networks

A rich collection of neural network architectures for use in Approximators.

Classes

ConsistencyModel(*args, **kwargs)	Implements a Consistency Model with Consistency Training (CT) a described in [1-2].
CouplingFlow(*args, **kwargs)	Implements a coupling flow as a sequence of dual couplings with permutations and activation normalization.
DeepSet(*args, **kwargs)	Implements a deep set encoder introduced in [1] for learning permutation-invariant representations of set-based data, as generated by exchangeable models.
FlowMatching(*args, **kwargs)	Implements Optimal Transport Flow Matching, originally introduced as Rectified Flow, with ideas incorporated from [1-3].
FusionTransformer(*args, **kwargs)	Implements a more flexible version of the TimeSeriesTransformer that applies a series of self-attention layers followed by cross-attention between the representation and a learnable template summarized via a recurrent net.
InferenceNetwork(*args, **kwargs)
MLP(*args, **kwargs)	Implements a simple configurable MLP with optional residual connections and dropout.
PointInferenceNetwork(*args, **kwargs)	Implements point estimation for user specified scoring rules by a shared feed forward architecture with separate heads for each scoring rule.
SetTransformer(*args, **kwargs)	Implements the set transformer architecture from [1] which ultimately represents a learnable permutation-invariant function.
SummaryNetwork(*args, **kwargs)
TimeSeriesNetwork(*args, **kwargs)	Implements a LSTNet Architecture as described in [1]
TimeSeriesTransformer(*args, **kwargs)	Creates a regular transformer coupled with Time2Vec embeddings of time used to flexibly compress time series.