diff --git a/docs/api/initializer.rst b/docs/api/initializer.rst
index 94a60e4021b0..30b2df35664a 100644
--- a/docs/api/initializer.rst
+++ b/docs/api/initializer.rst
@@ -60,12 +60,12 @@ Built-in initializers
 
 
 
-.. class:: XaiverInitializer
+.. class:: XavierInitializer
 
    The initializer documented in the paper [Bengio and Glorot 2010]: *Understanding
    the difficulty of training deep feedforward neuralnetworks*.
 
-   There are several different version of the XaiverInitializer used in the wild.
+   There are several different version of the XavierInitializer used in the wild.
    The general idea is that the variance of the initialization distribution is controlled
    by the dimensionality of the input and output. As a distribution one can either choose
    a normal distribution with μ = 0 and σ² or a uniform distribution from -σ to σ.
@@ -81,7 +81,7 @@ Built-in initializers
    - mxnet: 3 / (in + out)
 
    Distribution and variant can be chosen by enums (prefixed by ``xv_``).
-   As an example take ``mx.XaiverInitializer(distribution = mx.xv_normal, variant = mx.xv_mxnet)``,
+   As an example take ``mx.XavierInitializer(distribution = mx.xv_normal, variant = mx.xv_mxnet)``,
    which is currently the default.
 
 
diff --git a/docs/api/io.rst b/docs/api/io.rst
index f1ab959be6f2..6186db52ecc1 100644
--- a/docs/api/io.rst
+++ b/docs/api/io.rst
@@ -324,7 +324,7 @@ libmxnet data providers
    
    
    :param prefetch_buffer: Backend Param: Number of prefetched parameters
-   :type prefetch_buffer: , optional, default=4
+   :type prefetch_buffer: long (non-negative), optional, default=4
    
    
    :param rand_crop: Augmentation Param: Whether to random crop on the image
@@ -467,7 +467,7 @@ libmxnet data providers
    
    
    :param prefetch_buffer: Backend Param: Number of prefetched parameters
-   :type prefetch_buffer: , optional, default=4
+   :type prefetch_buffer: long (non-negative), optional, default=4
    
    :return: the constructed :class:`MXDataProvider`.
 
diff --git a/docs/api/symbolic-node.rst b/docs/api/symbolic-node.rst
index 78e359537ec3..d847c6a8bd38 100644
--- a/docs/api/symbolic-node.rst
+++ b/docs/api/symbolic-node.rst
@@ -186,7 +186,7 @@ Public APIs
    
    
    :param momentum: Momentum for moving average
-   :type momentum: float, optional, default=0.1
+   :type momentum: float, optional, default=0.9
    
    :param Base.Symbol name: The name of the :class:`SymbolicNode`. (e.g. `:my_symbol`), optional.
    
diff --git a/src/initializer.jl b/src/initializer.jl
index 2fac334448a5..01744a760448 100644
--- a/src/initializer.jl
+++ b/src/initializer.jl
@@ -100,12 +100,12 @@ function _init_weight(self :: NormalInitializer, name :: Base.Symbol, array :: N
 end
 
 #=doc
-.. class:: XaiverInitializer
+.. class:: XavierInitializer
 
    The initializer documented in the paper [Bengio and Glorot 2010]: *Understanding
    the difficulty of training deep feedforward neuralnetworks*.
 
-   There are several different version of the XaiverInitializer used in the wild.
+   There are several different version of the XavierInitializer used in the wild.
    The general idea is that the variance of the initialization distribution is controlled
    by the dimensionality of the input and output. As a distribution one can either choose
    a normal distribution with μ = 0 and σ² or a uniform distribution from -σ to σ.
@@ -121,20 +121,20 @@ end
    - mxnet: 3 / (in + out)
 
    Distribution and variant can be chosen by enums (prefixed by ``xv_``).
-   As an example take ``mx.XaiverInitializer(distribution = mx.xv_normal, variant = mx.xv_mxnet)``,
+   As an example take ``mx.XavierInitializer(distribution = mx.xv_normal, variant = mx.xv_mxnet)``,
    which is currently the default.
 =#
 
-@enum XaiverDistribution xv_uniform xv_normal
-@enum XaiverVariant xv_original xv_mrsa xv_caffe_avg xv_caffe_in zv_caffe_out xv_mxnet
+@enum XavierDistribution xv_uniform xv_normal
+@enum XavierVariant xv_original xv_mrsa xv_caffe_avg xv_caffe_in zv_caffe_out xv_mxnet
 
-immutable XaiverInitializer <: AbstractInitializer
-  distribution :: XaiverDistribution
-  variant :: XaiverVariant
+immutable XavierInitializer <: AbstractInitializer
+  distribution :: XavierDistribution
+  variant :: XavierVariant
 end
-XaiverInitializer(; distribution = xv_uniform, variant = xv_mxnet) = XaiverInitializer(distribution, variant)
+XavierInitializer(; distribution = xv_uniform, variant = xv_mxnet) = XavierInitializer(distribution, variant)
 
-function _init_weight(self :: XaiverInitializer, name :: Base.Symbol, array :: NDArray)
+function _init_weight(self :: XavierInitializer, name :: Base.Symbol, array :: NDArray)
   dims    = size(array)
   fan_in  = prod(dims[2:end])
   fan_out = dims[1]