Table 5.

Experimental details and network architectures. K is the dimension of z_i, N is the number of layers in the inference network, M is the number of layers in GCN, R is the dimension of X^(i).

	Inference model (${\mu }_{\varphi }$, ${\sigma }_{\varphi }$)		Generative model
	${\mu }_{\varphi }(N=2)$	${\sigma }_{\varphi }(N=2)$	setting	activation
GATE/reGATE (K = 68)	W1,μ : 68 * 256	W1,σ : 68 * 256	k-NN: 32	h1 : Sigmoid
	W2,μ : 256 * 68	W2,σ : 256 * 68	M = 2 R = 5	h2 : Sigmoid
	b1,μ : 256 * 1	b1,σ : 256 * 1	R = 5
	b2,μ : 68 * 1	b2,σ : 68 * 1
	φ1,μ = ReLu	φ1,σ = ReLu
	φ2,μ = Linear	φ2,σ = Linear