8
votes
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Can someone explain R1 regularization function in simple terms?
Here is how I understand this regularization.
$R_1$ is simply the norm of the gradients, which indicates how fast the weights will be updated. Gradient regularization penalizes large changes in the ...
- 927
8
votes
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What is "early stopping" in machine learning?
In some iterative learning methods the more iterations you apply the more specific your model becomes about the training set. If there are too many iterations, your model will become too specifically ...
- 381
6
votes
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Why is dropout favoured compared to reducing the number of units in hidden layers?
Dropout only ignores a portion of units during a single training batch update. Each training batch will use a different combination of units which gives them the best chance of that portion of them ...
- 211
5
votes
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What is the $\ell_{2, 1}$ norm?
$\ell_{2,1}$ is a matrix norm, as stated in this paper.
For a certain matrix $A \in \mathbb{R}^{r\times c}$,
we have
$$\|A\|_{2,1} = \sum_{i=1}^r \sqrt{\sum_{j=1}^c A_{ij}^2}$$
You first apply $\ell_2$...
5
votes
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Why L2 loss is more commonly used in Neural Networks than other loss functions?
I'll cover both L2 regularized loss, as well as Mean-Squared Error (MSE):
MSE:
L2 loss is continuously-differentiable across any domain, unlike L1 loss. This makes training more stable and allows ...
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5
votes
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Combine multiple losses with gradient descent
This is an important subfield within multi-task learning, called gradient combination. Here is a list of about a dozen recent approaches: https://github.com/Manchery/awesome-multi-task-learning#loss--...
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4
votes
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What is relation between gradient descent and regularization in deep learning?
Usually, when talking about regularization for neural networks there are 3 main types:
L1, L2 and dropout. All affect the gradient descent procedure.
L1 and L2 regularization is implemented in the ...
- 68
4
votes
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Is the dropout technique specific only to neural networks?
I'm sure you can use dropout in any parameterized model, but I suspect it'll only really be helpful if you have enough parameters/nodes. Also dropout in neural nets has a Bayesian meaning, Yarin Gal ...
- 1,126
3
votes
Should I apply normalization to the observations in deep reinforcement learning?
The use of normalisation in neural networks and many other (but not all - decision trees are a notable exception) machine learning methods, is to improve the quality of the parameter space with ...
- 30.3k
3
votes
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Does regularization just mean using an augmented loss function?
Regularization is not limited to methods like L1/L2 regularization which are specific versions of what you showed.
Regularization is any technique that would prevent network from overfitting and help ...
- 296
3
votes
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Why does L1 regularization yield sparse features?
In L1 regularization, the penalty term you compute for every parameter is a function of the absolute value of a given weight (times some regularization factor).
Thus, irrespective of whether a weight ...
- 815
3
votes
Accepted
What is the difference between TensorFlow's callbacks and early stopping?
Early stopping and callbacks are two different concepts:
Early stopping is a machine learning concept about when to stop training your model to avoid overfitting: You monitor a target value (e.g. ...
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3
votes
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Which is a better form of regularization: lasso (L1) or ridge (L2)?
The following graph shows the constraint region (green), along with contours for Residual sum of squares (red ellipse). These are iso-lines signifying that points on an ellipse have the same RSS.
...
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How does dropout work during backpropagation?
Backpropagation on a network with dropout works just as it does normally, it calculates the gradients and updates the weights.
Longer explanation
Dropout is a regularization technique which drops ...
- 2,103
3
votes
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Do different models using early stopping have the same validation set to check model training performance?
Short answer: Yes, the validation set should be the same otherwise you risk that a "lucky" set of validation samples is responsible for better performance.
Long answer: A fair comparison of ...
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2
votes
Can dropout layers not influence LSTM training?
A couple of points:
Have you firstly scaled your data, e.g. using MinMaxScaler? This could be one reason why your loss readings remain high.
Additionally, consider that while Dropout can be useful ...
- 151
2
votes
Why is dropout favoured compared to reducing the number of units in hidden layers?
The idea of dropout is that, at training time, with a certain probability $p_i \in [0, 1]$, the unit (or neuron) $i$ is dropped, $\forall i$, that is, the output of unit $i$ is set to zero so that $i$ ...
- 39.6k
2
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Should I remove the units of a neural network or increase dropout?
There is no incentive to increase the size of the model for not reason. If a model of size x gives the best possible performance, there is no reason to use a model of size 2*x with 0.5 dropout during ...
- 446
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Why did the L1/L2 regularization technique not improve my accuracy?
You have a small dataset. Should you even be using neural nets? Have you done any diagnostics to see if you even have enough data? Are you using the right metric? Accuracy is not always the ...
- 21
2
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How does L2 regularization make weights smaller?
Here is my take.
The larger the $\lambda$, the more the corresponding regularization term for a coefficient will be big, so when minimizing the cost function, the coefficient will be reduced by a ...
- 136
2
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Are there any rules of thumb for having some idea of what capacity a neural network needs to have for a given problem?
Theoretical results
Rather than providing a rule of thumb (which can be misleading, so I am not a big fan of them), I will provide some theoretical results (the first one is also reported in paper How ...
- 39.6k
2
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Are there any rules of thumb for having some idea of what capacity a neural network needs to have for a given problem?
This may sound counter intuitive but one of the biggest rules of thumb for model capacity in deep learning:
IT SHOULD OVERFIT.
Once you get a model to overfit, its easier to experiment with ...
- 2,359
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Does L1/L2 Regularization help reach an optimum result faster?
I am not aware of any empirical results regarding this question.
But in theory, adding a regularization term shall make the learning task actually even harder, since there is suddenly a second loss ...
- 815
2
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When would bias regularisation and activation regularisation be necessary?
Regularizer's are used as a means to combat over fitting.They essentially create a cost function penalty which tries to prevent quantities from becoming to large. I have primarily used kernel ...
- 704
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What are the conceptual differences between regularisation and optimisation in deep neural nets?
You are correct.
The main conceptual difference is that optimization is about finding the set of parameters/weights that maximizes/minimizes some objective function (which can also include a ...
- 39.6k
2
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What does it mean when accuracy of regularized model is higher for training set than for validation set?
It implies that your regularization effects are too much, and prevent the model from learning from data. Also, at such a low accuracy (~10%), we can't really talk about overfitting.
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Should I apply normalization to the observations in deep reinforcement learning?
On creating custom environments:
... always normalize your observation space when you can, i.e., when you know the boundaries
(From stable-baselines)
You could normalize them as part of the ...
- 531
2
votes
Accepted
How does Regularization Reduce Overfitting?
I think different mathematical explanations exist for different situations where regularization is useful. The importance of regularization varies by problem as well. It is absolutely necessary when $...
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What are the consequences when we multiply, instead of add, a penalty term?
Well let's consider one, Ridge regression.
We have 2 terms:
the regression loss $L^{pred} = \sum(f(x) - y)^2$, which we can see that it is a sum of squared values, thus $L^{pred} \ge 0$
the ...
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1
vote
Does regularization just mean using an augmented loss function?
Also, keep in mind that not just any augmentation of the loss function is a regularization.
For example, you can add terms to a loss function that enforce constraints on the solution but do not ...
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