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.
Automate residual plot assessment with R package and Shiny application
C-FAR automates clustering assessment for neural tracking.
Automated rock fragmentation assessment using deep learning and spatial statistics.
Automated fatigue assessment using ECG and actigraphy sensors.
Computer vision model automates residual plot assessment for diagnosing model assumptions.
Framework for automatically assessing and correcting data quality issues without domain knowledge.
Automates detecting problem statements in peer assessments.
In this paper, we present a data science automation system called Prediction Factory. The system uses several key automation algorithms to enable data scientists to rapidly develop predictive models and share them with domain experts. To assess the system's impact, we implemented 3 different interfaces for creating pre…
Malaria is a life-threatening disease affecting millions. Microscopy-based assessment of thin blood films is a standard method to (i) determine malaria species and (ii) quantitate high-parasitemia infections. Full automation of malaria microscopy by machine learning (ML) is a challenging task because field-prepared sli…
Perinatal stroke (PS) is a serious condition that, if undetected and thus untreated, often leads to life-long disability, in particular Cerebral Palsy (CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can be used to classify infant movements using a Gestalt approach, identifying infants at high ri…
Recall assistance methods are among the key aspects that improve the accuracy of online dietary assessment surveys. These methods still mainly rely on experience of trained interviewers with nutritional background, but data driven approaches could improve cost-efficiency and scalability of automated dietary assessment.…
Deep learning automates biofouling detection in ship hull images.
Paper uses TDA for automated Parkinson's disease classification and severity assessment.
Review of automation's role in chemical discoveries.
Survey on automating geometry problem solving with large models.
Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage fr…
Challenge aims to develop automated meningioma MRI segmentation models.
DFMM automates market making with adaptive pricing and risk management.
New framework assesses LLM security risks in BFSI.
Dynamic assessment of patient status (e.g. by an automated, continuously updated assessment of outcome) in the Intensive Care Unit (ICU) is of paramount importance for early alerting, decision support and resource allocation. Extraction and cleaning of expert-selected clinical variables discards information and protrac…
The paper examines statistical properties of IL and LVR in automated market makers.
With success on controlled tasks, generative models are being increasingly applied to humanitarian applications [1,2]. In this paper, we focus on the evaluation of a conditional generative model that illustrates the consequences of climate change-induced flooding to encourage public interest and awareness on the issue.…
Shai is a 10B model for asset management tasks, outperforming baselines.
The paper proposes a method to assess when automated predictions are reliable.
This study improves lung tumor segmentation in mice MRI scans with nnU-Net, reducing annotation needs.
Major advances have been made regarding the utilization of artificial intelligence in health care. In particular, deep learning approaches have been successfully applied for automated and assisted disease diagnosis and prognosis based on complex and high-dimensional data. However, despite all justified enthusiasm, over…
RAG-IT automates financial analysis using LLMs and specialized datasets.
Automatically assesses the quality of online health articles.
Model uses smartphone data to assess MS trajectories.
A popular tool for unsupervised modelling and mining multi-aspect data is tensor decomposition. In an exploratory setting, where and no labels or ground truth are available how can we automatically decide how many components to extract? How can we assess the quality of our results, so that a domain expert can factor th…
The AutoML task consists of selecting the proper algorithm in a machine learning portfolio, and its hyperparameter values, in order to deliver the best performance on the dataset at hand. Mosaic, a Monte-Carlo tree search (MCTS) based approach, is presented to handle the AutoML hybrid structural and parametric expensiv…
Study automates detection of visitation disruptions in ICU patients.
A key aspect of automating predictive machine learning entails the capability of properly triggering the update of the trained model. To this aim, suitable automatic solutions to self-assess the prediction quality and the data distribution drift between the original training set and the new data have to be devised. In …
fastml guards against data leakage in automated machine learning.
Study develops a new tool for assessing asphalt pavement conditions using deep learning.
FLAIR measures LP competitiveness in AMMs, improving LP performance evaluations.
Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data captured with a sensory system. Despite the essential role of rehabilitation assessment toward improved patient outcomes and reduced healthc…
Automated scoring engines are increasingly being used to score the free-form text responses that students give to questions. Such engines are not designed to appropriately deal with responses that a human reader would find alarming such as those that indicate an intention to self-harm or harm others, responses that all…
The rapid growth of text data has motivated the development of machine-learning based automatic text summarization strategies that concisely capture the essential ideas in a larger text. This study aimed to devise an extractive summarization method for A-133 Single Audits, which assess if recipients of federal grants a…
Unified scoring model improves efficiency and performance across multiple tasks.
Facial pain expression is an important modality for assessing pain, especially when the patient's verbal ability to communicate is impaired. The facial muscle-based action units (AUs), which are defined by the Facial Action Coding System (FACS), have been widely studied and are highly reliable as a method for detecting…
Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to the lack of feedback from physical therapists (PTs). Here, we analyze the feasibility of using trunk sway data and machine learning (ML) techniques to automatically evaluate balance, providing accurate assessments out…
Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating fr…
Deep-CAPTCHA cracks visual CAPTCHAs using deep learning.
Backtesting framework for CLMMs on Uniswap V3 reduces reward estimation error.
ChemCrow enhances LLMs for chemistry tasks, automating complex chemical processes.
The paper proposes a method to measure fairness through equality of effort using algorithmic recourse.
Multimodal analysis assesses job interview performance and provides feedback.