gradient boosting
PulseAugur coverage of gradient boosting — every cluster mentioning gradient boosting across labs, papers, and developer communities, ranked by signal.
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Gradient boosting framework speeds up tensor network learning
Researchers have developed a new gradient boosting framework called GB-CPKM to improve the efficiency of Tensor Network Kernel Machines (TNKMs). This method sequentially adds learners to increase model capacity, offerin…
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SteelAgent uses LLMs for physics-informed steel property prediction
Researchers have developed SteelAgent, a system that uses a large language model to orchestrate physics-informed models for predicting steel properties. This approach aims to improve generalization and uncertainty repor…
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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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New CBFI framework aims to reveal hidden interactions in ML models
Researchers have developed a new framework called localized case-based feature importance (CBFI) to better understand the decision-making processes of complex machine learning models. Unlike existing methods like SHAP a…
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LLM Tutoring Interactions Show Predictive Value for Student Performance
A new study published on arXiv explores the predictive value of large language model (LLM) tutoring interactions on student performance in an undergraduate artificial intelligence course. Researchers analyzed over 16,00…
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New CBFI framework disentangles feature interactions in ML models
Researchers have developed a new framework called localized case-based feature importance (CBFI) to better understand the internal workings of machine learning models. This method aims to disentangle individual feature …
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AIRO framework enhances IIoT security with streaming ML
A new machine learning framework called AIRO has been developed for threat detection and security within Industrial Internet of Things (IIoT) environments. This framework integrates Apache Kafka for streaming data, spec…
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Machine learning predicts concrete strength under temperature stress
Researchers have developed a data-driven framework using machine learning to predict the compressive strength of fiber-reinforced high-performance concrete under varying temperatures. The study evaluated eight models, f…
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New 5G AI framework predicts retransmissions to boost efficiency
Researchers have developed NOSTRAdAMUS, a predictive framework designed to enhance link adaptation in 5G NR networks. This system forecasts retransmissions based on HARQ history, allowing for proactive adjustments to Mo…
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Machine learning framework predicts restaurant food waste
Researchers have developed a machine learning framework to estimate daily food waste in restaurants, using operational data, weather, and event indicators. The study constructed a dataset of 77,980 records and employed …
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New Sparse Oblique Rule Boosting enhances AI model interpretability and accuracy
Researchers have developed a new method called Sparse Oblique Rule Boosting (SORB) to create more interpretable and accurate symbolic rule ensembles. This approach extends traditional methods by allowing rules to have o…
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New VAE method aids ECG analysis for myocardial scar diagnosis
Researchers have developed a new method using variational autoencoders (VAEs) to analyze electrocardiogram (ECG) data for the differential diagnosis of myocardial scar. The study evaluated $\beta$-VAE-derived ECG repres…
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Open-source pipeline enables crypto data analysis and fraud detection
A new open-source pipeline has been developed to process cryptocurrency market data, enabling ingestion, forecasting, and fraud detection on commodity hardware. This system utilizes Apache Kafka and Apache Spark to repl…
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AI models forecast weather impact on Sri Lankan tea prices
Researchers have developed a novel dataset and applied machine learning models to forecast weather-driven price dynamics in Sri Lanka's tea market. Analyzing four main tea catalogues—High Grown, Low Grown, Off-Grade, an…
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New system estimates calories in Bangladeshi street food using AI models
Researchers have developed a vision-based system for estimating the calorie content of Bangladeshi street food, addressing a gap in current Western-centric approaches. The study compared five object detection and segmen…
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Paper proposes Explainable AI for trustworthy heat demand forecasting
A new paper introduces an ante-hoc Explainable AI methodology to evaluate the global feature importance of machine learning models used in heat demand forecasting. The research aims to enhance the interpretability and t…
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Forma transformer model forecasts financial statements 20 quarters ahead
Researchers have developed Forma, a novel transformer-based model capable of forecasting complete financial statements up to 20 quarters into the future. Forma significantly outperforms various classical machine learnin…
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Gradient Boosting for Imbalanced Data: Weighting, Resampling, and Calibration
This article discusses strategies for handling imbalanced classification problems, particularly in the context of gradient boosting models like XGBoost. It highlights that a common issue is not the model's inability to …
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Gradient Boosting Prediction Costs Driven by Tree Count, Not Features
This article details the cost of running gradient boosting predictions at scale, emphasizing that the number of trees in the model is the primary driver of computational cost, not the number of features. The author prov…
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Google Maps POIs used to estimate income in Sao Paulo
Researchers have developed a method to estimate household income at a sub-municipal level in São Paulo, Brazil, by analyzing crowd-sourced data from Google Maps Points of Interest (POIs). This approach uses POI categori…