Because computing the normalizing constant P (X ) is intractable, we instead approximate the posterior with a family of distributions Q λ (Z), indexed by variational parameters λ.
we use To construct β w , we first define a neural network g(•; φ d ), with a single 32-dimensional hidden layer, which "encodes" x w d into a threedimensional vector.