A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improved few-shot visual reasoning with image preprocessing.
Enhances diffusion models by preprocessing data to improve reconstruction quality.
Paper proposes protecting DNN models with secret key preprocessing.
New system tracks musical performances in raw sheet images without preprocessing.
Recently, deep neural networks have demonstrated excellent performances in recognizing the age and gender on human face images. However, these models were applied in a black-box manner with no information provided about which facial features are actually used for prediction and how these features depend on image prepro…
MobileNet CNN achieves high accuracy in skin disease classification on Android.
Recently the GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to guarantee the credibility of visual contents. Although researchers have develo…
Deep networks have achieved huge successes in application domains like object and face recognition. The performance gain is attributed to different facets of the network architecture such as: depth of the convolutional layers, activation function, pooling, batch normalization, forward and back propagation and many more…
A framework uses preprocessing to improve psychiatric questionnaire predictions while maintaining interpretability.
Unsupervised anomaly detection aids doctors in evaluating X-ray images of hands.
In the United States, heart disease is the leading cause of death for both men and women, accounting for 610,000 deaths each year [1]. Physicians use Magnetic Resonance Imaging (MRI) scans to take images of the heart in order to non-invasively estimate its structural and functional parameters for cardiovascular diagnos…
TorchIO simplifies medical image processing for deep learning.
The paper presents the application of Variational Autoencoders (VAE) for data dimensionality reduction and explorative analysis of mass spectrometry imaging data (MSI). The results confirm that VAEs are capable of detecting the patterns associated with the different tissue sub-types with performance than standard appro…
Novel framework for whole-slide blood cell segmentation.
GANs generate realistic financial data for research.
As Super-Resolution (SR) has matured as a research topic, it has been applied to additional topics beyond image reconstruction. In particular, combining classification or object detection tasks with a super-resolution preprocessing stage has yielded improvements in accuracy especially with objects that are small relati…
The paper simplifies quickshift hyperparameter tuning for larger images.
This study automates blood cell classification using computer vision.
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of AD. However, they are difficult to reproduce because key components of the validation are often not readily available. These components include selected participants and input data, image prepr…
DELIMIT is a framework extension for deep learning in diffusion imaging, which extends the basic framework PyTorch towards spherical signals. Based on several novel layers, deep learning can be applied to spherical diffusion imaging data in a very convenient way. First, two spherical harmonic interpolation layers are a…
Improved disentanglement in VAEs using aggregated feature maps.
Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning because it automatically extracts meaningful features through a sparse and part-based representation. However, NMF has the drawback of being highly ill-posed, that is, there typically exist many different but equivalent facto…
CNNs improve InSAR image denoising and coherence estimation.
Data preprocessing techniques are devoted to correct or alleviate errors in data. Discretization and feature selection are two of the most extended data preprocessing techniques. Although we can find many proposals for static Big Data preprocessing, there is little research devoted to the continuous Big Data problem. A…
mlr3torch simplifies deep learning in R for tabular and image data.
SaR-SVM-STV improves hyperspectral image classification with shape-adaptive reconstruction and denoising.
SVMs improve forest fire detection accuracy on challenging datasets.
Automated trading system with preprocessing and reinforcement learning.
Deep neural networks are vulnerable to adversarial attacks. The literature is rich with algorithms that can easily craft successful adversarial examples. In contrast, the performance of defense techniques still lags behind. This paper proposes ME-Net, a defense method that leverages matrix estimation (ME). In ME-Net, i…
Photometric stereo is a method for estimating the normal vectors of an object from images of the object under varying lighting conditions. Motivated by several recent works that extend photometric stereo to more general objects and lighting conditions, we study a new robust approach to photometric stereo that utilizes …
Spectral methods improve parameter estimation in structured GLMs.
Enhances solar flare prediction with advanced preprocessing and contrastive learning.
TensorShield defends images from adversarial attacks using tensor decomposition.
This paper uses feature preprocessing and RRL to automate profitable financial trading.
This study compares different data preprocessing methods for gait analysis.
Proposes a new feature preprocessing method using kernel density integral transformation.
We present a new method for uncertainty estimation and out-of-distribution detection in neural networks with softmax output. We extend softmax layer with an additional constant input. The corresponding additional output is able to represent the uncertainty of the network. The proposed method requires neither additional…
Two preprocessing techniques reduce neural network training cost.
We explore several oversampling techniques for an imbalanced multi-label classification problem, a setting often encountered when developing models for Computer-Aided Diagnosis (CADx) systems. While most CADx systems aim to optimize classifiers for overall accuracy without considering the relative distribution of each …
New method speeds up neural network training by preprocessing weight-data correlation.
Data mining is about obtaining new knowledge from existing datasets. However, the data in the existing datasets can be scattered, noisy, and even incomplete. Although lots of effort is spent on developing or fine-tuning data mining models to make them more robust to the noise of the input data, their qualities still st…
Image segmentation is widely used in a variety of computer vision tasks, such as object localization and recognition, boundary detection, and medical imaging. This thesis proposes deep learning architectures to improve automatic object localization and boundary delineation for salient object segmentation in natural ima…
Paper presents a data preprocessing method for PHM models.
The paper uses VGG-19 for plant species classification from leaf images.
Reliable microaneurysm detection in digital fundus images is still an open issue in medical image processing. We propose an ensemble-based framework to improve microaneurysm detection. Unlike the well-known approach of considering the output of multiple classifiers, we propose a combination of internal components of mi…
3D CNNs interpret brain MRI differences between men and women.
RODMAN improves ML-based disk failure prediction accuracy in cloud environments.
Class-imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers have been…