Efficiently infers latent SDEs with scalable memory and time costs.
problem Inference of latent SDEs with high time and memory complexity.
method Amortized reparametrization of expectations under linear SDEs, coupled with efficient gradient approximation.
result Achieves similar performance to adjoint sensitivities with fewer model evaluations.
We introduce Natural Neural Networks, a novel family of algorithms that speed up convergence by adapting their internal representation during training to improve conditioning of the Fisher matrix. In particular, we show a specific example that employs a simple and efficient reparametrization of the neural network weigh…
Study on how reparametrization affects neural nets' parameter spaces from a geometric perspective.
problem Inconsistencies in flatness measures, optimization, and probability densities under reparametrization.
method Riemannian geometry to study invariance of neural nets under reparametrization.
result Invariance of neural nets is an inherent property if the metric is explicitly represented and transformation rules are correct.
A new method for 3D surface registration using dynamic programming.
problem Elastic shape registration of 3D surfaces.
method Optimization over a subset of reparametrizations using dynamic programming.
result Proposes an algorithm that produces a solution closer to optimal than gradient-based methods.
DualVDT improves time-series forecasting with a novel dual reparametrized structure.
problem Time-series forecasting with improved performance and analytical rigor.
method Dual reparametrized variational mechanisms on VAE, latent score based generative model, reverse time stochastic differential equation, variational ancestral sampling, KL divergence reduction.
result Advanced performance in time-series forecasting with reduced KL divergence.
AEVB improves understanding of latent variable models.
problem Training latent variable models efficiently and understanding their limitations.
method Motivates AEVB from EM, emphasizing approximate E-step and M-step.
result AEVB tightens ELBO, improving model training.
We introduce a new algorithm for approximate inference that combines reparametrization, Markov chain Monte Carlo and variational methods. We construct a very flexible implicit variational distribution synthesized by an arbitrary Markov chain Monte Carlo operation and a deterministic transformation that can be optimized…
Proposes spred for solving L1 penalty with SGD.
problem Solving L1 penalty in optimization problems. method Reparametrization and SGD approach.
result Proves spred as an exact differentiable solver of L1. New findings show a balance between data fit and complexity in kernel hyperparameters.
problem Overcorrelation due to reparametrization of kernel hyperparameters.
method Reparametrization of kernel hyperparameters and analysis of marginal likelihood.
result Data fit term influences all other kernel hyperparameters, not just the complexity penalty.
We prove an optimal control on the time-dependent measure of a measurable set under a reparametrized Lagrangian mean curvature flow of almost calibrated submanifolds in a Calabi-Yau manifold. Moreover we give a classification of those Lagrangian translating solitons in Cm that evolve by this reparametrized …
Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectations for one or more target functions - a computational pipeline which is inefficient when the target function(s) are known upfront. In this …
Monge SAM improves deep learning by making sharpness-aware minimization invariant to reparametrizations.
problem Non-invariance of sharpness-aware minimization (SAM) to reparametrizations.
method Introduces Monge SAM, a reparametrization-invariant version of SAM using a Riemannian metric.
result Monge SAM enhances robustness and generalization compared to previous methods.
This paper reviews recent advancements in amortized Variational Inference.
problem Scalability and efficiency issues in traditional Variational Inference.
method Systematic review of various Variational Inference techniques, focusing on amortized approaches.
result Amortized Variational Inference improves scalability and efficiency for generative modeling tasks.
Improves point-cloud reconstruction by optimizing projections with self-attention.
problem Inefficient and non-metric projection methods for sliced Wasserstein distances.
method Proposes distributional sliced Wasserstein distance with self-attention for permutation-invariant and metric optimization.
result Self-attention amortized distributional projection optimization achieves better performance in point-cloud reconstruction.
We consider pairs of a non-empty compact connected and locally connected Hausdorff space and a real-valued continuous function. Our aim is to measure the difference between this kind of the pairs. In this notes we introduce new pseudodistances between pairs associated with reparametrization invariant seminorms. We fini…
New option type preserves fungibility by amortizing payments over time.
problem Traditional installment options destroy fungibility and lapse when payments stop.
method Introduces amortizing perpetual options (AmPOs) with an implicit payment scheme.
result Valuation of AmPOs reduces to vanilla perpetual American options.
A new method optimizes projection directions for sliced Wasserstein distances.
problem Finding informative projecting directions for sliced Wasserstein distances is computationally expensive.
method Amortized projection optimization to predict directions efficiently.
result Proposed amortized models improve generative modeling performance.
Adaptive workflow combines fast amortized inference with MCMC for many datasets.
problem Trade-off between computational speed and sampling accuracy in Bayesian inference.
method Adaptive workflow integrating amortized inference and MCMC with principled diagnostics.
result Efficiency gains with high posterior quality on tens of thousands of datasets.
The variational autoencoder (VAE) is a popular model for density estimation and representation learning. Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate. However, it is often overlooked that an overly-expressive inference model ca…
ASPIRE improves amortized posterior inference for Bayesian inverse problems.
problem Bayesian inverse problems are computationally challenging due to uncertainty quantification.
method Iterative refinement of amortized posteriors using physics-based and summary statistics.
result ASPIRE achieves better posterior approximations with minimal extra computations.
This paper examines how neural architectures support amortized Bayesian inference and its performance under varying conditions.
problem Understanding and evaluating amortized inference under signal-to-noise variation and distribution shift.
method Statistical analysis of neural architectures including feedforward networks, Deep Sets, and Transformers.
result Neural architectures support amortized Bayesian inference, offering controlled generalization error and robustness under varying conditions.
Paper proves trajectories of Chaplygin systems are reparametrized geodesics.
problem Understanding trajectories of Chaplygin systems.
method Constructive proof using modified Riemannian metrics.
result Reparametrized geodesics of Chaplygin systems.
JANA trains networks to approximate Bayesian models efficiently.
problem Intractable likelihood functions and posterior densities in Bayesian models.
method End-to-end training of three networks: summary, posterior, and likelihood networks.
result JANA provides accurate amortized marginal likelihood and posterior predictive estimation.
Completeness of surface metrics established for Sobolev spaces.
problem Ensuring completeness of reparametrization-invariant Sobolev metrics on surface spaces.
method Recasting completeness criteria for infinite-dimensional Riemannian manifolds and applying geometric estimates based on the Michael--Simon--Sobolev inequality.
result Established metric and geodesic completeness for specific Sobolev metrics on immersed surfaces, validating Mumford's conjecture.
Improved diffusion sampling for inverse problems with faster and more robust inference.
problem High computational cost and lack of robustness in diffusion posterior sampling.
method Amortized variational inference with explicit likelihood guidance.
result Improved trade-off between inference speed and robustness to unseen degradations.
Efficiently predicts optimal transport plans using sliced potentials.
problem Predicting optimal transport plans across multiple measure pairs efficiently.
method Regression-based and objective-based amortization strategies using sliced optimal transport potentials.
result Efficient and accurate prediction of optimal transport plans for various tasks.
BayesFlow trains neural networks for fast Bayesian inference.
problem Fast Bayesian inference for complex models.
method Amortized neural networks for intractable posterior distributions.
result Fast inference through pre-trained neural networks.
Simformer uses transformer models to perform flexible Bayesian inference.
problem Current simulation-based inference methods are inflexible and require fixed priors.
method Trains a probabilistic diffusion model with transformer architectures.
result Outperforms state-of-the-art methods on various benchmarks.
New method improves generalization of VAEs by reducing overfitting.
problem Generalization issues in VAEs overfitting to training data.
method Proposed a new training objective to improve amortized inference.
result Improved performance in image modeling and lossless compression.
A new numerical framework simplifies elastic surface matching and comparison.
problem Challenging problem in surface comparison and matching in computer vision.
method Relaxing the geodesic boundary constraint using a varifold fidelity metric.
result Flexibility to deal with arbitrary topologies and sampling patterns, scalability to large meshes.
Adversarial robustness of amortized Bayesian inference is studied, showing it can be improved.
problem Adversarial robustness of amortized Bayesian inference.
method Simulation-based estimation, regularization scheme based on Fisher information.
result Adversarial robustness can be improved with a regularization scheme.
The paper tackles efficient computation of optimal transport by approximating conjugates with amortized optimization.
problem Efficient computation of convex conjugates in optimal transport is challenging and limits the quality of transport maps.
method The approach combines amortized approximations of conjugates with a fine-tuning solver to improve transport map quality.
result The method significantly improves the quality of transport maps for the Wasserstein-2 benchmark and models many 2D couplings and flows.
Neural processes approximate Gaussian process inference, revealing three key costs.
problem Approximating Gaussian process inference with neural processes.
method Bounding KL divergence into three components: label contamination, information bottleneck, and amortization error.
result Characterization of three costs of amortizing Gaussian process inference with neural processes.
Self-consistency improves the accuracy of model comparison methods.
problem Improving the accuracy of model comparison methods when simulation models are misspecified.
method Supplement traditional simulation-based training with a self-consistency loss on unlabeled real data.
result Self-consistency training improves model comparison accuracy, especially in open-world scenarios.
Improved community detection in graphs with probabilistic models.
problem Lack of probabilistic formulation and fixed number of communities in GNN-based methods.
method Combines GNNs with amortized clustering for variable numbers of clusters.
result Improved performance on synthetic and real datasets compared to previous methods.
Classical approaches for approximate inference depend on cleverly designed variational distributions and bounds. Modern approaches employ amortized variational inference, which uses a neural network to approximate any posterior without leveraging the structures of the generative models. In this paper, we propose Amorti…
We simplify SVI volatility smile constraints for three sub-SVIs without numerical methods.
problem No arbitrage constraints for SVI volatility smiles.
method Explicit domain derivation for sub-SVIs without numerical procedures.
result Explicit no arbitrage domains for Symmetric SVI, Vanishing Upward/Downward SVI, and SSVI.
Motivated by Demailly's strategy towards the Kobayashi hyperbolicity conjecture, we study the action on the k-jets of germs of holomorphic discs in a complex manifold X of the reparametrization group of k-jets of germs of biholomorphisms of the source. This reparametrization group is a subgroup of the general linear gr…
Proposes an amortized variational framework for Deep Q Networks.
problem Efficient exploration in deep reinforcement learning.
method Amortized variational inference for action value function approximation.
result Significantly less learning parameters and better performance.
The Square Root Normal Field (SRNF), introduced by Jermyn et al. in [3], provides a way of representing immersed surfaces in R3, and equipping the set of these immersions with a "distance function" (to be precise, a pseudometric) that is easy to compute. Importantly, this distance function is invariant under…
This paper compares amortized methods for Bayesian posterior estimation.
problem Bayesian inference difficulties with iterative routines.
method Amortized in-context Bayesian posterior estimation using transformers and normalizing flows.
result Reverse KL estimator superior for predictive problems.
Amortized VI for DGPs learns efficient inference.
problem Expressive limitations in GP approximations.
method Amortized variational inference for DGPs.
result Improved expressive prior and posterior for DGPs.
In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss. The resulting forward amortized variational inference is a likelihood-free method as its gradient can be sampled without bias and without requiring any evaluation of eith…
New method learns causal models from data efficiently.
problem Learning Structural Causal Models from data is challenging.
method Amortized inference via Conditional Fixed-Point Iterations with transformer embeddings.
result Single model predicts causal mechanisms conditioned on data and graph.
We classify compact Kähler surfaces with nonconstant Killing potentials such that all integral curves of their gradients are reparametrized geodesics.
A new recursive mixture estimation algorithm improves VAE inference efficiency and accuracy.
problem Inaccurate posterior approximation in traditional VAEs.
method Recursive mixture estimation algorithm using functional gradient approach for iterative component selection.
result Significantly higher test data likelihood compared to state-of-the-art methods on benchmark datasets.
We analyze a notion of multiple valued sections of a vector bundle over an abstract smooth Riemannian manifold, which was suggested by W. Allard in the unpublished note "Some useful techniques for dealing with multiple valued functions" and generalizes Almgren's Q-valued functions. We study some relevant properties o…
DAD learns to design experiments quickly, outperforming traditional methods.
problem Real-time decision-making in sequential Bayesian experimental design.
method Amortized design network trained with contrastive information bounds.
result DAD outperforms alternative strategies on various problems.