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Found 12 papers, 4 papers with code
Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing
Spectral unmixing is a significant challenge in hyperspectral image processing.
HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations
Publicly available collections of drug-like molecules have grown to comprise 10s of billions of possibilities in recent history due to advances in chemical synthesis.
Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT
Specifically, in this study we transform the cutting-edge Denoising Diffusion Probabilistic Model (DDPM) into a parallel framework for sub-volume-based sparse-view breast CT image reconstruction in projection and image domains.
Evaluating Point-Prediction Uncertainties in Neural Networks for Drug Discovery
In this paper, we examine UQ methods that estimate different sources of predictive uncertainty for NN models aiming at drug discovery.
(Fusionformer):Exploiting the Joint Motion Synergy with Fusion Network Based On Transformer for 3D Human Pose Estimation
For the current 3D human pose estimation task, a group of methods mainly learn the rules of 2D-3D projection from spatial and temporal correlation.
Ranked #9 on
3D Human Pose Estimation
on MPI-INF-3DHP
Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition
And then a spatio-temporal tuples self-attention module is proposed to capture the relationship of different joints in consecutive frames.
High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models
Structure-based Deep Fusion models were recently shown to outperform several physics- and machine learning-based protein-ligand binding affinity prediction methods.
Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference
We present fusion models to benefit from different feature representations of two neural network models to improve the binding affinity prediction.
Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction
Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences.
Deep-learning-based Breast CT for Radiation Dose Reduction
In this study, we propose a deep-learning-based method to establish a residual neural network model for the image reconstruction, which is applied for few-view breast CT to produce high quality breast CT images.
Dual Network Architecture for Few-view CT -- Trained on ImageNet Data and Transferred for Medical Imaging
Few-view CT image reconstruction is an important topic to reduce the radiation dose.
A high-order compact finite difference scheme and precise integration method based on modified Hopf-Cole transformation for numerical simulation of n-dimensional Burgers' system
This paper modifies a n-dimensional Hopf-Cole transformation to the n-dimensional Burgers' system.
Numerical Analysis Numerical Analysis
