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.
Proposes PA-DSL for correcting noisy human labels in automated data labeling.
Automated machine learning (AutoML) has received increasing attention in the recent past. While the main tools for AutoML, such as Auto-WEKA, TPOT, and auto-sklearn, mainly deal with single-label classification and regression, there is very little work on other types of machine learning tasks. In particular, there is a…
AutoWS-Bench-101 evaluates automated weak supervision methods for diverse domains.
Paper compares NMF and LDA for topic labeling in customer communications.
Annotating automotive radar data is a difficult task. This article presents an automated way of acquiring data labels which uses a highly accurate and portable global navigation satellite system (GNSS). The proposed system is discussed besides a revision of other label acquisitions techniques and a problem description …
Automated labeling of intracranial arteries improves accuracy and efficiency.
Automates galaxy morphology classification with less human labelling.
Paper tackles noisy label learning by exploiting memorization effect.
Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. …
Automated graphics testing detects novel corruptions without manual labeling.
Bayesian method improves deep learning for noisy EEG seizure detection.
Semi-supervised learning chain boosts histology image accuracy with minimal labeled data.
BERT-XML automates ICD coding from EHR notes using BERT pretraining.
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 …
System uses conformal prediction to help experts make accurate decisions without understanding when to trust it.
Increasing numbers of software vulnerabilities are discovered every year whether they are reported publicly or discovered internally in proprietary code. These vulnerabilities can pose serious risk of exploit and result in system compromise, information leaks, or denial of service. We leveraged the wealth of C and C++ …
Automated melodic phrase detection and segmentation is a classical task in content-based music information retrieval and also the key towards automated music structure analysis. However, traditional methods still cannot satisfy practical requirements. In this paper, we explore and adapt various neural network architect…
Painless Activation Steering automates post-training for LMs without manual intervention.
Build accurate DNN models requires training on large labeled, context specific datasets, especially those matching the target scenario. We believe advances in wireless localization, working in unison with cameras, can produce automated annotation of targets on images and videos captured in the wild. Using pedestrian an…
AutoBayes automates Bayesian graph exploration for robust machine learning.
Study examines how uncertainty visualization affects analyst trust in automated classification systems.
Work shows hallucination detection by LLMs is impossible without expert feedback.
The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and relatively low cost measure of mesoscale brain dynamics with high temporal resolution. Although signals recorded in parallel by multiple, near-adjacent EEG scalp electrode channels are highly-correlated and combine signals from many diff…
Deep learning automates biofouling detection in ship hull images.
This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is like…
Entity Linking (EL) is the task of automatically identifying entity mentions in a piece of text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. There is a large number of EL tools available for different types of documents and domains, yet EL remains a challenging task where t…
Statistical analysis (SA) is a complex process to deduce population properties from analysis of data. It usually takes a well-trained analyst to successfully perform SA, and it becomes extremely challenging to apply SA to big data applications. We propose to use deep neural networks to automate the SA process. In parti…
DeepCap automates coronary artery segmentation from IVOCT images.
Motivated by the problem of automated repair of software vulnerabilities, we propose an adversarial learning approach that maps from one discrete source domain to another target domain without requiring paired labeled examples or source and target domains to be bijections. We demonstrate that the proposed adversarial l…
Automated model tracks mouse behavior in home cages.
We explore solutions for automated labeling of content in bug trackers and customer support systems. In order to do that, we classify content in terms of several criteria, such as priority or product area. In the first part of the paper, we provide an overview of existing methods used for text classification. These met…
Study improves pollen detection in optical and holographic images using deep learning.
The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist …
Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full auto…
Improves medical note processing by training model on related concepts and global context.
Learning with auxiliary tasks can improve the ability of a primary task to generalise. However, this comes at the cost of manually labelling auxiliary data. We propose a new method which automatically learns appropriate labels for an auxiliary task, such that any supervised learning task can be improved without requiri…
Reduces test set maintenance effort by 80-100%.
Simple methods boost sound event classifier accuracy by 2.5%.
Automated quality control for seismic data reduces human labor and time.
Study evaluates graph-based semi-supervised learning under noisy label conditions.
This paper automates tagging programming challenge descriptions.
This study analyzes financial equity research reports to identify frequently asked questions and automates 80% of them.
This paper studies the problem of stance detection which aims to predict the perspective (or stance) of a given document with respect to a given claim. Stance detection is a major component of automated fact checking. As annotating stances in different domains is a tedious and costly task, automatic methods based on ma…
This paper evaluates a method to improve representations using incomplete external evidence across tasks.
A drift detection method for large datasets without labels.
Framework harmonizes EHR data across institutions for better analysis.
Paper tackles label noise in large datasets, purifying noisy data with a nonparametric framework.
Unified scoring model improves efficiency and performance across multiple tasks.