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Found 29 papers, 5 papers with code
Depth Anything with Any Prior
To this end, we design a coarse-to-fine pipeline to progressively integrate the two complementary depth sources.
OmniAudio: Generating Spatial Audio from 360-Degree Video
To generate spatial audio from 360-degree video, we propose a novel framework OmniAudio, which leverages self-supervised pre-training using both spatial audio data (in FOA format) and large-scale non-spatial data.
FlashAudio: Rectified Flows for Fast and High-Fidelity Text-to-Audio Generation
To alleviate the inefficient timesteps allocation and suboptimal distribution of noise, FlashAudio optimizes the time distribution of rectified flow with Bifocal Samplers and proposes immiscible flow to minimize the total distance of data-noise pairs in a batch vias assignment.
GTSinger: A Global Multi-Technique Singing Corpus with Realistic Music Scores for All Singing Tasks
The scarcity of high-quality and multi-task singing datasets significantly hinders the development of diverse controllable and personalized singing tasks, as existing singing datasets suffer from low quality, limited diversity of languages and singers, absence of multi-technique information and realistic music scores, and poor task suitability.
MEDIC: Zero-shot Music Editing with Disentangled Inversion Control
To counteract these issues, we introduce the Disentangled Inversion technique to disentangle the diffusion process into triple branches, rectifying the deviated path of the source branch caused by DDIM inversion.
AudioLCM: Text-to-Audio Generation with Latent Consistency Models
To overcome the convergence issue inherent in LDMs with reduced sample iterations, we propose the Guided Latent Consistency Distillation with a multi-step Ordinary Differential Equation (ODE) solver.
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
We suggest a general oracle-based framework that captures different parallel stochastic optimization settings described by a dependency graph, and derive generic lower bounds in terms of this graph.
Distributed Stochastic Multi-Task Learning with Graph Regularization
We propose methods for distributed graph-based multi-task learning that are based on weighted averaging of messages from other machines.
Gradient Sparsification for Communication-Efficient Distributed Optimization
Modern large scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures.
Improved Optimization of Finite Sums with Minibatch Stochastic Variance Reduced Proximal Iterations
We present novel minibatch stochastic optimization methods for empirical risk minimization problems, the methods efficiently leverage variance reduced first-order and sub-sampled higher-order information to accelerate the convergence speed.
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
We consider empirical risk minimization of linear predictors with convex loss functions.
Efficient coordinate-wise leading eigenvector computation
We develop and analyze efficient "coordinate-wise" methods for finding the leading eigenvector, where each step involves only a vector-vector product.
Stochastic Canonical Correlation Analysis
We study the sample complexity of canonical correlation analysis (CCA), \ie, the number of samples needed to estimate the population canonical correlation and directions up to arbitrarily small error.
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch-Prox
We present and analyze an approach for distributed stochastic optimization which is statistically optimal and achieves near-linear speedups (up to logarithmic factors).
Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates
We consider the problems of estimation and of constructing component-wise confidence intervals in a sparse high-dimensional linear regression model when some covariates of the design matrix are missing completely at random.
Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data
Sketching techniques have become popular for scaling up machine learning algorithms by reducing the sample size or dimensionality of massive data sets, while still maintaining the statistical power of big data.
Warm Starting Bayesian Optimization
We develop a framework for warm-starting Bayesian optimization, that reduces the solution time required to solve an optimization problem that is one in a sequence of related problems.
Efficient Distributed Learning with Sparsity
We propose a novel, efficient approach for distributed sparse learning in high-dimensions, where observations are randomly partitioned across machines.
A General Distributed Dual Coordinate Optimization Framework for Regularized Loss Minimization
In modern large-scale machine learning applications, the training data are often partitioned and stored on multiple machines.
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
We consider the problem of removing and replacing clouds in satellite image sequences, which has a wide range of applications in remote sensing.
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
We study the stochastic optimization of canonical correlation analysis (CCA), whose objective is nonconvex and does not decouple over training samples.
Distributed Multi-Task Learning with Shared Representation
We study the problem of distributed multi-task learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensional subspaces, i. e. when the predictor matrix has low rank.
Multi-Information Source Optimization
We consider Bayesian optimization of an expensive-to-evaluate black-box objective function, where we also have access to cheaper approximations of the objective.
Parallel Bayesian Global Optimization of Expensive Functions
We also show that the resulting one-step Bayes optimal algorithm for parallel global optimization finds high-quality solutions with fewer evaluations than a heuristic based on approximately maximizing the q-EI.
Reducing Runtime by Recycling Samples
Contrary to the situation with stochastic gradient descent, we argue that when using stochastic methods with variance reduction, such as SDCA, SAG or SVRG, as well as their variants, it could be beneficial to reuse previously used samples instead of fresh samples, even when fresh samples are available.
Distributed Multitask Learning
We present a communication-efficient estimator based on the debiased lasso and show that it is comparable with the optimal centralized method.
Bayesian optimization for materials design
We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets.
Inference for Sparse Conditional Precision Matrices
observations of a random vector $(X, Z)$, where $X$ is a high-dimensional vector and $Z$ is a low-dimensional index variable, we study the problem of estimating the conditional inverse covariance matrix $\Omega(z) = (E[(X-E[X \mid Z])(X-E[X \mid Z])^T \mid Z=z])^{-1}$ under the assumption that the set of non-zero elements is small and does not depend on the index variable.
