Search Results for author:
Found 23 papers, 6 papers with code
Efficient Over-parameterized Matrix Sensing from Noisy Measurements via Alternating Preconditioned Gradient Descent
We consider the noisy matrix sensing problem in the over-parameterization setting, where the estimated rank $r$ is larger than the true rank $r_\star$.
Focus On What Matters: Separated Models For Visual-Based RL Generalization
Perceiving the pre-eminence of image reconstruction in representation learning, we propose SMG (Separated Models for Generalization), a novel approach that exploits image reconstruction for generalization.
SAM-UNet:Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images
We train SAM-UNet on SA-Med2D-16M, the largest 2-dimensional medical image segmentation dataset to date, yielding a universal pretrained model for medical images.
FlamePINN-1D: Physics-informed neural networks to solve forward and inverse problems of 1D laminar flames
For forward problems, FlamePINN-1D aims to solve the flame fields and infer the unknown eigenvalues (such as laminar flame speeds) under the constraints of governing equations and boundary conditions.
Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning
Offline meta reinforcement learning (OMRL) has emerged as a promising approach for interaction avoidance and strong generalization performance by leveraging pre-collected data and meta-learning techniques.
A Fourier Approach to the Parameter Estimation Problem for One-dimensional Gaussian Mixture Models
Second, we reveal that there exists a fundamental limit to the problem of estimating the number of Gaussian components or model order in the mixture model if the number of i. i. d samples is finite.
Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning
As demonstrations, we propose a supervised and a self-supervised implementation of $I(Z; M)$, and empirically show that the corresponding optimization algorithms exhibit remarkable generalization across a broad spectrum of RL benchmarks, context shift scenarios, data qualities and deep learning architectures.
HyperEditor: Achieving Both Authenticity and Cross-Domain Capability in Image Editing via Hypernetworks
Editing real images authentically while also achieving cross-domain editing remains a challenge.
Simple Semantic-Aided Few-Shot Learning
This limitation severely constrains the potential of semantics in Few-Shot Learning.
SCAN-MUSIC: An Efficient Super-resolution Algorithm for Single Snapshot Wide-band Line Spectral Estimation
Moreover, in terms of speed, their performance is comparable to the state-of-the-art algorithms, while being more reliable for reconstructing line spectra with cluster structure.
Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery
In this paper, we propose a method to construct a boundary that discriminates safe and unsafe states.
IFF: A Super-resolution Algorithm for Multiple Measurements
The new feature also allows for a subsampling strategy that can circumvent the computation of singular-value decomposition for large matrices as in the usual subspace methods.
A measurement decoupling based fast algorithm for super-resolving point sources with multi-cluster structure
We consider the problem of resolving closely spaced point sources in one dimension from their Fourier data in a bounded domain.
Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images
The BD modules learn additional breast lesion boundary map to enhance the boundary quality of a segmentation result refinement.
Mathematical Theory of Computational Resolution Limit in Multi-dimensions
Our results indicate that there exists a phase transition phenomenon regarding to the super-resolution factor and the signal-to-noise ratio in each of the two recovery problems.
Differentially Private SGD with Non-Smooth Losses
We significantly relax these restrictive assumptions and establish privacy and generalization (utility) guarantees for private SGD algorithms using output and gradient perturbations associated with non-smooth convex losses.
Mathematical theory for topological photonic materials in one dimension
The main focus is on the existence and stability of interface modes that are induced by topological properties of the bulk structure.
Band Gap
Mathematical Physics
Mathematical Physics
Randomized spectral co-clustering for large-scale directed networks
Directed networks are broadly used to represent asymmetric relationships among units.
Randomized Spectral Clustering in Large-Scale Stochastic Block Models
In this paper, we study the spectral clustering using randomized sketching algorithms from a statistical perspective, where we typically assume the network data are generated from a stochastic block model that is not necessarily of full rank.
Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks
The approach can be roughly divided into two steps: in the first step, a specially designed convolutional neural network (CNN) translates the synthetic aperture radar (SAR) images into simulated optical images in an object-to-object manner; in the second step, the simulated optical image, together with the SAR image and the optical image corrupted by clouds, is fused to reconstruct the corrupted area by a generative adversarial network (GAN) with a particular loss function.
Ranked #7 on
Cloud Removal
on SEN12MS-CR
Differentially Private Precision Matrix Estimation
In this paper, we study the problem of precision matrix estimation when the dataset contains sensitive information.
Differential Privacy for Sparse Classification Learning
By the property of the post-processing holding of differential privacy, the proposed approach satisfies the $\epsilon-$differential privacy even when the original problem is unstable.
Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network
In the proposed D-Net, the dilation convolution is used in the backbone network to obtain a larger receptive field without losing spatial resolution, so as to reduce the loss of feature information and to reduce the difficulty of tiny thin vessels segmentation.
