research
∙
09/18/2023
Latent assimilation with implicit neural representations for unknown dynamics
Data assimilation is crucial in a wide range of applications, but it oft...
share
research
∙
08/22/2023
Machine learning assisted exploration for affine Deligne-Lusztig varieties
This paper presents a novel, interdisciplinary study that leverages a Ma...
research
∙
08/02/2023
PROV-IO+: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems
Data provenance, or data lineage, describes the life cycle of data. In s...
research
∙
08/01/2023
Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in whi...
research
∙
07/26/2023
Learning to simulate partially known spatio-temporal dynamics with trainable difference operators
Recently, using neural networks to simulate spatio-temporal dynamics has...
research
∙
07/10/2023
Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans
Gastric cancer is the third leading cause of cancer-related mortality wo...
research
∙
06/26/2023
Analysis of the Decoder Width for Parametric Partial Differential Equations
Recently, Meta-Auto-Decoder (MAD) was proposed as a novel reduced order ...
research
∙
05/29/2023
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans
**Background:** Accurate 3D CT scan segmentation of gastric tumors is pi...
research
∙
05/25/2023
Double Descent of Discrepancy: A Task-, Data-, and Model-Agnostic Phenomenon
In this paper, we studied two identically-trained neural networks (i.e. ...
research
∙
04/17/2023
Unsupervised Image Denoising with Score Function
Though achieving excellent performance in some cases, current unsupervis...
research
∙
04/01/2023
Devil is in the Queries: Advancing Mask Transformers for Real-world Medical Image Segmentation and Out-of-Distribution Localization
Real-world medical image segmentation has tremendous long-tailed complex...
research
∙
03/07/2023
A Comparative Study of Deep Learning and Iterative Algorithms for Joint Channel Estimation and Signal Detection
Joint channel estimation and signal detection (JCESD) is crucial in wire...
research
∙
02/18/2023
Solving Boltzmann equation with neural sparse representation
We consider the neural sparse representation to solve Boltzmann equation...
research
∙
02/16/2023
Meta-Auto-Decoder: A Meta-Learning Based Reduced Order Model for Solving Parametric Partial Differential Equations
Many important problems in science and engineering require solving the s...
research
∙
02/05/2023
Diffusion Model for Generative Image Denoising
In supervised learning for image denoising, usually the paired clean ima...
research
∙
12/05/2022
L2SR: Learning to Sample and Reconstruct for Accelerated MRI
Accelerated MRI aims to find a pair of samplers and reconstructors to re...
research
∙
05/17/2022
Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation
As one of the most challenging and practical segmentation tasks, open-wo...
research
∙
05/16/2022
A scalable deep learning approach for solving high-dimensional dynamic optimal transport
The dynamic formulation of optimal transport has attracted growing inter...
research
∙
02/24/2022
A Note on Machine Learning Approach for Computational Imaging
Computational imaging has been playing a vital role in the development o...
research
∙
02/08/2022
Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer
We proved that a trained model in supervised deep learning minimizes the...
research
∙
12/10/2021
Layer-Parallel Training of Residual Networks with Auxiliary-Variable Networks
Gradient-based methods for the distributed training of residual networks...
research
∙
12/09/2021
Implicit Feature Refinement for Instance Segmentation
We propose a novel implicit feature refinement module for high-quality i...
research
∙
10/07/2021
Learning invariance preserving moment closure model for Boltzmann-BGK equation
As one of the main governing equations in kinetic theory, the Boltzmann ...
research
∙
09/29/2021
Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training
This paper proposes an unsupervised cross-modality domain adaptation app...
research
∙
07/19/2021
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer...
research
∙
06/04/2021
SOLQ: Segmenting Objects by Learning Queries
In this paper, we propose an end-to-end framework for instance segmentat...
research
∙
05/07/2021
MOTR: End-to-End Multiple-Object Tracking with TRansformer
The key challenge in multiple-object tracking (MOT) task is temporal mod...
research
∙
03/09/2021
Generalizable Limited-Angle CT Reconstruction via Sinogram Extrapolation
Computed tomography (CT) reconstruction from X-ray projections acquired ...
research
∙
11/30/2020
Deep Interactive Denoiser (DID) for X-Ray Computed Tomography
Low dose computed tomography (LDCT) is desirable for both diagnostic ima...
research
∙
11/30/2020
Feature Space Singularity for Out-of-Distribution Detection
Out-of-Distribution (OoD) detection is important for building safe artif...
research
∙
10/27/2020
Meta-MgNet: Meta Multigrid Networks for Solving Parameterized Partial Differential Equations
This paper studies numerical solutions for parameterized partial differe...
research
∙
09/03/2020
Penalty and Augmented Lagrangian Methods for Layer-parallel Training of Residual Networks
Algorithms for training residual networks (ResNets) typically require fo...
research
∙
07/24/2020
Transferred Discrepancy: Quantifying the Difference Between Representations
Understanding what information neural networks capture is an essential p...
research
∙
06/03/2020
Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging
Computed Tomography (CT) takes X-ray measurements on the subjects to rec...
research
∙
06/03/2020
RODE-Net: Learning Ordinary Differential Equations with Randomness from Data
Random ordinary differential equations (RODEs), i.e. ODEs with random pa...
research
∙
05/30/2020
MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction
X-ray Computed Tomography (CT) is widely used in clinical applications s...
research
∙
05/19/2020
Enhancing Certified Robustness of Smoothed Classifiers via Weighted Model Ensembling
Randomized smoothing has achieved state-of-the-art certified robustness ...
research
∙
04/10/2020
Blind Adversarial Training: Balance Accuracy and Robustness
Adversarial training (AT) aims to improve the robustness of deep learnin...
research
∙
10/02/2019
Distillation ≈ Early Stopping? Harvesting Dark Knowledge Utilizing Anisotropic Information Retrieval For Overparameterized Neural Network
Distillation is a method to transfer knowledge from one model to another...
research
∙
07/26/2019
Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images
Segmenting coronary arteries is challenging, as classic unsupervised met...
research
∙
06/23/2019
A Review on Deep Learning in Medical Image Reconstruction
Medical imaging is crucial in modern clinics to guide the diagnosis and ...
research
∙
06/06/2019
Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View
The Transformer architecture is widely used in natural language processi...
research
∙
05/29/2019
NPTC-net: Narrow-Band Parallel Transport Convolutional Neural Network on Point Clouds
Convolution plays a crucial role in various applications in signal and i...
research
∙
05/27/2019
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning
Conservation laws are considered to be fundamental laws of nature. It ha...
research
∙
05/02/2019
You Only Propagate Once: Accelerating Adversarial Training Using Maximal Principle
Deep learning achieves state-of-the-art results in many areas. However r...
research
∙
05/02/2019
You Only Propagate Once: Painless Adversarial Training Using Maximal Principle
Deep learning achieves state-of-the-art results in many areas. However r...
research
∙
01/28/2019
CURE: Curvature Regularization For Missing Data Recovery
Missing data recovery is an important and yet challenging problem in ima...
research
∙
12/03/2018
JSR-Net: A Deep Network for Joint Spatial-Radon Domain CT Reconstruction from incomplete data
CT image reconstruction from incomplete data, such as sparse views and l...
research
∙
11/30/2018
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network
Partial differential equations (PDEs) are commonly derived based on empi...
research
∙
05/31/2018