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Found 46 papers, 8 papers with code
Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges
With recent missions such as advanced space-based observatories like the Solar Dynamics Observatory (SDO) and Parker Solar Probe, and ground-based telescopes like the Daniel K. Inouye Solar Telescope (DKIST), the volume, velocity, and variety of data have made solar physics enter a transformative era as solar physics big data (SPBD).
Verifiable cloud-based variational quantum algorithms
Variational quantum algorithms (VQAs) have shown potential for quantum advantage with noisy intermediate-scale quantum (NISQ) devices for quantum machine learning (QML).
Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models
We present Wideband back-projection diffusion, an end-to-end probabilistic framework for approximating the posterior distribution induced by the inverse scattering map from wideband scattering data.
ChatSchema: A pipeline of extracting structured information with Large Multimodal Models based on schema
For the result of value extraction based on correct key extraction, the overall accuracy was 97. 2%, precision was 95. 8%, recall was 95. 8%, and F1-score was 95. 8%.
Optical Character Recognition
Optical Character Recognition (OCR)
CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI
To the best of our knowledge, the CMRxRecon2024 dataset is the largest and most diverse publicly available cardiac k-space dataset.
An Empirical Study on the Fairness of Foundation Models for Multi-Organ Image Segmentation
We test state-of-the-art foundation models for medical image segmentation, including the original SAM, medical SAM and SAT models, to evaluate segmentation efficacy across different demographic groups and identify disparities.
Forward-Euler time-discretization for Wasserstein gradient flows can be wrong
In this note, we examine the forward-Euler discretization for simulating Wasserstein gradient flows.
Global-local Fourier Neural Operator for Accelerating Coronal Magnetic Field Model
The goal of this study is to accelerate coronal magnetic field simulation using deep learning, specifically, the Fourier Neural Operator (FNO).
High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning
However, current attention-based models may overlook the transferability of visual features and the distinctiveness of attribute localization when learning regional features in images.
AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning
Computational prediction of stable crystal structures has a profound impact on the large-scale discovery of novel functional materials.
The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023
To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network
Image super-resolution has been an important subject in image processing and recognition.
Beyond MOT: Semantic Multi-Object Tracking
Current multi-object tracking (MOT) aims to predict trajectories of targets (i. e., ''where'') in videos.
A Good Score Does not Lead to A Good Generative Model
In particular, it has been shown that SGMs can generate samples from a distribution that is close to the ground-truth if the underlying score function is learned well, suggesting the success of SGM as a generative model.
IEKM: A Model Incorporating External Keyword Matrices
In this paper, we propose an incorporation external keywords matrices model (IEKM) to address these challenges.
Accelerating optimization over the space of probability measures
The acceleration of gradient-based optimization methods is a subject of significant practical and theoretical importance, particularly within machine learning applications.
Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension
With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning.
Beyond expectations: Residual Dynamic Mode Decomposition and Variance for Stochastic Dynamical Systems
Finally, we present a suite of convergence results for the spectral information of stochastic Koopman operators.
MD-HIT: Machine learning for materials property prediction with dataset redundancy control
This issue is well known in the field of bioinformatics for protein function prediction, in which a redundancy reduction procedure (CD-Hit) is always applied to reduce the sample redundancy by ensuring no pair of samples has a sequence similarity greater than a given threshold.
Correcting auto-differentiation in neural-ODE training
Does the use of auto-differentiation yield reasonable updates to deep neural networks that represent neural ODEs?
A 3D grain-based reconstruction method from a 2D surface image for the Distinct Lattice Spring Model
The 3D GBM reconstruction is generated by a simulated annealing algorithm, with the Monte Carlo algorithm extending the calculation of the two-point probability function as the target function to the random particle model.
Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition
This paper proposes reconstructing the binary adjacency matrix via tensor decomposition, and a traffic flow forecasting method is proposed.
A Deep Learning Approach to Generating Photospheric Vector Magnetograms of Solar Active Regions for SOHO/MDI Using SDO/HMI and BBSO Data
We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data.
Inferring Line-of-Sight Velocities and Doppler Widths from Stokes Profiles of GST/NIRIS Using Stacked Deep Neural Networks
Obtaining high-quality magnetic and velocity fields through Stokes inversion is crucial in solar physics.
Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine Learning Methods
In addition, we sort the importance of SHARP parameters by Borda Count method calculated from the ranks that are rendered by 9 different machine learning methods.
Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network
Solar energetic particles (SEPs) are an essential source of space radiation, which are hazards for humans in space, spacecraft, and technology in general.
On the Global Convergence of Gradient Descent for multi-layer ResNets in the mean-field regime
Finding the optimal configuration of parameters in ResNet is a nonconvex minimization problem, but first-order methods nevertheless find the global optimum in the overparameterized regime.
Effective and Efficient Graph Learning for Multi-view Clustering
Despite the impressive clustering performance and efficiency in characterizing both the relationship between data and cluster structure, existing graph-based multi-view clustering methods still have the following drawbacks.
Tracing Halpha Fibrils through Bayesian Deep Learning
Our method consists of a data pre-processing component that prepares training data from a threshold-based tool, a deep learning model implemented as a Bayesian convolutional neural network for probabilistic image segmentation with uncertainty quantification to predict fibrils, and a post-processing component containing a fibril-fitting algorithm to determine fibril orientations.
Overparameterization of deep ResNet: zero loss and mean-field analysis
Finding parameters in a deep neural network (NN) that fit training data is a nonconvex optimization problem, but a basic first-order optimization method (gradient descent) finds a global optimizer with perfect fit (zero-loss) in many practical situations.
A simple and robust method for noise variance estimation for time-varying signals
In this brief paper, we present a simple approach to estimate the variance of measurement noise with time-varying 1-D signals.
Improving speech recognition models with small samples for air traffic control systems
By virtue of the common terminology used in the ATC domain, the transfer learning task can be regarded as a sub-domain adaption task, in which the transferred model is optimized using a joint corpus consisting of baseline samples and new transcribed samples from the target dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Constrained Ensemble Langevin Monte Carlo
In particular, we find that if one directly surrogates the gradient using the ensemble approximation, the algorithm, termed Ensemble Langevin Monte Carlo, is unstable due to a high variance term.
I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling
Reconstructing a high-precision and high-fidelity 3D human hand from a color image plays a central role in replicating a realistic virtual hand in human-computer interaction and virtual reality applications.
Ranked #11 on
3D Hand Pose Estimation
on HO-3D v2
Random Coordinate Underdamped Langevin Monte Carlo
We investigate the computational complexity of RC-ULMC and compare it with the classical ULMC for strongly log-concave probability distributions.
Random Coordinate Langevin Monte Carlo
We investigate the total complexity of RC-LMC and compare it with the classical LMC for log-concave probability distributions.
Langevin Monte Carlo: random coordinate descent and variance reduction
However, the method requires the evaluation of a full gradient in each iteration, and for a problem on $\mathbb{R}^d$, this amounts to $d$ times partial derivative evaluations per iteration.
PAC Model Checking of Black-Box Continuous-Time Dynamical Systems
In this paper we present a novel model checking approach to finite-time safety verification of black-box continuous-time dynamical systems within the framework of probably approximately correct (PAC) learning.
Systems and Control Formal Languages and Automata Theory Systems and Control
Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations
The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists.
A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics and Benchmark Datasets
Machine Reading Comprehension (MRC) is a challenging Natural Language Processing(NLP) research field with wide real-world applications.
Variance reduction for Random Coordinate Descent-Langevin Monte Carlo
The high variance induced by the randomness means a larger number of iterations are needed, and this balances out the saving in each iteration.
Non-recurrent Traffic Congestion Detection with a Coupled Scalable Bayesian Robust Tensor Factorization Model
However, it remains a challenging task to construct an analytical framework through which the natural spatial-temporal structural properties of multivariable traffic information can be effectively represented and exploited to better understand and detect NRTC.
Classical limit for the varying-mass Schrödinger equation with random inhomogeneities
The varying-mass Schr\"odinger equation (VMSE) has been successfully applied to model electronic properties of semiconductor hetero-structures, for example, quantum dots and quantum wells.
Numerical Analysis Numerical Analysis
Error Lower Bounds of Constant Step-size Stochastic Gradient Descent
To our knowledge, this is the first analysis for SGD error lower bound without the strong convexity assumption.
A sparse decomposition of low rank symmetric positive semi-definite matrices
In this paper, we partition the indices from 1 to $N$ into several patches and propose to quantify the sparseness of a vector by the number of patches on which it is nonzero, which is called patch-wise sparseness.
Numerical Analysis
