diff --git a/torchflows/bijections/finite/autoregressive/architectures.py b/torchflows/bijections/finite/autoregressive/architectures.py
index a45140d..66046b0 100644
--- a/torchflows/bijections/finite/autoregressive/architectures.py
+++ b/torchflows/bijections/finite/autoregressive/architectures.py
@@ -16,7 +16,9 @@
     LRSForwardMaskedAutoregressive,
     LRSInverseMaskedAutoregressive,
     DenseSigmoidalCoupling,
-    DeepDenseSigmoidalCoupling
+    DeepDenseSigmoidalCoupling, DeepSigmoidalInverseMaskedAutoregressive, DeepSigmoidalForwardMaskedAutoregressive,
+    DenseSigmoidalInverseMaskedAutoregressive, DenseSigmoidalForwardMaskedAutoregressive,
+    DeepDenseSigmoidalInverseMaskedAutoregressive, DeepDenseSigmoidalForwardMaskedAutoregressive
 )
 from torchflows.bijections.base import BijectiveComposition
 from torchflows.bijections.finite.autoregressive.layers_base import CouplingBijection, \
@@ -221,6 +223,40 @@ def __init__(self,
         super().__init__(event_shape, bijections, **kwargs)
 
 
+class InverseAutoregressiveDeepSF(BijectiveComposition):
+    """Inverse autoregressive deep sigmoidal flow architecture.
+
+    Reference: Huang et al. "Neural Autoregressive Flows" (2018); https://arxiv.org/abs/1804.00779.
+    """
+
+    def __init__(self,
+                 event_shape,
+                 n_layers: int = 2,
+                 edge_list: List[Tuple[int, int]] = None,
+                 **kwargs):
+        if isinstance(event_shape, int):
+            event_shape = (event_shape,)
+        bijections = make_basic_layers(DeepSigmoidalInverseMaskedAutoregressive, event_shape, n_layers, edge_list)
+        super().__init__(event_shape, bijections, **kwargs)
+
+
+class MaskedAutoregressiveDeepSF(BijectiveComposition):
+    """Masked autoregressive deep sigmoidal flow architecture.
+
+    Reference: Huang et al. "Neural Autoregressive Flows" (2018); https://arxiv.org/abs/1804.00779.
+    """
+
+    def __init__(self,
+                 event_shape,
+                 n_layers: int = 2,
+                 edge_list: List[Tuple[int, int]] = None,
+                 **kwargs):
+        if isinstance(event_shape, int):
+            event_shape = (event_shape,)
+        bijections = make_basic_layers(DeepSigmoidalForwardMaskedAutoregressive, event_shape, n_layers, edge_list)
+        super().__init__(event_shape, bijections, **kwargs)
+
+
 class CouplingDenseSF(BijectiveComposition):
     """Coupling dense sigmoidal flow architecture.
 
@@ -238,6 +274,40 @@ def __init__(self,
         super().__init__(event_shape, bijections, **kwargs)
 
 
+class InverseAutoregressiveDenseSF(BijectiveComposition):
+    """Inverse autoregressive dense sigmoidal flow architecture.
+
+    Reference: Huang et al. "Neural Autoregressive Flows" (2018); https://arxiv.org/abs/1804.00779.
+    """
+
+    def __init__(self,
+                 event_shape,
+                 n_layers: int = 2,
+                 edge_list: List[Tuple[int, int]] = None,
+                 **kwargs):
+        if isinstance(event_shape, int):
+            event_shape = (event_shape,)
+        bijections = make_basic_layers(DenseSigmoidalInverseMaskedAutoregressive, event_shape, n_layers, edge_list)
+        super().__init__(event_shape, bijections, **kwargs)
+
+
+class MaskedAutoregressiveDenseSF(BijectiveComposition):
+    """Masked autoregressive dense sigmoidal flow architecture.
+
+    Reference: Huang et al. "Neural Autoregressive Flows" (2018); https://arxiv.org/abs/1804.00779.
+    """
+
+    def __init__(self,
+                 event_shape,
+                 n_layers: int = 2,
+                 edge_list: List[Tuple[int, int]] = None,
+                 **kwargs):
+        if isinstance(event_shape, int):
+            event_shape = (event_shape,)
+        bijections = make_basic_layers(DenseSigmoidalForwardMaskedAutoregressive, event_shape, n_layers, edge_list)
+        super().__init__(event_shape, bijections, **kwargs)
+
+
 class CouplingDeepDenseSF(BijectiveComposition):
     """Coupling deep-dense sigmoidal flow architecture.
 
@@ -255,6 +325,40 @@ def __init__(self,
         super().__init__(event_shape, bijections, **kwargs)
 
 
+class InverseAutoregressiveDeepDenseSF(BijectiveComposition):
+    """Inverse autoregressive deep-dense sigmoidal flow architecture.
+
+    Reference: Huang et al. "Neural Autoregressive Flows" (2018); https://arxiv.org/abs/1804.00779.
+    """
+
+    def __init__(self,
+                 event_shape,
+                 n_layers: int = 2,
+                 edge_list: List[Tuple[int, int]] = None,
+                 **kwargs):
+        if isinstance(event_shape, int):
+            event_shape = (event_shape,)
+        bijections = make_basic_layers(DeepDenseSigmoidalInverseMaskedAutoregressive, event_shape, n_layers, edge_list)
+        super().__init__(event_shape, bijections, **kwargs)
+
+
+class MaskedAutoregressiveDeepDenseSF(BijectiveComposition):
+    """Masked autoregressive deep-dense sigmoidal flow architecture.
+
+    Reference: Huang et al. "Neural Autoregressive Flows" (2018); https://arxiv.org/abs/1804.00779.
+    """
+
+    def __init__(self,
+                 event_shape,
+                 n_layers: int = 2,
+                 edge_list: List[Tuple[int, int]] = None,
+                 **kwargs):
+        if isinstance(event_shape, int):
+            event_shape = (event_shape,)
+        bijections = make_basic_layers(DeepDenseSigmoidalForwardMaskedAutoregressive, event_shape, n_layers, edge_list)
+        super().__init__(event_shape, bijections, **kwargs)
+
+
 class UMNNMAF(BijectiveComposition):
     """Unconstrained monotonic neural network masked autoregressive flow (UMNN-MAF) architecture.
 
diff --git a/torchflows/bijections/finite/autoregressive/layers.py b/torchflows/bijections/finite/autoregressive/layers.py
index 4e1eb22..5d4b173 100644
--- a/torchflows/bijections/finite/autoregressive/layers.py
+++ b/torchflows/bijections/finite/autoregressive/layers.py
@@ -209,6 +209,32 @@ def __init__(self,
         super().__init__(transformer, coupling, conditioner_transform)
 
 
+class DeepSigmoidalInverseMaskedAutoregressive(InverseMaskedAutoregressiveBijection):
+    def __init__(self,
+                 event_shape: torch.Size,
+                 context_shape: torch.Size = None,
+                 n_hidden_layers: int = 2,
+                 **kwargs):
+        transformer: ScalarTransformer = DeepSigmoid(
+            event_shape=torch.Size(event_shape),
+            n_hidden_layers=n_hidden_layers
+        )
+        super().__init__(event_shape, context_shape, transformer=transformer, **kwargs)
+
+
+class DeepSigmoidalForwardMaskedAutoregressive(MaskedAutoregressiveBijection):
+    def __init__(self,
+                 event_shape: torch.Size,
+                 context_shape: torch.Size = None,
+                 n_hidden_layers: int = 2,
+                 **kwargs):
+        transformer: ScalarTransformer = DeepSigmoid(
+            event_shape=torch.Size(event_shape),
+            n_hidden_layers=n_hidden_layers
+        )
+        super().__init__(event_shape, context_shape, transformer=transformer, **kwargs)
+
+
 class DenseSigmoidalCoupling(CouplingBijection):
     def __init__(self,
                  event_shape: torch.Size,
@@ -235,6 +261,32 @@ def __init__(self,
         super().__init__(transformer, coupling, conditioner_transform)
 
 
+class DenseSigmoidalInverseMaskedAutoregressive(InverseMaskedAutoregressiveBijection):
+    def __init__(self,
+                 event_shape: torch.Size,
+                 context_shape: torch.Size = None,
+                 n_dense_layers: int = 2,
+                 **kwargs):
+        transformer: ScalarTransformer = DenseSigmoid(
+            event_shape=torch.Size(event_shape),
+            n_dense_layers=n_dense_layers
+        )
+        super().__init__(event_shape, context_shape, transformer=transformer, **kwargs)
+
+
+class DenseSigmoidalForwardMaskedAutoregressive(MaskedAutoregressiveBijection):
+    def __init__(self,
+                 event_shape: torch.Size,
+                 context_shape: torch.Size = None,
+                 n_dense_layers: int = 2,
+                 **kwargs):
+        transformer: ScalarTransformer = DenseSigmoid(
+            event_shape=torch.Size(event_shape),
+            n_dense_layers=n_dense_layers
+        )
+        super().__init__(event_shape, context_shape, transformer=transformer, **kwargs)
+
+
 class DeepDenseSigmoidalCoupling(CouplingBijection):
     def __init__(self,
                  event_shape: torch.Size,
@@ -261,6 +313,32 @@ def __init__(self,
         super().__init__(transformer, coupling, conditioner_transform)
 
 
+class DeepDenseSigmoidalInverseMaskedAutoregressive(InverseMaskedAutoregressiveBijection):
+    def __init__(self,
+                 event_shape: torch.Size,
+                 context_shape: torch.Size = None,
+                 n_hidden_layers: int = 2,
+                 **kwargs):
+        transformer: ScalarTransformer = DeepDenseSigmoid(
+            event_shape=torch.Size(event_shape),
+            n_hidden_layers=n_hidden_layers
+        )
+        super().__init__(event_shape, context_shape, transformer=transformer, **kwargs)
+
+
+class DeepDenseSigmoidalForwardMaskedAutoregressive(MaskedAutoregressiveBijection):
+    def __init__(self,
+                 event_shape: torch.Size,
+                 context_shape: torch.Size = None,
+                 n_hidden_layers: int = 2,
+                 **kwargs):
+        transformer: ScalarTransformer = DeepDenseSigmoid(
+            event_shape=torch.Size(event_shape),
+            n_hidden_layers=n_hidden_layers
+        )
+        super().__init__(event_shape, context_shape, transformer=transformer, **kwargs)
+
+
 class LinearAffineCoupling(AffineCoupling):
     def __init__(self, event_shape: torch.Size, **kwargs):
         super().__init__(event_shape, **kwargs, n_layers=1)