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Found 17 papers, 5 papers with code
Mean Aggregator Is More Robust Than Robust Aggregators Under Label Poisoning Attacks
Robustness to malicious attacks is of paramount importance for distributed learning.
BAGS: Building Animatable Gaussian Splatting from a Monocular Video with Diffusion Priors
In this work, we propose a method to build animatable 3D Gaussian Splatting from monocular video with diffusion priors.
SweetDreamer: Aligning Geometric Priors in 2D Diffusion for Consistent Text-to-3D
Therefore, we improve the consistency by aligning the 2D geometric priors in diffusion models with well-defined 3D shapes during the lifting, addressing the vast majority of the problem.
Byzantine-Robust Decentralized Stochastic Optimization with Stochastic Gradient Noise-Independent Learning Error
Motivated by this observation, we introduce two variance reduction methods, stochastic average gradient algorithm (SAGA) and loopless stochastic variance-reduced gradient (LSVRG), to Byzantine-robust decentralized stochastic optimization for eliminating the negative effect of the stochastic gradient noise.
Example-based Motion Synthesis via Generative Motion Matching
At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches.
Patch-based 3D Natural Scene Generation from a Single Example
We target a 3D generative model for general natural scenes that are typically unique and intricate.
Simultaneous Best Subset Selection and Dimension Reduction via Primal-Dual Iterations
Sparse reduced rank regression is an essential statistical learning method.
A Correlation-Ratio Transfer Learning and Variational Stein's Paradox
A basic condition for efficient transfer learning is the similarity between a target model and source models.
Ternary and Binary Quantization for Improved Classification
Furthermore, this quantization property could be maintained in the random projections of sparse features, if both the features and random projection matrices are sufficiently sparse.
Cascaded Compressed Sensing Networks: A Reversible Architecture for Layerwise Learning
In the letter, we show that target propagation could be achieved by modeling the network s each layer with compressed sensing, without the need of auxiliary networks.
Deep Learning to Ternary Hash Codes by Continuation
Recently, it has been observed that {0, 1,-1}-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform {-1, 1}-binary codes in image retrieval.
Stochastic Alternating Direction Method of Multipliers for Byzantine-Robust Distributed Learning
This paper aims to solve a distributed learning problem under Byzantine attacks.
MoCo-Flow: Neural Motion Consensus Flow for Dynamic Humans in Stationary Monocular Cameras
In this paper, we introduce Neural Motion Consensus Flow (MoCo-Flow), a representation that models dynamic humans in stationary monocular cameras using a 4D continuous time-variant function.
Byzantine-Robust Decentralized Stochastic Optimization over Static and Time-Varying Networks
In this paper, we consider the Byzantine-robust stochastic optimization problem defined over decentralized static and time-varying networks, where the agents collaboratively minimize the summation of expectations of stochastic local cost functions, but some of the agents are unreliable due to data corruptions, equipment failures or cyber-attacks.
Communication-Censored Linearized ADMM for Decentralized Consensus Optimization
COLA is proven to be convergent when the local cost functions have Lipschitz continuous gradients and the censoring threshold is summable.
Communication-Censored Distributed Stochastic Gradient Descent
Specifically, in CSGD, the latest mini-batch stochastic gradient at a worker will be transmitted to the server if and only if it is sufficiently informative.
Sparse Matrix-based Random Projection for Classification
As a typical dimensionality reduction technique, random projection can be simply implemented with linear projection, while maintaining the pairwise distances of high-dimensional data with high probability.
