arXiv research

A novel framework refines diffusion models iteratively for better downstream reward optimization.

Refines neural network predictions using background knowledge for improved accuracy.

GENESIS-V2 infers unordered object representations without iterative refinement.

LARA forecasts financial asset trends by refining noisy labels and extracting profitable samples.

To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (IFR-CS), equipped with fixed transforms, to restore the meaningful structures and details. Nevertheless, the proposed IFR-CS still has some …

Variational methods that rely on a recognition network to approximate the posterior of directed graphical models offer better inference and learning than previous methods. Recent advances that exploit the capacity and flexibility in this approach have expanded what kinds of models can be trained. However, as a proposal…

We propose a conditional non-autoregressive neural sequence model based on iterative refinement. The proposed model is designed based on the principles of latent variable models and denoising autoencoders, and is generally applicable to any sequence generation task. We extensively evaluate the proposed model on machine…

Algorithm recovers causal graphs from data with fewer tests.

Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales. To overcome such a limitation, in this work, we propose a recurrent attentional convolutional-deconvolution network (RACDNN). Using spatial transformer and recurrent …

Improved image generation through iterative flow matching to reduce hallucinations.

We propose a black-box variational inference method to approximate intractable distributions with an increasingly rich approximating class. Our method, termed variational boosting, iteratively refines an existing variational approximation by solving a sequence of optimization problems, allowing the practitioner to trad…

Algorithm recovers causal graphs in presence of latent confounders and selection bias.

Refines deep generative models to improve data density precision.

IterefinE combines KG refinement with embeddings to improve KG quality.

ICR speeds up GP modeling on unevenly spaced data.

Tab-TRM uses recursive model for insurance pricing on tabular data.

This paper refines the Gaussian Sinkhorn algorithm for general multivariate models.

Improved text-to-image alignment using iterative VQA feedback.

This study develops a NURBS-based method for conformal surface flattening without singularities.

AURA: Adaptive Uncertainty-aware Refinement for LLM-as-a-Judge Auditing

Study finds differences in LTs across tasks and architectures, proposing a consensus-based method for generating refined lottery tickets.

Quantizes symplectic fibrations to analyze vector bundles and metrics.

WaveGrad generates high-fidelity audio using gradient estimation.

Self-guiding diffusion models improve time series forecasting, refinement, and generation.

MaxMax Q-Learning improves coordination in multi-agent reinforcement learning by refining action selection.

Gradient descent with small random init mimics spectral methods for low-rank matrix recovery.

We proof existence theorems for the Dirichlet problem for hypersurfaces of constant special Lagrangian curvature in Hadamard manifolds. The first results are obtained using the continuity method and approximation and then refined using two iterations of the Perron method. The a-priori estimates used in the continuity m…

Study Kähler-Einstein potentials on stable varieties near singularities

In this letter, we address sparse signal recovery using spike and slab priors. In particular, we focus on a Bayesian framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. The optimization resulting from spike and slab prior maximization is known to be a hard non-convex problem, a…

SLHF uses sequential game theory to optimize preferences from human feedback.

We give improved algorithms for the $\ell_{p}$-regression problem, $\min_{x} \|x\|_{p}$ such that $A x=b,$ for all $p \in (1,2) \cup (2,\infty).$ Our algorithms obtain a high accuracy solution in $\tilde{O}_{p}(m^{\frac{|p-2|}{2p + |p-2|}}) \le \tilde{O}_{p}(m^{\frac{1}{3}})$ iterations, where each iteration requires s…

Directed latent variable models that formulate the joint distribution as $p(x,z) = p(z) p(x \mid z)$ have the advantage of fast and exact sampling. However, these models have the weakness of needing to specify $p(z)$, often with a simple fixed prior that limits the expressiveness of the model. Undirected latent variabl…

We give a new geometric obstruction to the iterated Bing double of a knot being a slice link: for n>1 the (n+1)-st iterated Bing double of a knot is rationally slice if and only if the n-th iterated Bing double of the knot is rationally slice. The main technique of the proof is a covering link construction simplifying …

Optimal learning rate schedules derived for various tasks.

Online learning to rank is a core problem in machine learning. In Lattimore et al. (2018), a novel online learning algorithm was proposed based on topological sorting. In the paper they provided a set of self-normalized inequalities (a) in the algorithm as a criterion in iterations and (b) to provide an upper bound for…

The iterative nature of the expectation maximization (EM) algorithm presents a challenge for privacy-preserving estimation, as each iteration increases the amount of noise needed. We propose a practical private EM algorithm that overcomes this challenge using two innovations: (1) a novel moment perturbation formulation…

Proposes a method to refine PDE-driven high-dimensional rare-event simulation.

Estimate arrival times in random recursive trees using iterated Jordan centralities.

This paper investigates multilevel initialization strategies for training very deep neural networks with a layer-parallel multigrid solver. The scheme is based on the continuous interpretation of the training problem as a problem of optimal control, in which neural networks are represented as discretizations of time-de…

Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications. In this work, we extract trees or collection of sub-trees from image data by, first deriving a graph-based representation of the volumetric data and then, posing the tree extraction as a graph …

AIHT improves online high-dimensional quantile regression by separating support discovery and refinement.

Molecule optimization is about generating molecule $Y$ with more desirable properties based on an input molecule $X$. The state-of-the-art approaches partition the molecules into a large set of substructures $S$ and grow the new molecule structure by iteratively predicting which substructure from $S$ to add. However, s…

A new method for estimating large-scale linear models with improved precision.

We use a cluster ensemble to determine the number of clusters, k, in a group of data. A consensus similarity matrix is formed from the ensemble using multiple algorithms and several values for k. A random walk is induced on the graph defined by the consensus matrix and the eigenvalues of the associated transition proba…

Unified framework for AMP iterations using graph indexing.

DP-SEP privatizes EP by refining a single factor per data point.

We re-derive Manolescu's unoriented skein exact triangle for knot Floer homology over F_2 combinatorially using grid diagrams, and extend it to the case with Z coefficients by sign refinements. Iteration of the triangle gives a cube of resolutions that converges to the knot Floer homology of an oriented link. Finally, …

Artificial Neural Networks (ANNs) have demonstrated remarkable utility in various challenging machine learning applications. While formally verified properties of their behaviors are highly desired, they have proven notoriously difficult to derive and enforce. Existing approaches typically formulate this problem as a p…