Search Results for author:
Found 30 papers, 6 papers with code
Enhanced Sentence Alignment Network for Efficient Short Text Matching
Cross-sentence attention has been widely applied in text matching, in which model learns the aligned information between two intermediate sequence representations to capture their semantic relationship.
Towards performant and reliable undersampled MR reconstruction via diffusion model sampling
Magnetic Resonance (MR) image reconstruction from under-sampled acquisition promises faster scanning time.
Undersampled MRI Reconstruction with Side Information-Guided Normalisation
In this paper, we investigate the use of such side information as normalisation parameters in a convolutional neural network (CNN) to improve undersampled MRI reconstruction.
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet
Given the recent success of deep learning in medical image segmentation, Neural Architecture Search (NAS) has been introduced to find high-performance 3D segmentation network architectures.
RSBNet: One-Shot Neural Architecture Search for A Backbone Network in Remote Sensing Image Recognition
Recently, a massive number of deep learning based approaches have been successfully applied to various remote sensing image (RSI) recognition tasks.
GAN-based disentanglement learning for chest X-ray rib suppression
Clinical evidence has shown that rib-suppressed chest X-rays (CXRs) can improve the reliability of pulmonary disease diagnosis.
Inverse Aerodynamic Design of Gas Turbine Blades using Probabilistic Machine Learning
One of the critical components in Industrial Gas Turbines (IGT) is the turbine blade.
U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction
Recently, both supervised and unsupervised deep learning methods have been widely applied on the CT metal artifact reduction (MAR) task.
XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors
A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane.
3D-Aware Image Synthesis
Bone Suppression From Dual Energy Chest X-Rays
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A Multi-Task Incremental Learning Framework with Category Name Embedding for Aspect-Category Sentiment Analysis
In this paper, to make multi-task learning feasible for incremental learning, we proposed Category Name Embedding network (CNE-net).
Portfolio Optimization on Multivariate Regime Switching GARCH Model with Normal Tempered Stable Innovation
We propose a Markov regime switching GARCH model with multivariate normal tempered stable innovation to accommodate fat tails and other stylized facts in returns of financial assets.
SAINT: Spatially Aware Interpolation NeTwork for Medical Slice Synthesis
Deep learning-based single image super-resolution (SISR) methods face various challenges when applied to 3D medical volumetric data (i. e., CT and MR images) due to the high memory cost and anisotropic resolution, which adversely affect their performance.
Ellipse R-CNN: Learning to Infer Elliptical Object from Clustering and Occlusion
We first propose a robust and compact ellipse regression based on the Mask R-CNN architecture for elliptical object detection.
Entanglement and Confinement in Coupled Quantum Systems
We also consider the coupled gauged matrix model and vector model, and argue that the deconfinement is associated with the loss of the entanglement, similarly to the previous observation for the coupled SYK model.
High Energy Physics - Theory Strongly Correlated Electrons
Idle Time Optimization for Target Assignment and Path Finding in Sortation Centers
In this paper, we study the one-shot and lifelong versions of the Target Assignment and Path Finding problem in automated sortation centers, where each agent needs to constantly assign itself a sorting station, move to its assigned station without colliding with obstacles or other agents, wait in the queue of that station to obtain a parcel for delivery, and then deliver the parcel to a sorting bin.
Unsupervised Many-to-Many Image-to-Image Translation Across Multiple Domains
To improve the image quality, we propose an effective many-to-many mapping framework for unsupervised multi-domain image-to-image translation.
Potential Field: Interpretable and Unified Representation for Trajectory Prediction
From the generated potential fields, we further estimate future motion direction and speed, which are modeled as Gaussian distributions to account for the multi-modal nature of the problem.
DG-GAN: the GAN with the duality gap
Generative Adversarial Networks (GANs) are powerful, but difficult to understand and train because GANs is a min-max problem.
Map as The Hidden Sensor: Fast Odometry-Based Global Localization
The resulting map-corrected odometry localization is able to provide an accurate belief tensor of the robot state.
Towards multi-sequence MR image recovery from undersampled k-space data
Undersampled MR image recovery has been widely studied for accelerated MR acquisition.
Deep Slice Interpolation via Marginal Super-Resolution, Fusion and Refinement
We propose a marginal super-resolution (MSR) approach based on 2D convolutional neural networks (CNNs) for interpolating an anisotropic brain magnetic resonance scan along the highly under-sampled direction, which is assumed to axial without loss of generality.
DuDoNet: Dual Domain Network for CT Metal Artifact Reduction
The linkage between the sigogram and image domains is a novel Radon inversion layer that allows the gradients to back-propagate from the image domain to the sinogram domain during training.
Generative Tensor Network Classification Model for Supervised Machine Learning
By investigating the distances in the many-body Hilbert space, we find that (a) the samples are naturally clustering in such a space; and (b) bounding the bond dimensions of the TN's to finite values corresponds to removing redundant information in the image recognition.
Quantum simulation for thermodynamics of infinite-size many-body systems by O(10) sites
In this work we propose to simulate many-body thermodynamics of infinite-size quantum lattice models in one, two, and three dimensions, in terms of few-body models of only O(10) sites, which we coin as quantum entanglement simulators (QES's).
Strongly Correlated Electrons Computational Physics Quantum Physics
Adaptive View Planning for Aerial 3D Reconstruction
We then present (i)~a method that builds a view manifold for view selection, and (ii) an algorithm to select a sparse set of views.
Machine Learning by Two-Dimensional Hierarchical Tensor Networks: A Quantum Information Theoretic Perspective on Deep Architectures
The resemblance between the methods used in studying quantum-many body physics and in machine learning has drawn considerable attention.
Machine Learning by Unitary Tensor Network of Hierarchical Tree Structure
We study the quantum features of the TN states, including quantum entanglement and fidelity.
Review of Tensor Network Contraction Approaches
One goal is to provide a systematic introduction of TN contraction algorithms (motivations, implementations, relations, implications, etc.
Computational Physics Statistical Mechanics Strongly Correlated Electrons Applied Physics Quantum Physics
A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning
For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility.
View Selection with Geometric Uncertainty Modeling
Consider a world point $g \in \mathcal{G}$ and its worst case reconstruction uncertainty $\varepsilon(g,\mathcal{S})$ obtained by merging \emph{all} possible views of $g$ chosen from $\mathcal{S}$.
