Shap
PulseAugur coverage of Shap — every cluster mentioning Shap across labs, papers, and developer communities, ranked by signal.
- used by streamlit 90%
- used by gradient boosting 80%
- used by Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases 70%
- used by alphaXiv 70%
- used by Gotit.pub 70%
- used by ScienceCast 70%
- used by CatalyzeX 70%
- used by Shapley Additive Explanations 70%
- instance of alphaXiv 70%
- competes with Local Interpretable Model Agnostic Explanations 70%
- instance of Local Interpretable Model Agnostic Explanations 70%
- used by SpaceXAI 70%
12 day(s) with sentiment data
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AI model predicts 92% hospital readmission risk using SHAP values
A machine learning model has been developed to predict hospital readmission risk with a reported 92% accuracy. Analysis using SHAP values indicates that key factors influencing readmission include patient age, previous …
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New FCA-Guided framework offers perfect validity for AI breast cancer diagnosis explanations
Researchers have developed a novel framework called FCA-Guided Counterfactual (FCA-CF) to generate actionable explanations for multi-modal breast cancer diagnosis models. This framework uses Formal Concept Analysis to c…
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Machine learning decodes lipid nanoparticle targeting for RNA delivery
Researchers have developed an interpretable machine learning framework to predict and guide the extrahepatic targeting of lipid nanoparticles (LNPs). By analyzing a dataset of 476 LNP formulations, the study identified …
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Physics-based model predicts IN718 texture in 3D printing
Researchers have developed a novel two-stage physics-based model for predicting crystallographic texture intensity in Inconel 718 (IN718) during laser powder bed fusion. The model first maps process variables to melting…
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Interpretable ML analyzes EEG for subject-specific attention shifts
Researchers have developed a machine learning approach to analyze electroencephalography (EEG) signals related to attention shifts. By using a controlled experimental paradigm, they could distinguish between self-initia…
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New ADORE framework enhances ML model interpretability, outperforming LIME and SHAP
Researchers have introduced Adaptive Derivative-Ordered Random Explanation (ADORE), a novel framework designed to enhance the interpretability of complex machine learning models. ADORE addresses limitations of existing …
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New IDE tool FairLint-DL enables pre-training bias detection in deep learning
Researchers have developed FairLint-DL, a new tool integrated into Visual Studio Code that allows developers to test for bias in deep learning models before training. The tool uses information-theoretic metrics based on…
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New hybrid AI framework improves personalized blood pressure estimation
Researchers have developed a new hybrid framework for estimating blood pressure using photoplethysmography (PPG) signals. This approach combines a convolutional neural network (CNN) with a morphology-prior branch to cap…
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Fog-based AI predicts cold-chain temperature on edge hardware
Researchers have successfully deployed a fog-based deep learning system for real-world cold-chain temperature prediction, marking a first for this application. The system, utilizing an LSTM-GRU model on a Raspberry Pi 4…
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New framework uses data storytelling to make AI decisions understandable
Researchers have developed a new framework that combines data storytelling with interpretable machine learning (IML) to make AI decisions more understandable for non-experts. This approach, detailed in a new paper, uses…
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AI models identify biomarkers for liver cancer prediction · 2 sources tracked
Researchers have developed new methods using artificial intelligence to predict and identify biomarkers for hepatocellular carcinoma (HCC), a common form of liver cancer. One study constructed a dataset of 770 patient s…
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New AI frameworks improve Alzheimer's diagnosis using MRI and clinical data
Researchers have developed new deep learning frameworks for diagnosing Alzheimer's disease using multimodal data. One study focuses on grounding image-based models with anatomical references and addressing label leakage…
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XAI framework enhances DER cybersecurity with self-verifying anomaly detection
Researchers have developed a new explainable AI (XAI) framework called ExCYDER to enhance the cybersecurity of Distributed Energy Resources (DERs) in power grids. This framework uses a self-verifying mechanism that comb…
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New DynSHAP framework enhances AI explainability for medical survival analysis
Researchers have developed DynSHAP, a new framework designed to provide explainability for dynamic survival analysis (DSA) models. Existing methods struggle with the longitudinal and irregular nature of patient data and…
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New DP-FedProx framework enhances privacy in telecom churn prediction
Researchers have developed a new framework called DP-FedProx to address customer churn prediction in telecommunication networks. This framework utilizes differentially private federated proximal optimization, allowing m…
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NVIDIA cuML and RAPIDS accelerate ML workflows on GPUs
This tutorial demonstrates how to implement machine learning workflows using NVIDIA's cuML and RAPIDS libraries for GPU acceleration. It covers setting up the GPU environment, accelerating scikit-learn workloads with cu…
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SHAP limitations in fraud detection agents and new explainability techniques explored · 2 sources tracked
This cluster explores the limitations of SHAP (SHapley Additive exPlanations) in the context of autonomous fraud detection agents. It discusses why SHAP falls short for these agents and introduces alternative explainabi…
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Machine learning model predicts ESBL risk to guide antibiotic selection
Researchers have developed a machine learning model using XGBoost to predict the risk of ESBL-producing Enterobacteriaceae before culture results are available. This model, trained on data from 12 hospitals, aims to gui…
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New CARRE framework uses LLMs for explainable customer churn prescription
Researchers have developed CARRE, a novel three-stage framework designed to improve churn prescription for businesses. This system integrates retrieval-augmented candidate generation, cost-aware counterfactual scoring, …
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New multimodal AI framework improves oral cancer detection using imaging and clinical data
Researchers have developed M2-OPMDNet, a novel multimodal deep learning framework designed to improve the detection of oral potentially malignant disorders (OPMDs). This system integrates both imaging data, including wh…