diff --git a/neural-networks-1.md b/neural-networks-1.md
index 5581ece0..1d6be00b 100644
--- a/neural-networks-1.md
+++ b/neural-networks-1.md
@@ -110,7 +110,7 @@ Every activation function (or *non-linearity*) takes a single number and perform
 
 **Leaky ReLU.** Leaky ReLUs are one attempt to fix the "dying ReLU" problem. Instead of the function being zero when x < 0, a leaky ReLU will instead have a small negative slope (of 0.01, or so). That is, the function computes \\(f(x) = \mathbb{1}(x < 0) (\alpha x) + \mathbb{1}(x>=0) (x) \\) where \\(\alpha\\) is a small constant. Some people report success with this form of activation function, but the results are not always consistent. The slope in the negative region can also be made into a parameter of each neuron, as seen in PReLU neurons, introduced in [Delving Deep into Rectifiers](http://arxiv.org/abs/1502.01852), by Kaiming He et al., 2015. However, the consistency of the benefit across tasks is presently unclear.
 
-**Maxout**. Other types of units have been proposed that do not have the functional form \\(f(w^Tx + b)\\) where a non-linearity is applied on the dot product between the weights and the data. One relatively popular choice is the Maxout neuron (introduced recently by [Goodfellow et al.](http://www-etud.iro.umontreal.ca/~goodfeli/maxout.html)) that generalizes the ReLU and its leaky version. The Maxout neuron computes the function \\(\max(w_1^Tx+b_1, w_2^Tx + b_2)\\). Notice that both ReLU and Leaky ReLU are a special case of this form (for example, for ReLU we have \\(w_1, b_1 = 0\\)). The Maxout neuron therefore enjoys all the benefits of a ReLU unit (linear regime of operation, no saturation) and does not have its drawbacks (dying ReLU). However, unlike the ReLU neurons it doubles the number of parameters for every single neuron, leading to a high total number of parameters.
+**Maxout**. Other types of units have been proposed that do not have the functional form \\(f(w^Tx + b)\\) where a non-linearity is applied on the dot product between the weights and the data. One relatively popular choice is the Maxout neuron (introduced recently by [Goodfellow et al.](https://arxiv.org/abs/1302.4389)) that generalizes the ReLU and its leaky version. The Maxout neuron computes the function \\(\max(w_1^Tx+b_1, w_2^Tx + b_2)\\). Notice that both ReLU and Leaky ReLU are a special case of this form (for example, for ReLU we have \\(w_1, b_1 = 0\\)). The Maxout neuron therefore enjoys all the benefits of a ReLU unit (linear regime of operation, no saturation) and does not have its drawbacks (dying ReLU). However, unlike the ReLU neurons it doubles the number of parameters for every single neuron, leading to a high total number of parameters.
 
 This concludes our discussion of the most common types of neurons and their activation functions. As a last comment, it is very rare to mix and match different types of neurons in the same network, even though there is no fundamental problem with doing so.