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Found 8 papers, 4 papers with code
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
Heuristics are crucial in SAT solvers, while no heuristic rules are suitable for all problem instances.
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
