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Found 17 papers, 4 papers with code
Learning Progressive Joint Propagation for Human Motion Prediction
Last, in order to incorporate a general motion space for high-quality prediction, we build a memory-based dictionary, which aims to preserve the global motion patterns in training data to guide the predictions.
Hand-Transformer: Non-Autoregressive Structured Modeling for 3D Hand Pose Estimation
To address this issue, we connect this structured output learning problem with the structured modeling framework in sequence transduction field.
Accurate and scalable exchange-correlation with deep learning
Skala achieves chemical accuracy for atomization energies of small molecules while retaining the computational efficiency typical of semi-local DFT.
Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems
In this study, we generate a substantially larger training set (PubChemQH) than used previously and use it to create a scalable model for DFT calculations with physical accuracy.
Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
Beyond Hamiltonian prediction, the proposed sparsification techniques also hold significant potential for improving the efficiency and scalability of other SE(3) equivariant networks, further broadening their applicability and impact.
E2Former: A Linear-time Efficient and Equivariant Transformer for Scalable Molecular Modeling
Equivariant Graph Neural Networks (EGNNs) have demonstrated significant success in modeling microscale systems, including those in chemistry, biology and materials science.
Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning
To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm.
Masked adversarial neural network for cell type deconvolution in spatial transcriptomics
MACD employs adversarial learning to align real ST data with simulated ST data generated from scRNA-seq data.
Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models
In this study, we introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep Equilibrium Models (DEQs) for predicting Density Functional Theory (DFT) Hamiltonians.
High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detection
In this study, the structural problems of the YOLOv5 model were analyzed emphatically.
Neural Voting Field for Camera-Space 3D Hand Pose Estimation
We present a unified framework for camera-space 3D hand pose estimation from a single RGB image based on 3D implicit representation.
YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidification
Maintaining inference speeds remarkably similar to those of the YOLOv5 model, the large, medium, and small YOLOCS models surpass the YOLOv5 model's AP by 1. 1%, 2. 3%, and 5. 2%, respectively.
Neural Correspondence Field for Object Pose Estimation
Unlike classical correspondence-based methods which predict 3D object coordinates at pixels of the input image, the proposed method predicts 3D object coordinates at 3D query points sampled in the camera frustum.
YOLOSA: Object detection based on 2D local feature superimposed self-attention
Applying an attention module here can effectively improve the detection accuracy of the model.
AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation
As a specific semantic segmentation task, aerial imagery segmentation has been widely employed in high spatial resolution (HSR) remote sensing images understanding.
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting
How to discover the useful implicit relation between entities and effectively utilize the relations for each entity under various circumstances is crucial.
Magnon-mediated interlayer coupling in an all-antiferromagnetic junction
The interlayer coupling mediated by fermions in ferromagnets brings about parallel and anti-parallel magnetization orientations of two magnetic layers, resulting in the giant magnetoresistance, which forms the foundation in spintronics and accelerates the development of information technology.
Materials Science
