Papers Normalising Flows
“Normalising Flows” 태그가 달린 논문 49편 · 필터 해제
Quinoa: a Q-function You Infer Normalized Over Actions
We present an algorithm for learning an approximate action-value soft Q-function in the relative entropy regularised reinforcement learning setting, for which an optimal improved policy can be recovered in closed form. W…
Normalising Flowsreinforcement-learningReinforcement LearningReinforcement Learning (RL)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. To date, however, this strategy has been most applicable to models of independent data.…
Bayesian InferenceNormalising Flowsparameter estimationState Space Models+3Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
We show that normalising flows become pathological when used to model targets whose supports have complicated topologies. In this scenario, we prove that a flow must become arbitrarily numerically noninvertible in order …
Density EstimationNormalising FlowsLocalised Generative Flows
We argue that flow-based density models based on continuous bijections are limited in their ability to learn target distributions with complicated topologies, and propose localised generative flows (LGFs) to address this…
Density EstimationNormalising FlowsMoGlow: 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. This paper introduces a new class of probabilistic, generative, and controlla…
Motion SynthesisNormalising FlowsBlock Neural Autoregressive Flow
Normalising flows (NFS) map two density functions via a differentiable bijection whose Jacobian determinant can be computed efficiently. Recently, as an alternative to hand-crafted bijections, Huang et al. (2018) propose…
Density EstimationNormalising FlowsOn the relationship between Normalising Flows and Variational- and Denoising Autoencoders
Normalising Flows (NFs) are a class of likelihood-based generative models that have recently gained popularity. They are based on the idea of transforming a simple density into that of the data. We seek to better underst…
DenoisingNormalising FlowsRepresentation LearningConditional Density Estimation with Bayesian Normalising Flows
Modeling complex conditional distributions is critical in a variety of settings. Despite a long tradition of research into conditional density estimation, current methods employ either simple parametric forms or are diff…
Density EstimationNormalising FlowsImplicit Weight Uncertainty in Neural Networks
Modern neural networks tend to be overconfident on unseen, noisy or incorrectly labelled data and do not produce meaningful uncertainty measures. Bayesian deep learning aims to address this shortcoming with variational a…
Deep LearningNormalising Flows