A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
End-to-end learnable Gaussian mixture priors improve diffusion models' exploration and expressiveness.
Proposes diffusion models using mixed Gaussian priors for better data representation.
Plug-and-play L-GM-AMP improves CS recovery for any i.i.d. source prior.
New pruning method retains model expressiveness for NLP tasks.
Proposes scale mixture of NNGPs for more flexible stochastic processes.
PDGMM-VAE uses adaptive priors for better ICA recovery.
In this paper we propose a novel framework for the construction of sparsity-inducing priors. In particular, we define such priors as a mixture of exponential power distributions with a generalized inverse Gaussian density (EP-GIG). EP-GIG is a variant of generalized hyperbolic distributions, and the special cases inclu…
Extends Gaussian Process regression for handling multiple prior distributions.
We study the Nonparametric Maximum Likelihood Estimator (NPMLE) for estimating Gaussian location mixture densities in -dimensions from independent observations. Unlike usual likelihood-based methods for fitting mixtures, NPMLEs are based on convex optimization. We prove finite sample results on the Hellinger accurac…
The study improves representation learning bounds using data-dependent Gaussian mixtures.
Patch priors have become an important component of image restoration. A powerful approach in this category of restoration algorithms is the popular Expected Patch Log-Likelihood (EPLL) algorithm. EPLL uses a Gaussian mixture model (GMM) prior learned on clean image patches as a way to regularize degraded patches. In th…
Paper presents a reparameterized DP-DLGMM for clustering.
The paper studies multi-view representation learning with generalization guarantees and a new regularizer.
This paper presents a new model called infinite mixtures of multivariate Gaussian processes, which can be used to learn vector-valued functions and applied to multitask learning. As an extension of the single multivariate Gaussian process, the mixture model has the advantages of modeling multimodal data and alleviating…
A novel Bayesian method for dynamic sparsity in Gaussian dynamic linear regression.
In recent years, a rich variety of shrinkage priors have been proposed that have great promise in addressing massive regression problems. In general, these new priors can be expressed as scale mixtures of normals, but have more complex forms and better properties than traditional Cauchy and double exponential priors. W…
This work approximates finite neural networks with Gaussian processes, providing error bounds and applications in prior selection.
We propose a greedy variational method for decomposing a non-negative multivariate signal as a weighted sum of Gaussians, which, borrowing the terminology from statistics, we refer to as a Gaussian mixture model. Notably, our method has the following features: (1) It accepts multivariate signals, i.e. sampled multivari…
Improved sample complexity for Gaussian Mixture Models using Pair Correlation Factor.
Deep models memorize training data in geophysical inversion, leading to biased posterior distributions.
Bayesian neural networks with dependent weights converge to Gaussian mixtures.
Learning the parameters of Gaussian mixture models is a fundamental and widely studied problem with numerous applications. In this work, we give new algorithms for learning the parameters of a high-dimensional, well separated, Gaussian mixture model subject to the strong constraint of differential privacy. In particula…
Proposes a neural network for sparsity regularization in inverse problems using Gaussian mixture.
Generalized autoregressive conditional heteroscedasticity (GARCH) models have long been considered as one of the most successful families of approaches for volatility modeling in financial return series. In this paper, we propose an alternative approach based on methodologies widely used in the field of statistical mac…
We assume that a high-dimensional datum, like an image, is a compositional expression of a set of properties, with a complicated non-linear relationship between the datum and its properties. This paper proposes a factorial mixture prior for capturing latent properties, thereby adding structured compositionality to deep…
One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the prior for the latent code in the ancestral sampling procedure for the da…
VampPrior Mixture Model improves clustering in DLVMs.
X-VAE uses data-adaptive Gaussian priors to improve latent space modeling.
We consider the problem of spherical Gaussian Mixture models with components when the components are well separated. A fundamental previous result established that separation of is necessary and sufficient for identifiability of the parameters with polynomial sample complexity (Regev and V…
PIMA autoencoders discover shared features in multimodal scientific data.
Deep generative models are commonly used for generating images and text. Interpretability of these models is one important pursuit, other than the generation quality. Variational auto-encoder (VAE) with Gaussian distribution as prior has been successfully applied in text generation, but it is hard to interpret the mean…
Researchers derive exact priors for finite Bayesian neural networks.
Paper proposes a new MIMO detection algorithm using Gaussian Mixture Expectation Propagation.
Fast Bayesian inference with adaptable priors for real-time applications.
Efficiently learns mixtures of Gaussians without separation assumptions.
Gradient method converges locally linearly for overparameterized Gaussian mixtures.
Computed tomography (CT) equivalent information is needed for attenuation correction in PET imaging and for dose planning in radiotherapy. Prior work has shown that Gaussian mixture models can be used to generate a substitute CT (s-CT) image from a specific set of MRI modalities. This work introduces a more flexible cl…
GMVAEs cluster and generate game levels without labels.
StrADiff separates sources from mixtures without labels, using structured priors.
Difficult image segmentation problems, for instance left atrium MRI, can be addressed by incorporating shape priors to find solutions that are consistent with known objects. Nonetheless, a single multivariate Gaussian is not an adequate model in cases with significant nonlinear shape variation or where the prior distri…
Survey on Bayesian inference for Gaussian mixture models.
Recent work has shown that deep generative models assign higher likelihood to out-of-distribution inputs than to training data. We show that a factor underlying this phenomenon is a mismatch between the nature of the prior distribution and that of the data distribution, a problem found in widely used deep generative mo…
Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information. In high-dimensional settings, the space of possible graphs becomes enormous, rendering even state-of-the-art Bayesian stochastic search computationally…
Study Langevin Monte Carlo for sampling non-log-concave distributions.
Paper tackles MSDA with GMMs and OT, improving over prior art.
Many different methods to train deep generative models have been introduced in the past. In this paper, we propose to extend the variational auto-encoder (VAE) framework with a new type of prior which we call "Variational Mixture of Posteriors" prior, or VampPrior for short. The VampPrior consists of a mixture distribu…
Proposes a tail-adaptive shrinkage method for robust sparse estimation.
Susceptibility of deep neural networks to adversarial attacks poses a major theoretical and practical challenge. All efforts to harden classifiers against such attacks have seen limited success. Two distinct categories of samples to which deep networks are vulnerable, "adversarial samples" and "fooling samples", have b…