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Found 12 papers, 4 papers with code
Lipschitz-regularized gradient flows and generative particle algorithms for high-dimensional scarce data
We build a new class of generative algorithms capable of efficiently learning an arbitrary target distribution from possibly scarce, high-dimensional data and subsequently generate new samples.
Function-space regularized Rényi divergences
We propose a new family of regularized R\'enyi divergences parametrized not only by the order $\alpha$ but also by a variational function space.
A Variance Reduction Method for Neural-based Divergence Estimation
Unfortunately, the approximation of expectations that are inherent in variational formulas by statistical averages can be problematic due to high statistical variance, e. g., exponential for the Kullback-Leibler divergence and certain estimators.
Forward Looking Best-Response Multiplicative Weights Update Methods for Bilinear Zero-sum Games
Our work focuses on extra gradient learning algorithms for finding Nash equilibria in bilinear zero-sum games.
Inference of Stochastic Dynamical Systems from Cross-Sectional Population Data
Inferring the driving equations of a dynamical system from population or time-course data is important in several scientific fields such as biochemistry, epidemiology, financial mathematics and many others.
$(f,Γ)$-Divergences: Interpolating between $f$-Divergences and Integral Probability Metrics
We develop a rigorous and general framework for constructing information-theoretic divergences that subsume both $f$-divergences and integral probability metrics (IPMs), such as the $1$-Wasserstein distance.
Enhancing Speech Intelligibility in Text-To-Speech Synthesis using Speaking Style Conversion
Intelligibility enhancement as quantified by the Intelligibility in Bits (SIIB-Gauss) measure shows that the proposed Lombard-SSDRC TTS system shows significant relative improvement between 110% and 130% in speech-shaped noise (SSN), and 47% to 140% in competing-speaker noise (CSN) against the state-of-the-art TTS approach.
Speaker Conditional WaveRNN: Towards Universal Neural Vocoder for Unseen Speaker and Recording Conditions
In terms of performance, our system has been preferred over the baseline TTS system by 60% over 15. 5% and by 60. 9% over 32. 6%, for seen and unseen speakers, respectively.
Ranked #11 on
Speech Synthesis
on LibriTTS
Optimizing Variational Representations of Divergences and Accelerating their Statistical Estimation
Recently, they have gained popularity in machine learning as a tractable and scalable approach for training probabilistic models and for statistically differentiating between data distributions.
Predictive modeling approaches in laser-based material processing
Predictive modelling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new materials, processes and structures.
Cumulant GAN
In this paper, we propose a novel loss function for training Generative Adversarial Networks (GANs) aiming towards deeper theoretical understanding as well as improved stability and performance for the underlying optimization problem.
Training Generative Adversarial Networks with Weights
The impressive success of Generative Adversarial Networks (GANs) is often overshadowed by the difficulties in their training.
