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 MCC-F1 curve for better binary classification evaluation.
This paper provides new insight into maximizing F1 scores in the context of binary classification and also in the context of multilabel classification. The harmonic mean of precision and recall, F1 score is widely used to measure the success of a binary classifier when one class is rare. Micro average, macro average, a…
Enhances multi-tag classification using low-dimensional vector representations and virtual data.
Multi-language speech datasets are scarce and often have small sample sizes in the medical domain. Robust transfer of linguistic features across languages could improve rates of early diagnosis and therapy for speakers of low-resource languages when detecting health conditions from speech. We utilize out-of-domain, unp…
The 'macro F1' metric is frequently used to evaluate binary, multi-class and multi-label classification problems. Yet, we find that there exist two different formulas to calculate this quantity. In this note, we show that only under rare circumstances the two computations can be considered equivalent. More specifically…
Syndromic surveillance detects and monitors individual and population health indicators through sources such as emergency department records. Automated classification of these records can improve outbreak detection speed and diagnosis accuracy. Current syndromic systems rely on hand-coded keyword-based methods to parse…
Communities in social networks or graphs are sets of well-connected, overlapping vertices. The effectiveness of a community detection algorithm is determined by accuracy in finding the ground-truth communities and ability to scale with the size of the data. In this work, we provide three contributions. First, we show t…
Paper studies Landsberg curvature of a specific Finsler metric.
Given two maps f1 and f2 from the sphere Sm to an n-manifold N, when are they loose, i.e. when can they be deformed away from one another? We study the geometry of their (generic) coincidence locus and its Nielsen decomposition. On the one hand the resulting bordism class of coincidence data and the corresponding Niels…
Modeling human language learning with multi-checkpoint machine translation.
New scoring rules improve probabilistic classification model evaluation.
Paper semantifies bioassay text using neural networks.
This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all…
PCA-Triage optimizes sensor data sampling for IoT networks.
In predictive maintenance, model performance is usually assessed by means of precision, recall, and F1-score. However, employing the model with best performance, e.g. highest F1-score, does not necessarily result in minimum maintenance cost, but can instead lead to additional expenses. Thus, we propose to perform model…
Proposes BA method for unbiased time series anomaly detection evaluation.
Paper classifies Brazilian music genres using song lyrics with BLSTM network.
Gestalt combines two models to improve SQuAD2.0 performance.
New model detects crying in real-world settings with improved accuracy.
DISPR uses diffusion models to predict 3D cell shapes from 2D images.
ART adapts class-wise resampling to improve imbalanced classification performance.
To address Task 5 in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 challenge, in this paper, we propose an ensemble learning system. The proposed system consists of three different models, based on convolutional neural network and long short memory recurrent neural network. With extracted …
For technology (like serious games) that aims to deliver interactive learning, it is important to address relevant mental experiences such as reflective thinking during problem solving. To facilitate research in this direction, we present the weDraw-1 Movement Dataset of body movement sensor data and reflective thinkin…
In real-world machine learning applications, data subsets correspond to especially critical outcomes: vulnerable cyclist detections are safety-critical in an autonomous driving task, and "question" sentences might be important to a dialogue agent's language understanding for product purposes. While machine learning mod…
SELO model predicts link signs better than SDGNN using subgraph encoding and linear optimization.
Proposes sigmoidF1 loss for multilabel classification, improving performance metrics.
Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. However, existing weak supervision approaches fail to model multi-resolution sources for sequential data, like video, that can assign labels to in…
Pronoun resolution is part of coreference resolution, the task of pairing an expression to its referring entity. This is an important task for natural language understanding and a necessary component of machine translation systems, chat bots and assistants. Neural machine learning systems perform far from ideally in th…
Class imbalance problem is commonly faced while developing machine learning models for real-life issues. Due to this problem, the fitted model tends to be biased towards the majority class data, which leads to lower precision, recall, AUC, F1, G-mean score. Several researches have been done to tackle this problem, most…
Transformer models improve malware classification, especially for imbalanced datasets.
FinCARE combines financial data and AI reasoning to improve causal analysis of financial performance.
The Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the minority class, leading to misclassifications for the majority class, and effecting the overall balance of the model. In this article, we present an approa…
Paper fine-tunes a language model to predict long-term stock buy signals.
BERTino is a lightweight Italian DistilBERT model for NLP tasks.
We study the performance of data-driven, a priori and random approaches to label space partitioning for multi-label classification with a Gaussian Naive Bayes classifier. Experiments were performed on 12 benchmark data sets and evaluated on 5 established measures of classification quality: micro and macro averaged F1 s…
Improved race prediction model outperforms existing methods.
Synthetic reference strings are as effective as real ones for training citation parsing models.
Improved classifier for PU data using logistic regression.
DARE uses GPT-2 to generate relation examples, improving RE performance.
Deep transfer learning boosts single-EEG arousal detection accuracy.
We propose a novel neural network architecture, named the Global Workspace Network (GWN), which addresses the challenge of dynamic and unspecified uncertainties in multimodal data fusion. Our GWN is a model of attention across modalities and evolving through time, and is inspired by the well-established Global Workspac…
In this paper, we study local solutions F=(F1,..,Fn) of a general functional equation of the form F1(U1(x,y))+....+Fn(Un(x,y))=0. A such equation will be called an ``abelian functional equation'' (Afe). We will restrict ourselves to the case when the inner functions Ui's are real rational functions. First we prove that…
Patients increasingly turn to search engines and online content before, or in place of, talking with a health professional. Low quality health information, which is common on the internet, presents risks to the patient in the form of misinformation and a possibly poorer relationship with their physician. To address thi…
The study improves credit evaluation in peer-to-peer lending using machine learning.
Quantum Support Vector Classifier outperforms other QML models in finance fraud detection.
Improved multiclass classification with class-weighted nearest neighbors.
A method uses ITD and XGBoost for precise power transformer fault diagnosis.
Patients suffering from obstructive sleep apnea are mainly treated with continuous positive airway pressure (CPAP). Good compliance with this therapy is broadly accepted as more than 4h of CPAP average use nightly. Although it is a highly effective treatment, compliance with this therapy is problematic to achieve with …