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Found 10 papers, 4 papers with code
MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentation
Second, a large-window multiscale mamba module is integrated into the decoder to capture global dependencies across regions and enhance the segmentation capability for complex anatomical structures.
Leave-One-Out Analysis for Nonconvex Robust Matrix Completion with General Thresholding Functions
We study the problem of robust matrix completion (RMC), where the partially observed entries of an underlying low-rank matrix is corrupted by sparse noise.
Elementary Analysis of Policy Gradient Methods
Projected policy gradient under the simplex parameterization, policy gradient and natural policy gradient under the softmax parameterization, are fundamental algorithms in reinforcement learning.
AutoSAT: Automatically Optimize SAT Solvers via Large Language Models
Conflict-Driven Clause Learning (CDCL) is the mainstream framework for solving the Satisfiability problem (SAT), and CDCL solvers typically rely on various heuristics, which have a significant impact on their performance.
Global Convergence of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction
Natural policy gradient (NPG) and its variants are widely-used policy search methods in reinforcement learning.
On the Linear Convergence of Policy Gradient under Hadamard Parameterization
To show the local linear convergence of the algorithm, we have indeed established the contraction of the sub-optimal probability $b_s^k$ (i. e., the probability of the output policy $\pi^k$ on non-optimal actions) when $k\ge k_0$.
Is Attention Better Than Matrix Decomposition?
As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery.
Ranked #7 on
Semantic Segmentation
on PASCAL VOC 2012 test
Accelerated Alternating Projections for Robust Principal Component Analysis
We study robust PCA for the fully observed setting, which is about separating a low rank matrix $\boldsymbol{L}$ and a sparse matrix $\boldsymbol{S}$ from their sum $\boldsymbol{D}=\boldsymbol{L}+\boldsymbol{S}$.
New region force for variational models in image segmentation and high dimensional data clustering
We propose an effective framework for multi-phase image segmentation and semi-supervised data clustering by introducing a novel region force term into the Potts model.
Rapid, Robust, and Reliable Blind Deconvolution via Nonconvex Optimization
To the best of our knowledge, our algorithm is the first blind deconvolution algorithm that is numerically efficient, robust against noise, and comes with rigorous recovery guarantees under certain subspace conditions.
Information Theory Information Theory
