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Found 13 papers, 4 papers with code
Understanding Forgetting in Continual Learning with Linear Regression
In this paper, we provide a general theoretical analysis of forgetting in the linear regression model via Stochastic Gradient Descent (SGD) applicable to both underparameterized and overparameterized regimes.
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
To address these issues, we first consider the problem in the $\epsilon$ non-interactive LDP model and provide a lower bound of $\Omega(\frac{\sqrt{dk\log d}}{\sqrt{n}\epsilon})$ on the $\ell_2$-norm estimation error for sub-Gaussian data, where $n$ is the sample size and $d$ is the dimension of the space.
On Stability and Generalization of Bilevel Optimization Problem
In this paper, we conduct a thorough analysis on the generalization of first-order (gradient-based) methods for the bilevel optimization problem.
Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing
However, existing LL1-based HU algorithms use a three-factor parameterization of the tensor (i. e., the hyperspectral image cube), which leads to a number of challenges including high per-iteration complexity, slow convergence, and difficulties in incorporating structural prior information.
Attention guided global enhancement and local refinement network for semantic segmentation
First, commonly used upsampling methods in the decoder such as interpolation and deconvolution suffer from a local receptive field, unable to encode global contexts.
Category Guided Attention Network for Brain Tumor Segmentation in MRI
In this model, we design a Supervised Attention Module (SAM) based on the attention mechanism, which can capture more accurate and stable long-range dependency in feature maps without introducing much computational cost.
TransBTS: Multimodal Brain Tumor Segmentation Using Transformer
To capture the local 3D context information, the encoder first utilizes 3D CNN to extract the volumetric spatial feature maps.
Ranked #1 on
Brain Tumor Segmentation
on BRATS 2019
(Dice Score metric)
Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling
This work employs an idea that models spectral images as tensors following the block-term decomposition model with multilinear rank-$(L_r, L_r, 1)$ terms (i. e., the LL1 model) and formulates the HSR problem as a coupled LL1 tensor decomposition problem.
Differentially-private Federated Neural Architecture Search
To address this problem, we propose federated neural architecture search (FNAS), where different parties collectively search for a differentiable architecture by exchanging gradients of architecture variables without exposing their data to other parties.
Tensor completion via nonconvex tensor ring rank minimization with guaranteed convergence
Key words: nonconvex optimization, tensor ring rank, logdet function, tensor completion, alternating direction method of multipliers.
Tensor train rank minimization with nonlocal self-similarity for tensor completion
The TT rank minimization accompany with \emph{ket augmentation}, which transforms a lower-order tensor (e. g., visual data) into a higher-order tensor, suffers from serious block-artifacts.
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI
In this work, we aim to segment brain MRI volumes.
A generalized parametric 3D shape representation for articulated pose estimation
We present a novel parametric 3D shape representation, Generalized sum of Gaussians (G-SoG), which is particularly suitable for pose estimation of articulated objects.
