Normalising Flows
22 papers with code • 0 benchmarks • 0 datasets
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Latest papers
Robust model training and generalisation with Studentising flows
simonalexanderson/StyleGestures
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Normalising flows are tractable probabilistic models that leverage the power of deep learning to describe a wide parametric family of distributions, all while remaining trainable using maximum likelihood.
Deep Structural Causal Models for Tractable Counterfactual Inference
biomedia-mira/deepscm
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NeurIPS 2020
We formulate a general framework for building structural causal models (SCMs) with deep learning components.
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity
L0SG/NanoFlow
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NeurIPS 2020
Normalizing flows (NFs) have become a prominent method for deep generative models that allow for an analytic probability density estimation and efficient synthesis.
Latent Transformations for Discrete-Data Normalising Flows
Normalising flows (NFs) for discrete data are challenging because parameterising bijective transformations of discrete variables requires predicting discrete/integer parameters.
Style-Controllable Speech-Driven Gesture Synthesis Using Normalising Flows
simonalexanderson/StyleGestures
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Computer Graphics Forum 2020
In interactive scenarios, systems for generating natural animations on the fly are key to achieving believable and relatable characters.
Woodbury Transformations for Deep Generative Flows
yolu1055/WoodburyTransformations
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NeurIPS 2020
In this paper, we introduce Woodbury transformations, which achieve efficient invertibility via the Woodbury matrix identity and efficient determinant calculation via Sylvester's determinant identity.
VFlow: More Expressive Generative Flows with Variational Data Augmentation
Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations.
The Neural Moving Average Model for Scalable Variational Inference of State Space Models
Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training.
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
We show that normalising flows become pathological when used to model targets whose supports have complicated topologies.
MoGlow: Probabilistic and controllable motion synthesis using normalising flows
Data-driven modelling and synthesis of motion is an active research area with applications that include animation, games, and social robotics.
