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.
Deep ROC analysis improves model selection and interpretation in medical and AI applications.
Receiver operating characteristic (ROC) analysis is widely used for evaluating diagnostic systems. Recent studies have shown that estimating an area under ROC curve (AUC) with standard cross-validation methods suffers from a large bias. The leave-pair-out (LPO) cross-validation has been shown to correct this bias. Howe…
Deep learning uses ROC cost functions to improve virtual screening accuracy.
Throughout science and technology, receiver operating characteristic (ROC) curves and associated area under the curve (AUC) measures constitute powerful tools for assessing the predictive abilities of features, markers and tests in binary classification problems. Despite its immense popularity, ROC analysis has been su…
The study improves the assessment of fairness in face recognition using ROC curves and statistical guarantees.
A scalable ROC-SVM variant reduces training time for imbalanced binary classification.
DCC separates marginal estimation from dependence modeling for improved classification accuracy.
New method tests independence using ROC analysis and bipartite ranking.
Proposes MCC-F1 curve for better binary classification evaluation.
Improves ROC/AUC for multi-class classification.
Neural network models improve ROC curve evaluation of biomarkers, focusing on age's role in physical activity-mortality association.
CP-ROC bands improve graph classification accuracy and uncertainty quantification.
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted sums to combine many aspects of similarity, yielding parameter-free models for virtual screening. In this report, we decompose the ROCS color force field into "color com…
Study evaluates different saliency maps for CT image classification.
Geometric analysis of ROC and PR curves for binary classification.
Novel method for multiclass ROC curves using multidimensional Gini index.
Deep neural networks predict earthquake locations with high accuracy.
The fuzzy ROC extends Receiver Operating Curve (ROC) visualization to the situation where some data points, falling in an indeterminacy region, are not classified. It addresses two challenges: definition of sensitivity and specificity bounds under indeterminacy; and visual summarization of the large number of possibili…
Optimizes partial AUC across various FPRs for machine learning models.
Deep learning improves forensic matching of casings.
The Receiver Operating Characteristic (ROC) curve is a representation of the statistical information discovered in binary classification problems and is a key concept in machine learning and data science. This paper studies the statistical properties of ROC curves and its implication on model selection. We analyze the …
Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The …
This review examines deep learning in financial fraud detection over 5 years.
Paper estimates optimal ROC curve arc length and AUC, improving classification performance.
AUC used as a measure of clustering quality in unsupervised learning.
Modified AUC improves CNN training by considering model confidence.
This paper compares machine and deep learning algorithms for IoT data classification.
New optimization method improves AUC for binary classification and changepoint detection.
ABROCA assesses algorithmic bias, revealing skewed distributions that inflate results.
FROCC uses random projections for fast one-class classification.
Many problems that appear in biomedical decision making, such as diagnosing disease and predicting response to treatment, can be expressed as binary classification problems. The costs of false positives and false negatives vary across application domains and receiver operating characteristic (ROC) curves provide a visu…
Study finds AUC is most consistent across different prevalence in binary classification.
Feature selection aims to select the smallest subset of features for a specified level of performance. The optimal achievable classification performance on a feature subset is summarized by its Receiver Operating Curve (ROC). When infinite data is available, the Neyman- Pearson (NP) design procedure provides the most e…
In Machine Learning as a Service, a provider trains a deep neural network and gives many users access. The hosted (source) model is susceptible to model stealing attacks, where an adversary derives a surrogate model from API access to the source model. For post hoc detection of such attacks, the provider needs a robust…
Improved FDAM algorithms for heterogeneous data with constant communication complexity.
The paper tackles fairness in scoring functions for binary classification.
Study evaluates data augmentation methods for prostate cancer detection in MRI.
We developed an automated deep learning system to detect hip fractures from frontal pelvic x-rays, an important and common radiological task. Our system was trained on a decade of clinical x-rays (~53,000 studies) and can be applied to clinical data, automatically excluding inappropriate and technically unsatisfactory …
The variational autoencoder (VAE) is a generative model with continuous latent variables where a pair of probabilistic encoder (bottom-up) and decoder (top-down) is jointly learned by stochastic gradient variational Bayes. We first elaborate Gaussian VAE, approximating the local covariance matrix of the decoder as an o…
Study reveals significant performance flips in GLOD using repurposed graph classification datasets.
Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions. Previous work on deep-learning-based anomaly detection has primarily focused on the reconstruction error. We argue instead, that pixel-wise anomaly ratings derived from a Variation…
We investigate a long-debated question, which is how to create predictive models of recidivism that are sufficiently accurate, transparent, and interpretable to use for decision-making. This question is complicated as these models are used to support different decisions, from sentencing, to determining release on proba…
Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large scale screening is the inability to exhaustively detect fine blood vessels crucial to disease diagnosis. In this work we present a computational ima…
We discovered that past changes in the market correlation structure are significantly related with future changes in the market volatility. By using correlation-based information filtering networks we device a new tool for forecasting the market volatility changes. In particular, we introduce a new measure, the "correl…
Deep learning predicts ICU mortality with enhanced interpretability.
Thousands of security vulnerabilities are discovered in production software each year, either reported publicly to the Common Vulnerabilities and Exposures database or discovered internally in proprietary code. Vulnerabilities often manifest themselves in subtle ways that are not obvious to code reviewers or the develo…
Background: Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to guarantee a minimum level of quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of risk of one-year mortality. Objectives: The main objectiv…
Study compares AI models for stock price prediction using financial news.