arXiv research

Flow Annealing Posterior Sampling unifies stochastic-process regression and PDE inverse problems.

New analysis of annealing paths in sampling and estimation.

KL annealing helps VAEs avoid posterior collapse and overfitting.

New approach avoids meta-amortization error in few-shot learning.

DAPS++ improves diffusion-based image restoration by decoupling prior and likelihood.

Investigates quantum vs classical portfolio optimization of 60 stocks.

Framework for multi-scale clustering using phase transitions.

Thermodynamic integration (TI) for computing marginal likelihoods is based on an inverse annealing path from the prior to the posterior distribution. In many cases, the resulting estimator suffers from high variability, which particularly stems from the prior regime. When comparing complex models with differences in a …

New method samples from posterior distributions efficiently.

New framework tackles deep learning issues like local traps and miscalibration.

Reverse annealing boosts quantum matrix factorization performance.

This work benchmarks diffusion model-based samplers for Bayesian inverse problems.

New entropy-based objective for sparse coding improves learning.

D2PCCA integrates deep learning and probabilistic modeling for nonlinear dynamical systems.

Combining diffusion models with Langevin dynamics improves posterior sampling efficiency.

Algorithm estimates graph structure with prior information and Langevin diffusion.

We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference. Variational inference is a deterministic approach to approximate posterior inference in Bayesian models in which a typically non-convex objective function is locally optimized over the parameters of t…

New method uses reinforcement learning to improve Simulated Annealing.

New sampler improves uniform sampling over convex bodies with fewer queries.

aMCL uses annealing to improve hypothesis diversity in ambiguous tasks.

DPMC improves inverse problem solving with MCMC, reducing error in noisy conditions.

This paper presents studies on a deterministic annealing algorithm based on quantum annealing for variational Bayes (QAVB) inference, which can be seen as an extension of the simulated annealing for variational Bayes (SAVB) inference. QAVB is as easy as SAVB to implement. Experiments revealed QAVB finds a better local …

Quantum annealers aim at solving non-convex optimization problems by exploiting cooperative tunneling effects to escape local minima. The underlying idea consists in designing a classical energy function whose ground states are the sought optimal solutions of the original optimization problem and add a controllable qua…

Mathematical analysis shows annealing prevents mode collapse in Gaussian mixtures.

We introduce a novel framework for adversarial training where the target distribution is annealed between the uniform distribution and the data distribution. We posited a conjecture that learning under continuous annealing in the nonparametric regime is stable irrespective of the divergence measures in the objective fu…

Proposes a method to improve SLMC for multimodal distributions.

Simulated annealing improves candidate optimization for multi-objective Bayesian optimization.

AdaAnn optimizes annealing for efficient probability density approximation.

CRAFT improves on existing methods for sampling complex distributions.

Bayesian approach uses generative models as priors for better source separation.

We investigate a hybrid quantum-classical solution method to the mean-variance portfolio optimization problems. Starting from real financial data statistics and following the principles of the Modern Portfolio Theory, we generate parametrized samples of portfolio optimization problems that can be related to quadratic b…

Markov chain Monte Carlo (MCMC) is one of the main workhorses of probabilistic inference, but it is notoriously hard to measure the quality of approximate posterior samples. This challenge is particularly salient in black box inference methods, which can hide details and obscure inference failures. In this work, we ext…

Annealed importance sampling (AIS) is a common algorithm to estimate partition functions of useful stochastic models. One important problem for obtaining accurate AIS estimates is the selection of an annealing schedule. Conventionally, an annealing schedule is often determined heuristically or is simply set as a linear…

CR-AIS improves AIS efficiency by constant rate annealing.

Quantum Annealing Enhanced Reinforcement Learning for Accurate RUL Prediction

Bayesian framework learns prior from data to quantify uncertainty in MRI reconstruction.

The paper analyzes the InfoNCE loss under different temperature schedules using Langevin dynamics.

Study improves sampling from complex distributions using annealed Langevin Monte Carlo.

Researchers analyze a new neural network training method.

We describe an adaptation of the simulated annealing algorithm to nonparametric clustering and related probabilistic models. This new algorithm learns nonparametric latent structure over a growing and constantly churning subsample of training data, where the portion of data subsampled can be interpreted as the inverse …

Annealed Entropic Allocation improves ranking and selection by mitigating hard switching and improving finite-budget discrimination.

A new method uses optimal transport for semi-supervised classification.

Paper analyzes Langevin dynamics for multimodal Gaussian mixtures, controlling errors across dimensions.

Maximum likelihood estimation (MLE) is one of the most important methods in machine learning, and the expectation-maximization (EM) algorithm is often used to obtain maximum likelihood estimates. However, EM heavily depends on initial configurations and fails to find the global optimum. On the other hand, in the field …

We propose a modified expectation-maximization algorithm by introducing the concept of quantum annealing, which we call the deterministic quantum annealing expectation-maximization (DQAEM) algorithm. The expectation-maximization (EM) algorithm is an established algorithm to compute maximum likelihood estimates and appl…

DAPS++ improves diffusion-based image restoration by decoupling prior and likelihood.

QAPCA uses quantum annealing for robust PCA.

Quantum machine learns faster by reverse annealing on AQCs.