logistic regression model
PulseAugur coverage of logistic regression model — every cluster mentioning logistic regression model across labs, papers, and developer communities, ranked by signal.
- instance of alphaXiv 90%
- instance of Gotit.pub 90%
- used by tf–idf 90%
- instance of ScienceCast 70%
- used by ScienceCast 70%
- instance of decision tree 70%
- instance of CatalyzeX 70%
- instance of tf–idf 70%
- instance of naive Bayes classifier 70%
- instance of linear regression 70%
- used by COVID-19 70%
- instance of Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases 70%
16 day(s) with sentiment data
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Research paper warns reweighting can introduce new harms in AI fairness
A new research paper published on arXiv explores the potential harms of reweighting, a common technique used to improve fairness in machine learning models. The study, which utilized controlled simulations across 3,000 …
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Machine learning models for IVF live-birth prediction show limited gains over simpler methods
A systematic review and meta-analysis of machine learning models for predicting live birth after IVF/ICSI found that while reported performance varies widely, the actual gains over simpler logistic regression models are…
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New research explores adversarial training for AI robustness · 2 sources tracked
Two new arXiv papers explore adversarial training techniques for improving AI model robustness. The first paper analyzes the dynamics of gradient descent with large step sizes in adversarial linear logistic regression a…
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New hybrid model fuses CNN and classical methods for efficient signal classification
Researchers have developed FuseAMC, a novel decision-fusion model for automatic modulation classification (AMC) that combines a compact convolutional neural network (CNN) with a classical higher-order-cumulant feature c…
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New L2 regularization variant enhances continual learning in linear networks
Researchers have developed a new weighted variant of L2 regularization designed to improve continual learning in diagonal linear networks. This method aims to preserve the sparsity bias that L2 regularization promotes i…
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New method uses on-chain behavior to identify crypto exchange addresses on Ethereum
Researchers have developed a new method to identify centralized exchange (CEX) addresses on the Ethereum blockchain by analyzing their on-chain behavior. The study utilized Proof-of-Reserves disclosures from 15 CEXs and…
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Data augmentation boosts sentiment classification on small datasets
A new arXiv paper explores methods to improve sentiment classification performance on small datasets. The study compares three data augmentation techniques: Easy Data Augmentation (EDA), back-translation, and contextual…
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LLM research explores data selection, TCS evaluation, and RAG vs. fine-tuning strategies
Researchers are exploring new methods for fine-tuning Large Language Models (LLMs) and evaluating their capabilities. One study suggests prioritizing data coverage over difficulty for more effective fine-tuning, outperf…
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NLP improves emergency message routing in disaster simulations
This research paper explores the use of Natural Language Processing (NLP) to prioritize emergency messages during disasters, using the 2015 Nepal earthquake as a case study. The study employed TF-IDF with Logistic Regre…
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HECKTOR 2026 challenge uses AI for head and neck cancer prediction
The HECKTOR 2026 challenge focuses on machine learning applications for head and neck cancer, involving tumor delineation, TN staging, and recurrence-free survival prediction. One team, nucli, utilized a self-supervised…
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AI framework enhances fraud detection in digital banking
This study introduces a novel framework for detecting fraud in digital banking, particularly addressing challenges like imbalanced data and operational limits. The framework incorporates chronological evaluation, featur…
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Research paper analyzes $\alpha$-divergence generalized posteriors
A new research paper explores the asymptotic behavior of generalized variational inference solutions when regularized by an $\alpha$-divergence. The study reveals that under certain conditions, the minimizer of this pro…
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New research questions random splits for CTR model calibration
A new paper evaluates the effectiveness of post-hoc calibration for click-through rate (CTR) models, challenging the common practice of using random data splits for offline evaluation. The research, conducted on the Ava…
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AI framework visualizes cancer care team dynamics and survival risk
Researchers have developed a visual analytics framework to better understand how care team dynamics in cancer treatment evolve over time and impact patient survival risk. This system uses electronic health record commun…
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LLMs evaluated for cost-saving abstention in IT ticket routing
A new arXiv paper explores cost-aware abstention for Large Language Models (LLMs) in IT ticket classification. The research evaluates how abstention, or deferring a decision to a human, can reduce costs associated with …
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AI framework visualizes cancer care team dynamics and patient risk
Researchers have developed a visual analytics framework to model and interpret the evolving dynamics of care teams in cancer treatment. By analyzing communication data from electronic health records, they use a temporal…
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Gradient Descent on Logistic Regression: Equal Norms and Convergence
A new research paper explores the behavior of gradient descent on logistic regression models, particularly when data points are normalized to have equal norms. The study proves that for linearly separable data, gradient…
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Graph-to-Table Systems: Component Interdependence Revealed
Researchers explored graph-to-table systems, which integrate encoders, context constructors, and tabular decoders to process relational data. Their study revealed that the performance and effectiveness of individual com…
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New non-parametric framework enhances scalable learning in stochastic optimization
Researchers have developed a novel non-parametric framework for stochastic optimization (SO) problems, specifically addressing time-varying scenarios crucial for adaptive control and machine learning. This new approach,…
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AI model predicts IVF live birth rates using novel attention network
Researchers have developed a novel hybrid attention network combined with a logistic regression model to predict live birth rates in IVF/ICSI cycles. This model, trained on a curated dataset of 616 cycles, achieved the …