
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https://www.semanticscholar.org/paper/On-the-problem-of-local-minima-in-recurrent-neural-Bianchini-Gori/71e2ad0ffd39f7d5e117b49272a8f598130b07d7
This paper analyses the problem of optimal learning in recurrent networks by proposing conditions that guarantee local minima free error ...
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37
How neural network's avoid local minima problem - Reddit
https://www.reddit.com/r/learnmachinelearning/comments/6wqfu7/how_neural_networks_avoid_local_minima_problem/
Neural networks uses backpropagation to learn and for this purpose employs gradient descent. While using loss function algorithm how latest ...
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https://www.reddit.com/r/learnmachinelearning/comments/6wqfu7/how_neural_networks_avoid_local_minima_problem/
Neural networks uses backpropagation to learn and for this purpose employs gradient descent. While using loss function algorithm how latest ...
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38
How to train Neural Networks like a pro! - Towards Data Science
https://towardsdatascience.com/how-to-train-neural-networks-like-a-pro-1d2362768c1
The complete package for dealing with neural network training issues like Overfitting/Underfitting, Vanishing Gradient, Local Minima, Learning ...
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https://towardsdatascience.com/how-to-train-neural-networks-like-a-pro-1d2362768c1
The complete package for dealing with neural network training issues like Overfitting/Underfitting, Vanishing Gradient, Local Minima, Learning ...
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39
Exponentially many local minima for single neurons
http://www0.cs.ucl.ac.uk/staff/M.Herbster/pubs/exp-min.pdf
for neural networks consists of minimizing the error function with respect to the weight vector w E Rd. This function is the sum of the losses between ...
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http://www0.cs.ucl.ac.uk/staff/M.Herbster/pubs/exp-min.pdf
for neural networks consists of minimizing the error function with respect to the weight vector w E Rd. This function is the sum of the losses between ...
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40
Momentum and Learning Rate Adaptation
https://cnl.salk.edu/~schraudo/teach/NNcourse/momrate.html
We have already mentioned one way to escape a local minimum: use online learning. The noise in the stochastic error surface is likely to bounce the network out ...
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https://cnl.salk.edu/~schraudo/teach/NNcourse/momrate.html
We have already mentioned one way to escape a local minimum: use online learning. The noise in the stochastic error surface is likely to bounce the network out ...
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41
Local Minima and Plateaus in Multilayer Neural Networks
https://www.ism.ac.jp/~fukumizu/papers/icann99.pdf
Local minima and plateaus pose a serious problem in learning of neural networks. We investigate the geometric structure of the.
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https://www.ism.ac.jp/~fukumizu/papers/icann99.pdf
Local minima and plateaus pose a serious problem in learning of neural networks. We investigate the geometric structure of the.
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42
Global Minima of Overparameterized Neural Networks
https://epubs.siam.org/doi/pdf/10.1137/19M1308943
as neural networks for MNIST and CIFAR, and conclude that ``many problems have ... good solutions in deep learning because many local minima are close to ...
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https://epubs.siam.org/doi/pdf/10.1137/19M1308943
as neural networks for MNIST and CIFAR, and conclude that ``many problems have ... good solutions in deep learning because many local minima are close to ...
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43
On the capacity and superposition of minima in neural network ...
https://iopscience.iop.org/article/10.1088/2632-2153/ac64e6/pdf
Minima of the loss function landscape (LFL) of a neural network are locally ... in question correspond to local minima of a reference neural.
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https://iopscience.iop.org/article/10.1088/2632-2153/ac64e6/pdf
Minima of the loss function landscape (LFL) of a neural network are locally ... in question correspond to local minima of a reference neural.
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44
Neural network, local minimum evasion techniques
https://stackoverflow.com/questions/18533643/neural-network-local-minimum-evasion-techniques
There are many possible methods of escaping local minima. Parallel learning has been investigated in the past, with different results, ...
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https://stackoverflow.com/questions/18533643/neural-network-local-minimum-evasion-techniques
There are many possible methods of escaping local minima. Parallel learning has been investigated in the past, with different results, ...
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45
203.5.10 Local vs. Global Minimum - Statinfer
https://statinfer.com/203-5-10-local-vs-global-minimum/
The neural network might give different results with different start weights. · The algorithm tries to find the local minima rather than global minima. · There ...
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https://statinfer.com/203-5-10-local-vs-global-minimum/
The neural network might give different results with different start weights. · The algorithm tries to find the local minima rather than global minima. · There ...
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46
On the Omnipresence of Spurious Local Minima in Certain ...
https://paperswithcode.com/paper/on-the-omnipresence-of-spurious-local-minima
We study the loss landscape of training problems for deep artificial neural networks with a one-dimensional real output whose activation ...
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https://paperswithcode.com/paper/on-the-omnipresence-of-spurious-local-minima
We study the loss landscape of training problems for deep artificial neural networks with a one-dimensional real output whose activation ...
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47
Identification of the Challenges of Local Minima in Recurrent ...
https://www.rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.5&Issue1/210-217.pdf
recurrent neural networks, local minima in neural networks, optimal learning in the case of feedforward networks, the local minimum is a real question in ...
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https://www.rsisinternational.org/journals/ijrias/DigitalLibrary/Vol.5&Issue1/210-217.pdf
recurrent neural networks, local minima in neural networks, optimal learning in the case of feedforward networks, the local minimum is a real question in ...
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48
Neural Network - d4datascience.com
https://d4datascience.com/category/neural-network/
Adagrad is a gradient based algorithm that adapts the learning rate to the parameters. In momentum based optimizers we adapted our updates to the slope of error ...
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https://d4datascience.com/category/neural-network/
Adagrad is a gradient based algorithm that adapts the learning rate to the parameters. In momentum based optimizers we adapted our updates to the slope of error ...
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49
Identifying and attacking the saddle point problem in high ...
https://ganguli-gang.stanford.edu/pdf/14.SaddlePoint.NIPS.pdf
matrix theory, neural network theory, and empirical evidence, ... A typical problem for both local minima and saddle-points is that they are often ...
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https://ganguli-gang.stanford.edu/pdf/14.SaddlePoint.NIPS.pdf
matrix theory, neural network theory, and empirical evidence, ... A typical problem for both local minima and saddle-points is that they are often ...
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50
Artificial Neural Networks
https://www.cs.montana.edu/courses/spring2009/536/lectures/tom-4b.ppt
Artificial Neural Networks. ML 4.6-4.9. Paul Scheible. BackPropagation Algorithm. Convergence to Local Minima. Performs well in many practical problems ...
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https://www.cs.montana.edu/courses/spring2009/536/lectures/tom-4b.ppt
Artificial Neural Networks. ML 4.6-4.9. Paul Scheible. BackPropagation Algorithm. Convergence to Local Minima. Performs well in many practical problems ...
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51
Is Gradient Descent Sufficient for Neural Network Models
https://www.analyticsvidhya.com/blog/2021/04/is-gradient-descent-sufficient-for-neural-network/
The point where the red ball is placed has minimum cost function value amongst its neighbors and therefore is the Local Minima or the local ...
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https://www.analyticsvidhya.com/blog/2021/04/is-gradient-descent-sufficient-for-neural-network/
The point where the red ball is placed has minimum cost function value amongst its neighbors and therefore is the Local Minima or the local ...
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52
Global Search Methods for Neural Network Training
https://www.dcs.bbk.ac.uk/~gmagoulas/365.pdf
leviate the problem of occasional convergence to local minima in BP training. Global search meth- ods for feedforward neural network batch training.
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https://www.dcs.bbk.ac.uk/~gmagoulas/365.pdf
leviate the problem of occasional convergence to local minima in BP training. Global search meth- ods for feedforward neural network batch training.
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53
Non-attracting regions of local minima in deep and wide ...
https://dl.acm.org/doi/pdf/10.5555/3546258.3546401
The common approach of using gradient descent variants on non-convex loss curves of deep neural networks is vulnerable precisely to that problem. Authors ...
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https://dl.acm.org/doi/pdf/10.5555/3546258.3546401
The common approach of using gradient descent variants on non-convex loss curves of deep neural networks is vulnerable precisely to that problem. Authors ...
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54
Setting the learning rate of your neural network. - Jeremy Jordan
https://www.jeremyjordan.me/nn-learning-rate/
Because neural networks can have thousands or even millions of parameters, it's unlikely that we'll observe a true local minimum across all ...
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https://www.jeremyjordan.me/nn-learning-rate/
Because neural networks can have thousands or even millions of parameters, it's unlikely that we'll observe a true local minimum across all ...
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55
Spurious Local Minima Exist for Almost All Over ...
https://optimization-online.org/2019/10/7409/
A popular belief for explaining the efficiency in training deep neural networks is that over-paramenterized neural networks have nice landscape.
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https://optimization-online.org/2019/10/7409/
A popular belief for explaining the efficiency in training deep neural networks is that over-paramenterized neural networks have nice landscape.
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56
Local Minima in Quantum Neural Networks - Qu&Co
https://quandco.com/blog/arxiv-2110-02479
QNN training is often a non-deterministic polynomial time problem. So far, the investigation of training QNNs has been a trial-and-error ...
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https://quandco.com/blog/arxiv-2110-02479
QNN training is often a non-deterministic polynomial time problem. So far, the investigation of training QNNs has been a trial-and-error ...
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57
6.4 Error Surfaces - RIT
https://www.cs.rit.edu/~rlaz/PatternRecognition/slides/6-4_5_6.pdf
if there are many local minima plague the error ... Training neural network (without backpropagation) for X-OR problem…
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https://www.cs.rit.edu/~rlaz/PatternRecognition/slides/6-4_5_6.pdf
if there are many local minima plague the error ... Training neural network (without backpropagation) for X-OR problem…
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58
The Shape of the Error Surfaces of some simple Neural ...
https://liacs.leidenuniv.nl/assets/PDF/TechRep/tr95-19.pdf
The error surface of the network with two hidden units without connections from the inputs to the output unit has no local minima for finite weights.
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https://liacs.leidenuniv.nl/assets/PDF/TechRep/tr95-19.pdf
The error surface of the network with two hidden units without connections from the inputs to the output unit has no local minima for finite weights.
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59
A Biology Inspired Algorithm to Mitigate the ... - UOB Journals
https://journal.uob.edu.bh/handle/123456789/3486
ANN is a very well-known approach used for classification based on supervised machine learning. This approach faces some issues, notably the ...
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https://journal.uob.edu.bh/handle/123456789/3486
ANN is a very well-known approach used for classification based on supervised machine learning. This approach faces some issues, notably the ...
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60
How neural networks are trained - Machine Learning for Artists
https://ml4a.github.io/ml4a/how_neural_networks_are_trained/
Besides for local minima, “vanilla” gradient descent has another major problem: it's too slow. A neural net may have hundreds of millions of parameters; ...
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https://ml4a.github.io/ml4a/how_neural_networks_are_trained/
Besides for local minima, “vanilla” gradient descent has another major problem: it's too slow. A neural net may have hundreds of millions of parameters; ...
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61
Shaping the learning landscape in neural networks around ...
https://www.pnas.org/doi/10.1073/pnas.1908636117
Here we show that such landscapes possess very peculiar wide flat minima and that the current models have been shaped to make the loss functions and the ...
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https://www.pnas.org/doi/10.1073/pnas.1908636117
Here we show that such landscapes possess very peculiar wide flat minima and that the current models have been shaped to make the loss functions and the ...
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62
Adding One Neuron Can Eliminate All Bad Local ... - TechLeer
https://www.techleer.com/articles/526-adding-one-neuron-can-eliminate-all-bad-local-minima/
One of the main difficulties in analyzing neural networks is the non-convexity of the loss function which may have many bad local minima.
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https://www.techleer.com/articles/526-adding-one-neuron-can-eliminate-all-bad-local-minima/
One of the main difficulties in analyzing neural networks is the non-convexity of the loss function which may have many bad local minima.
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63
Synthetic Generation of Local Minima and Saddle Points for ...
https://www.padl.ws/papers/Paper%2040.pdf
Qualitatively characterizing neural network optimization problems. arXiv:1412.6544, December 2014b. Hochreiter, Sepp and Schmidhuber, Jürgen. Flat minima.
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https://www.padl.ws/papers/Paper%2040.pdf
Qualitatively characterizing neural network optimization problems. arXiv:1412.6544, December 2014b. Hochreiter, Sepp and Schmidhuber, Jürgen. Flat minima.
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64
Indeed, the old local maxima problem. Why doesn't evolution ...
https://news.ycombinator.com/item?id=10884233
Luckily machine learning came along and has shown empirically that the solution is to increase the number of dimensions. Our tiny neural nets of the 90s also ...
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https://news.ycombinator.com/item?id=10884233
Luckily machine learning came along and has shown empirically that the solution is to increase the number of dimensions. Our tiny neural nets of the 90s also ...
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65
local-minima · GitHub Topics
https://github.com/topics/local-minima
Awesome list for Neural Network Optimization methods. ... Visualizing the structure of planning problems using local-minima trees.
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https://github.com/topics/local-minima
Awesome list for Neural Network Optimization methods. ... Visualizing the structure of planning problems using local-minima trees.
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66
What is the Plateau Problem in Neural Networks and How to ...
https://analyticsindiamag.com/what-is-the-plateau-problem-in-neural-networks-and-how-to-fix-it/
If our learning rate is too low, you may not be able to escape the local minimum. Remember that while explaining the plateau in figure 1 (a), we ...
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https://analyticsindiamag.com/what-is-the-plateau-problem-in-neural-networks-and-how-to-fix-it/
If our learning rate is too low, you may not be able to escape the local minimum. Remember that while explaining the plateau in figure 1 (a), we ...
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67
The Global Optimization Geometry of Shallow Linear Neural ...
https://www.weizmann.ac.il/math/yonina/sites/math.yonina/files/The%20Global%20Optimization%20Geometry%20of%20Shallow%20Linear%20Neural%20Networks.pdf
tions than previous works—that the corresponding optimization problems have benign geometric ... neural networks at differentiable local minima was examined.
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https://www.weizmann.ac.il/math/yonina/sites/math.yonina/files/The%20Global%20Optimization%20Geometry%20of%20Shallow%20Linear%20Neural%20Networks.pdf
tions than previous works—that the corresponding optimization problems have benign geometric ... neural networks at differentiable local minima was examined.
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68
Deep Learning Local Optima
https://www.cs.ubc.ca/labs/lci/mlrg/slides/deep_learning.pdf
With Hessian. Local minimum: ... Previous theoretical work on neural networks. ... problem in high-dimensional non-convex optimization” NIPS, 2014.
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https://www.cs.ubc.ca/labs/lci/mlrg/slides/deep_learning.pdf
With Hessian. Local minimum: ... Previous theoretical work on neural networks. ... problem in high-dimensional non-convex optimization” NIPS, 2014.
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69
Control on Landscapes with Local Minima and Flat Regions
https://folk.ntnu.no/skoge/prost/proceedings/cdc-2008/data/papers/1828.pdf
and a 'practical' local minimum using standard backpropagation problems that can arise in high dimensional multilayer neural networks.
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https://folk.ntnu.no/skoge/prost/proceedings/cdc-2008/data/papers/1828.pdf
and a 'practical' local minimum using standard backpropagation problems that can arise in high dimensional multilayer neural networks.
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70
A Biology Inspired Algorithm to Mitigate the Local ... - Gale
https://link.gale.com/apps/doc/A607662144/AONE?u=googlescholar&sid=AONE&xid=f140d58d
A Biology Inspired Algorithm to Mitigate the Local Minima Problem and Improve the Classification in Neural Networks. Citation metadata. Authors: Nabil M. Hewahi ...
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https://link.gale.com/apps/doc/A607662144/AONE?u=googlescholar&sid=AONE&xid=f140d58d
A Biology Inspired Algorithm to Mitigate the Local Minima Problem and Improve the Classification in Neural Networks. Citation metadata. Authors: Nabil M. Hewahi ...
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71
Artificial neural network - Wikipedia
https://en.wikipedia.org/wiki/Artificial_neural_network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural ...
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https://en.wikipedia.org/wiki/Artificial_neural_network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural ...
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72
Artificial Neural Networks
https://web.cs.hacettepe.edu.tr/~ilyas/Courses/BIL712/lec03-NeuralNetwork.pdf
Artificial neural networks (ANNs) provide a general, practical method ... if there are multiple local minima in the error surface, then there is.
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https://web.cs.hacettepe.edu.tr/~ilyas/Courses/BIL712/lec03-NeuralNetwork.pdf
Artificial neural networks (ANNs) provide a general, practical method ... if there are multiple local minima in the error surface, then there is.
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73
What is Gradient Descent? - IBM
https://www.ibm.com/cloud/learn/gradient-descent
Local minima and saddle points ... For convex problems, gradient descent can find the global minimum with ease, but as nonconvex problems emerge, ...
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https://www.ibm.com/cloud/learn/gradient-descent
Local minima and saddle points ... For convex problems, gradient descent can find the global minimum with ease, but as nonconvex problems emerge, ...
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74
The Generalization Mystery: Sharp vs Flat Minima - inFERENCe
https://www.inference.vc/sharp-vs-flat-minima-are-still-a-mystery-to-me/
The loss surface of deep nets tends to have many local minima. Many of these might be equally good in terms of training error, ...
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https://www.inference.vc/sharp-vs-flat-minima-are-still-a-mystery-to-me/
The loss surface of deep nets tends to have many local minima. Many of these might be equally good in terms of training error, ...
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75
LNCS 3173 - Modified Error Function with Added Terms for ...
https://userweb.cs.txstate.edu/~zz11/publications/ISNN.pdf
We have noted that many local minima difficulties in the backpropagation learn- ing for feedforward neural network are closely related to the neuron ...
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https://userweb.cs.txstate.edu/~zz11/publications/ISNN.pdf
We have noted that many local minima difficulties in the backpropagation learn- ing for feedforward neural network are closely related to the neuron ...
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76
What is wrong when my neural network's error increases?
https://sebastianraschka.com/faq/docs/neuralnet-error.html
... we chose a learning rate that was too high, which in turn let to the problem that we were overshooting the local minima of the cost function.
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https://sebastianraschka.com/faq/docs/neuralnet-error.html
... we chose a learning rate that was too high, which in turn let to the problem that we were overshooting the local minima of the cost function.
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77
Theory of Deep Learning
https://www.cs.princeton.edu/courses/archive/fall19/cos597B/lecnotes/bookdraft.pdf
9.2 Deep neural networks. 87. 9.3 Landscape of the Optimization Problem. 90. 9.3.1. Implicit bias in local optima. 92. 9.3.2. Landscape properties.
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https://www.cs.princeton.edu/courses/archive/fall19/cos597B/lecnotes/bookdraft.pdf
9.2 Deep neural networks. 87. 9.3 Landscape of the Optimization Problem. 90. 9.3.1. Implicit bias in local optima. 92. 9.3.2. Landscape properties.
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78
Neural Networks: Optimization Part 1 - Deep Learning, CMU
https://deeplearning.cs.cmu.edu/F22/document/slides/lec6.optimization.pdf
In classification problems, the classification error is a ... large networks, most local minima lie in a band and are equivalent.
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https://deeplearning.cs.cmu.edu/F22/document/slides/lec6.optimization.pdf
In classification problems, the classification error is a ... large networks, most local minima lie in a band and are equivalent.
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79
Do Deep Networks have Bad Local Minima? - Duke University
https://users.cs.duke.edu/~rongge/stoc2018ml/Ge_STOC_2018.pptx
Bad Local Minima? Brief survey on optimization landscape for neural networks. Rong Ge. Duke University. Non-convex optimization. Theory: NP-hard ...
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https://users.cs.duke.edu/~rongge/stoc2018ml/Ge_STOC_2018.pptx
Bad Local Minima? Brief survey on optimization landscape for neural networks. Rong Ge. Duke University. Non-convex optimization. Theory: NP-hard ...
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80
The Normalized Risk-Averting Error Criterion for Avoiding ...
https://www.csee.umbc.edu/~ypeng/Publications/2015/Nc2013.pdf
The Normalized Risk-Averting Error Criterion for Avoiding Nonglobal Local Minima in. Training Neural Networks$. James Ting-Ho Loa, ...
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https://www.csee.umbc.edu/~ypeng/Publications/2015/Nc2013.pdf
The Normalized Risk-Averting Error Criterion for Avoiding Nonglobal Local Minima in. Training Neural Networks$. James Ting-Ho Loa, ...
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81
My Neural Network isn't working! What should I do?
https://theorangeduck.com/page/neural-network-not-working
An untrained neural network will typically output values roughly in ... Firstly, it can help the training to "jump" out of local minima in ...
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https://theorangeduck.com/page/neural-network-not-working
An untrained neural network will typically output values roughly in ... Firstly, it can help the training to "jump" out of local minima in ...
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82
Does Adding One Neuron Help Real World Networks? | Rossum
https://rossum.ai/blog/does-adding-one-neuron-help-real-world-networks/
Experimental addition by AI Researcher Antonín Hoskovec to the paper Adding One Neuron Can Eliminate All Bad Local Minima by Shiyu Liang et ...
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https://rossum.ai/blog/does-adding-one-neuron-help-real-world-networks/
Experimental addition by AI Researcher Antonín Hoskovec to the paper Adding One Neuron Can Eliminate All Bad Local Minima by Shiyu Liang et ...
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83
Avoiding local minima in Variational Quantum Algorithms with ...
http://ui.adsabs.harvard.edu/abs/2021arXiv210402955R/abstract
The effect of this neural network is to peturb the cost landscape as a function of its parameters, so that local minima can be escaped or avoided via a ...
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http://ui.adsabs.harvard.edu/abs/2021arXiv210402955R/abstract
The effect of this neural network is to peturb the cost landscape as a function of its parameters, so that local minima can be escaped or avoided via a ...
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84
Improved Back Propagation Algorithm to Avoid Local Minima ...
https://zenodo.org/record/1328734/files/606.pdf
We have applied the three term back propagation to multiplicative neural network learning. The algorithm is tested on XOR and parity problem and.
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https://zenodo.org/record/1328734/files/606.pdf
We have applied the three term back propagation to multiplicative neural network learning. The algorithm is tested on XOR and parity problem and.
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85
Training Considerations - Ryan Wingate
https://ryanwingate.com/intro-to-machine-learning/deep/training-considerations/
Random Restart is a means of solving the local minima problem. Local minima is a phenomenon where the network weights settle into a ...
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https://ryanwingate.com/intro-to-machine-learning/deep/training-considerations/
Random Restart is a means of solving the local minima problem. Local minima is a phenomenon where the network weights settle into a ...
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86
Spurious Local Minima are Common in Two-Layer ... - Vimeo
https://vimeo.com/287767803
› TechTalksTV › Videos
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https://vimeo.com/287767803
› TechTalksTV › Videos
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87
The Problem of Local Optima - Optimization Algorithms
https://www.coursera.org/lecture/deep-neural-network/the-problem-of-local-optima-RFANA
Video created by DeepLearning.AI for the course "Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization".
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https://www.coursera.org/lecture/deep-neural-network/the-problem-of-local-optima-RFANA
Video created by DeepLearning.AI for the course "Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization".
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88
KF: Escaping the Local Minimum - Kenneth Friedman
https://kennethfriedman.org/projects/escaping-local-min/
These steps repeat along that path until the direction of the slope changes (which proves a local minimum). The problem with gradient descent is ...
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https://kennethfriedman.org/projects/escaping-local-min/
These steps repeat along that path until the direction of the slope changes (which proves a local minimum). The problem with gradient descent is ...
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89
a problem with neural network training - MATLAB Answers
https://www.mathworks.com/matlabcentral/answers/108658-a-problem-with-neural-network-training
Hi everybody I have read in some papers that in order to avoid your neural network getting stuck in local minima during resampling methods, a network is ...
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https://www.mathworks.com/matlabcentral/answers/108658-a-problem-with-neural-network-training
Hi everybody I have read in some papers that in order to avoid your neural network getting stuck in local minima during resampling methods, a network is ...
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90
Neural Networks: Computational Models and Applications
https://books.google.com/books?id=apc7oPqcOkcC&pg=PA161&lpg=PA161&dq=local+minima+problem+neural+network&source=bl&ots=0BDYdreVzV&sig=ACfU3U3_3ReLLj1dBqd64iTI7QIAq8Hflg&hl=en&sa=X&ved=2ahUKEwjZ7Lzby8D7AhX5aqQEHflfCysQ6AF6BQjXAhAD
However, the local minima problem is still an open issue. This chapter studies the performance of the CCM and aims to improve its local minima problem.
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https://books.google.com/books?id=apc7oPqcOkcC&pg=PA161&lpg=PA161&dq=local+minima+problem+neural+network&source=bl&ots=0BDYdreVzV&sig=ACfU3U3_3ReLLj1dBqd64iTI7QIAq8Hflg&hl=en&sa=X&ved=2ahUKEwjZ7Lzby8D7AhX5aqQEHflfCysQ6AF6BQjXAhAD
However, the local minima problem is still an open issue. This chapter studies the performance of the CCM and aims to improve its local minima problem.
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91
Activity-difference training of deep neural networks using ...
https://www.nature.com/articles/s41928-022-00869-w
Most deep neural networks are trained using stochastic gradient descent (SGD) via the backpropagation algorithm, which performs gradient-based ...
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https://www.nature.com/articles/s41928-022-00869-w
Most deep neural networks are trained using stochastic gradient descent (SGD) via the backpropagation algorithm, which performs gradient-based ...
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92
Neural Networks: Playground Exercises | Machine Learning
https://developers.google.com/machine-learning/crash-course/introduction-to-neural-networks/playground-exercises
› crash-course › playgro...
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https://developers.google.com/machine-learning/crash-course/introduction-to-neural-networks/playground-exercises
› crash-course › playgro...
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93
Data-Driven Based Prediction of the Energy Consumption of ...
https://www.mdpi.com/2075-5309/12/11/2039
The SVM model outperforms the Backpropagation Neural Network (BPNN) model in terms of ... solves the high-dimensional difficulty and local minima problem.
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https://www.mdpi.com/2075-5309/12/11/2039
The SVM model outperforms the Backpropagation Neural Network (BPNN) model in terms of ... solves the high-dimensional difficulty and local minima problem.
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