Papers Normalising Flows
“Normalising Flows” 태그가 달린 논문 49편 · 필터 해제
OverFlow: Putting flows on top of neural transducers for better TTS
Neural HMMs are a type of neural transducer recently proposed for sequence-to-sequence modelling in text-to-speech. They combine the best features of classic statistical speech synthesis and modern neural TTS, requiring …
Normalising FlowsSpeech Synthesistext-to-speechText to Speech+1Expressive, Variable, and Controllable Duration Modelling in TTS
Duration modelling has become an important research problem once more with the rise of non-attention neural text-to-speech systems. The current approaches largely fall back to relying on previous statistical parametric s…
Normalising FlowsSpeech Synthesistext-to-speechText to SpeechText-free non-parallel many-to-many voice conversion using normalising flows
Non-parallel voice conversion (VC) is typically achieved using lossy representations of the source speech. However, ensuring only speaker identity information is dropped whilst all other information from the source speec…
Normalising FlowsSpeech Synthesistext-to-speechText to Speech+2Gradient estimators for normalising flows
Recently a machine learning approach to Monte-Carlo simulations called Neural Markov Chain Monte-Carlo (NMCMC) is gaining traction. In its most popular form it uses neural networks to construct normalizing flows which ar…
AttributeNormalising FlowsVariational Gibbs Inference for Statistical Model Estimation from Incomplete Data
Statistical models are central to machine learning with broad applicability across a range of downstream tasks. The models are controlled by free parameters that are typically estimated from data by maximum-likelihood es…
BIG-bench Machine LearningNormalising Flowsparameter estimationVariational InferenceBootstrap Your Flow
Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling. However, current flow-based approaches are limited on chall…
Normalising FlowsSinusoidal Flow: A Fast Invertible Autoregressive Flow
Normalising flows offer a flexible way of modelling continuous probability distributions. We consider expressiveness, fast inversion and exact Jacobian determinant as three desirable properties a normalising flow should …
Normalising FlowsImplicit Riemannian Concave Potential Maps
We are interested in the challenging problem of modelling densities on Riemannian manifolds with a known symmetry group using normalising flows. This has many potential applications in physical sciences such as molecular…
Density EstimationNormalising FlowsEstimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows
One major drawback of state-of-the-art artificial intelligence is its lack of explainability. One approach to solve the problem is taking causality into account. Causal mechanisms can be described by structural causal mo…
Causal DiscoveryDensity EstimationNormalising FlowsParallelised Diffeomorphic Sampling-based Motion Planning
We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PDMP). PDMP is a novel parallelised framework that uses bijective and differentiable mappings, or diffeomorphisms, to transform sampling distributions…
MORPHMotion PlanningNormalising FlowsvalidCopula Flows for Synthetic Data Generation
The ability to generate high-fidelity synthetic data is crucial when available (real) data is limited or where privacy and data protection standards allow only for limited use of the given data, e.g., in medical and fina…
Density EstimationNormalising FlowsSynthetic Data GenerationGaussian Process Latent Variable Flows for Massively Missing Data
Gaussian process latent variable models (GPLVM) are used to perform nonlinear and probabilistic dimensionality reduction. They extend Gaussian processes (GP) to the domain of unsupervised learning. The Bayesian incarnati…
Dimensionality ReductionGaussian ProcessesNormalising FlowsVariational InferenceLearning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows
As data volumes continue to grow, the labelling process increasingly becomes a bottleneck, creating demand for methods that leverage information from unlabelled data. Impressive results have been achieved in semi-supervi…
AttributeGeneral Classificationimage-classificationImage Classification+3Robust model training and generalisation with Studentising flows
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. We discuss how …
modelNormalising FlowsDeep Structural Causal Models for Tractable Counterfactual Inference
We formulate a general framework for building structural causal models (SCMs) with deep learning components. The proposed approach employs normalising flows and variational inference to enable tractable inference of exog…
counterfactualCounterfactual InferenceNormalising FlowsVariational InferenceLatent 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. Having a neural network architecture predi…
Normalising FlowsNanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity
Normalizing flows (NFs) have become a prominent method for deep generative models that allow for an analytic probability density estimation and efficient synthesis. However, a flow-based network is considered to be ineff…
Density EstimationNormalising FlowsSpeech SynthesisStyle-Controllable Speech-Driven Gesture Synthesis Using Normalising Flows
Automatic synthesis of realistic gestures promises to transform the fields of animation, avatars and communicative agents. In off‐line applications, novel tools can alter the role of an animator to that of a director, wh…
Gesture GenerationMotion SynthesisNormalising FlowsWoodbury Transformations for Deep Generative Flows
Normalizing flows are deep generative models that allow efficient likelihood calculation and sampling. The core requirement for this advantage is that they are constructed using functions that can be efficiently inverted…
Normalising FlowsVFlow: More Expressive Generative Flows with Variational Data Augmentation
Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations. However, tractability imposes architectural constraints on generative flows, m…
Density EstimationImage GenerationNormalising FlowsVariational Inference