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.
Soft labeling impacts OOD detection in neural networks.
Convolutional neural network improves assertion detection in multi-label clinical text.
Adaptive sampling detects local concept drift with limited labels.
We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measur…
New method detects money laundering in Bitcoin using minimal labels.
Study benchmarks label noise detection methods, identifying best practices.
Improved OOD detection using label smoothing and k-NN density estimates.
A drift detection method for large datasets without labels.
The development of real-time affect detection models often depends upon obtaining annotated data for supervised learning by employing human experts to label the student data. One open question in annotating affective data for affect detection is whether the labelers (i.e., human experts) need to be socio-culturally sim…
DynaCor detects noisy labels by learning from corrupted training signals.
Detects changes in classifier scores to identify shifts in class priors.
Improves anomaly detection with contaminated unlabeled data.
A new algorithm detects changepoints in labeled and unlabeled data.
New taxonomy reveals different detection limits for various types of fraud.
HMS-BERT detects cyberbullying in multiple languages and labels.
Deep semi-supervised anomaly detection improves fraud detection in financial markets.
Preventing early progression of epilepsy and so the severity of seizures requires an effective diagnosis. Epileptic transients indicate the ability to develop seizures but humans overlook such brief events in an electroencephalogram (EEG) what compromises patient treatment. Traditionally, training of the EEG event dete…
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets. Typically anomaly detection is treated as an unsupervised learning problem. In practice however, one may have---in addition to a large set of unlabeled samples---access to a small pool of label…
Deep RL detects anomalies from few labeled examples and large unlabeled data.
Detects harmful shifts without labels for model performance.
This paper considers a semi-supervised learning framework for weakly labeled polyphonic sound event detection problems for the DCASE 2019 challenge's task4 by combining both the tri-training and adversarial learning. The goal of the task4 is to detect onsets and offsets of multiple sound events in a single audio clip. …
Improves fault detection models in noisy data.
One important assumption underlying common classification models is the stationarity of the data. However, in real-world streaming applications, the data concept indicated by the joint distribution of feature and label is not stationary but drifting over time. Concept drift detection aims to detect such drifts and adap…
Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground truth. Though there exists a large set of reviews online, only a few of them hav…
New algorithm detects community labels in networks using unlabeled data.
Detects drifts in data for classification tasks using constrained embeddings.
FLOPART solves peak detection by creating accurate train and test set predictions.
A novel semi-supervised outlier detection model detects anomalies with few labels.
New approach predicts event probabilities for better event detection.
Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervised algorithms to achieve accurate predictions. The accuracy achieved by top weakly supervised algorithms is still significantly lower than …
Detecting and recovering labels in binomial logistic mixtures is challenging due to an information gap.
TAMA uses LMMs to detect and interpret anomalies in time series data with few labels.
Bayesian method improves deep learning for noisy EEG seizure detection.
We propose a supervised anomaly detection method based on neural density estimators, where the negative log likelihood is used for the anomaly score. Density estimators have been widely used for unsupervised anomaly detection. By the recent advance of deep learning, the density estimation performance has been greatly i…
We propose using five data-driven community detection approaches from social networks to partition the label space for the task of multi-label classification as an alternative to random partitioning into equal subsets as performed by RAkELd: modularity-maximizing fastgreedy and leading eigenvector, infomap, walktrap an…
A new method detects concept drift without true labels.
GWHD dataset offers 4,700 high-res images of wheat heads.
Deep neural networks (DNNs) are powerful tools in computer vision tasks. However, in many realistic scenarios label noise is prevalent in the training images, and overfitting to these noisy labels can significantly harm the generalization performance of DNNs. We propose a novel technique to identify data with noisy lab…
This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models. It is common to assume that the parameters are distinct within each segment. As such, many Bayesian change point detection models do not exploit the segment parameter patterns, whi…
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…
Community detection is one of the fundamental problems of network analysis, for which a number of methods have been proposed. Most model-based or criteria-based methods have to solve an optimization problem over a discrete set of labels to find communities, which is computationally infeasible. Some fast spectral algori…
Paper detects common subtrees with identical labels in trees.
Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and I…
Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the wild can be unreliable due to careless annotations o…
Unsupervised method detects earthquakes from raw waveforms, generalizing across datasets.
Study evaluates predictive uncertainty in malware detection.
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised anomaly detector based on the Autoencoder (AE). There are two main approaches in anomaly detection: supervised and unsupervised. The supervised approach accurately detects the known anomalies included in training data, but it cannot detect the unk…
New method improves false-/true-positive-rate estimation in fraud detection with noisy labels.