#explainablemachinelearning 검색 결과
At @FraunhoferITWM for ISOLDE conference. Looking forward to meeting in person again many colleagues and friends. Looking forward to discussing with them about some deep links between Location and #ExplainableMachineLearning I am working with @jasoneramirez + @DoloresRomeroM

Check this newly published article "Towards Faithful Local Explanations: Leveraging SVM to Interpret Black-Box Machine Learning Models" at brnw.ch/21wWEYa Authors: Jiaxiang Xu et al. #mdpisymmetry #explainablemachinelearning @2024_HUST

I had the honor to give a plenary lecture in Optimization 2023. Thanks to Luís Gouveia, Agostinho Agra and Cristina Requejo (@RequejoCris) for giving me the chance the present our latest research on the interface between #ExplainableMachineLearning and #Optimization (1/2)

Heute startet die #KI2020 @uni_bamberg_of mit dem Student Day, der spannende Workshops & Tutorials bereithält. U.a. gibt es das #BAyDel2020 Tutorial von Doktorand*innen der @UniWiai oder ein Workshop zu #explainablemachinelearning. Zeit für regen Austausch gibt es natürlich auch.

It is time for Emilio Carrizosa @emiliocarrizosa to delight us with his talk on Group #CounterfactualExplanations: An #ExplainableMachineLearning Problem Addressed By #MathematicalOptimization Methods #bidas5 @BCAMBilbao #orms #XAI

Have a look at #DukeUniversity’s winning entry in our #explainablemachinelearning challenge. They replaced the #blackbox with an interpretable model that was 74% accurate. bit.ly/2Uadmde

Applying Explainable Machine Learning to Classify Smoking Status from Basic Health Biological Signals Read the Article here: bit.ly/3GRBEXf #Explainablemachinelearning #Featureimportance #Machinelearning #Modelprediction #Smoking #Biomedical #Pharmacology

Access the paper here: sciencedirect.com/science/articl… #groundwater #explainablemachinelearning #datascience #mountainousregions
Have a look at #DukeUniversity’s winning entry in our #explainablemachinelearning challenge. They replaced the #blackbox with an interpretable model that was 74% accurate. bit.ly/2Uadmde

💡Highly Cited and Hot Papers in 2022 (mdpi.com/journal/make/a…) 📌No. 8 "#ExplainableMachineLearning Reveals Capabilities, Redundancy, and Limitations of a Geospatial #AirQuality Benchmark #Dataset" 📎mdpi.com/2504-4990/4/1/8

📈 Highly Viewed Papers in 2022 📌No. 10 "#ExplainableMachineLearning Reveals Capabilities, Redundancy, and Limitations of a Geospatial #AirQuality Benchmark #Dataset" Authors: Scarlet Stadtler, Clara Betancourt and Ribana Roscher #Views: 4042 📎mdpi.com/2504-4990/4/1/8

📈Top Cited Papers in Volume 4, Issue 1 (March 2022) 📌No. 3 "#ExplainableMachineLearning Reveals Capabilities, Redundancy, and Limitations of a Geospatial #AirQuality Benchmark Dataset" #Views: 2464 #Citations: 3 📎mdpi.com/2504-4990/4/1/8 #neuralnetwork #randomforest #XAI

Shah Nawaz et al.: Explainable machine learning for diffraction patterns #ExplainableMachineLearning #GradCAM #VisualizationOfRepresentations ... #IUCr scripts.iucr.org/cgi-bin/paper?…
#highlycitedpaper Using Explainable Machine Learning to Improve Intensive Care Unit Alarm Systems mdpi.com/1424-8220/21/2… @UVIGOir #Alarms #ExplainableMachineLearning

Explainable machine learning for public policy: use cases, gaps, & research directions @AmareKas, Kit T. Rodolfa, @HemankLamba & @RayidGhani → doi.org/10.1017/dap.20… #ExplainableMachineLearning #MachineLearning #PublicPolicy #ML #ExplainableML #InterpretableML @HeinzCollege

Explainable Machine Learning: The Importance of a System-Centric Perspective #TechRxiv #Explainablemachinelearning #deeplearning techrxiv.org/articles/prepr…
Very happy (😊) to see our (@Hristostyr) new #arXiv on @Deep__AI!!! #DataScience #ExplainableMachineLearning #FeatureExtraction #Hydroclimatology #Hydrology #Hydrometeorology #LargeSampleHydrology #MachineLearning #RandomForests #TimeSeries #TimeSeriesAnalysis #UngaugedBasins
Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets deepai.org/publication/ti… by @GeorgiaPapachar and @Hristostyr #DataScience #Statistics
deepai.org
Time series features for supporting hydrometeorological explorations and predictions in ungauged...
04/13/22 - Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from...
Have a look at #DukeUniversity’s winning entry in our #explainablemachinelearning challenge. They replaced the #blackbox with an interpretable model that was 74% accurate. community.fico.com/s/page/a5Q2E00…
Rsqrd AI - Umang Bhatt - Challenges in Deploying Explainable Machine Learning #explainablemachinelearning ⚡ youtu.be/tXYQoV0XkSs
Check this newly published article "Towards Faithful Local Explanations: Leveraging SVM to Interpret Black-Box Machine Learning Models" at brnw.ch/21wWEYa Authors: Jiaxiang Xu et al. #mdpisymmetry #explainablemachinelearning @2024_HUST

Applying Explainable Machine Learning to Classify Smoking Status from Basic Health Biological Signals Read the Article here: bit.ly/3GRBEXf #Explainablemachinelearning #Featureimportance #Machinelearning #Modelprediction #Smoking #Biomedical #Pharmacology

📈 Highly Viewed Papers in 2022 📌No. 10 "#ExplainableMachineLearning Reveals Capabilities, Redundancy, and Limitations of a Geospatial #AirQuality Benchmark #Dataset" Authors: Scarlet Stadtler, Clara Betancourt and Ribana Roscher #Views: 4042 📎mdpi.com/2504-4990/4/1/8

💡Highly Cited and Hot Papers in 2022 (mdpi.com/journal/make/a…) 📌No. 8 "#ExplainableMachineLearning Reveals Capabilities, Redundancy, and Limitations of a Geospatial #AirQuality Benchmark #Dataset" 📎mdpi.com/2504-4990/4/1/8

Access the paper here: sciencedirect.com/science/articl… #groundwater #explainablemachinelearning #datascience #mountainousregions
Shah Nawaz et al.: Explainable machine learning for diffraction patterns #ExplainableMachineLearning #GradCAM #VisualizationOfRepresentations ... #IUCr scripts.iucr.org/cgi-bin/paper?…
I had the honor to give a plenary lecture in Optimization 2023. Thanks to Luís Gouveia, Agostinho Agra and Cristina Requejo (@RequejoCris) for giving me the chance the present our latest research on the interface between #ExplainableMachineLearning and #Optimization (1/2)

At @FraunhoferITWM for ISOLDE conference. Looking forward to meeting in person again many colleagues and friends. Looking forward to discussing with them about some deep links between Location and #ExplainableMachineLearning I am working with @jasoneramirez + @DoloresRomeroM

It is time for Emilio Carrizosa @emiliocarrizosa to delight us with his talk on Group #CounterfactualExplanations: An #ExplainableMachineLearning Problem Addressed By #MathematicalOptimization Methods #bidas5 @BCAMBilbao #orms #XAI

#highlycitedpaper Using Explainable Machine Learning to Improve Intensive Care Unit Alarm Systems mdpi.com/1424-8220/21/2… @UVIGOir #Alarms #ExplainableMachineLearning

📈Top Cited Papers in Volume 4, Issue 1 (March 2022) 📌No. 3 "#ExplainableMachineLearning Reveals Capabilities, Redundancy, and Limitations of a Geospatial #AirQuality Benchmark Dataset" #Views: 2464 #Citations: 3 📎mdpi.com/2504-4990/4/1/8 #neuralnetwork #randomforest #XAI

Explainable machine learning for public policy: use cases, gaps, & research directions @AmareKas, Kit T. Rodolfa, @HemankLamba & @RayidGhani → doi.org/10.1017/dap.20… #ExplainableMachineLearning #MachineLearning #PublicPolicy #ML #ExplainableML #InterpretableML @HeinzCollege

Explainable Machine Learning: The Importance of a System-Centric Perspective #TechRxiv #Explainablemachinelearning #deeplearning techrxiv.org/articles/prepr…
#mdpimake Call for reading: Special Issue "#ExplainableMachineLearning" edited by Professor Jochen Garcke and Professor Ribana Roscher View and download all published papers via mdpi.com/journal/make/s… #MachineLearning #transparency #interpretability #explainability

3rd paper 👉doi.org/10.3390/w14101… @Water_MDPI @MDPIOpenAccess #DataScience #ExplainableMachineLearning #FeatureExtraction #Hydroclimatology #Hydrology #Hydrometeorology #LargeSampleHydrology #MachineLearning #RandomForests #TimeSeries #TimeSeriesAnalysis #UngaugedBasins

Very happy (😊) to see our (@Hristostyr) new #arXiv on @Deep__AI!!! #DataScience #ExplainableMachineLearning #FeatureExtraction #Hydroclimatology #Hydrology #Hydrometeorology #LargeSampleHydrology #MachineLearning #RandomForests #TimeSeries #TimeSeriesAnalysis #UngaugedBasins
Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets deepai.org/publication/ti… by @GeorgiaPapachar and @Hristostyr #DataScience #Statistics
deepai.org
Time series features for supporting hydrometeorological explorations and predictions in ungauged...
04/13/22 - Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from...
Heute startet die #KI2020 @uni_bamberg_of mit dem Student Day, der spannende Workshops & Tutorials bereithält. U.a. gibt es das #BAyDel2020 Tutorial von Doktorand*innen der @UniWiai oder ein Workshop zu #explainablemachinelearning. Zeit für regen Austausch gibt es natürlich auch.

Have a look at #DukeUniversity’s winning entry in our #explainablemachinelearning challenge. They replaced the #blackbox with an interpretable model that was 74% accurate. bit.ly/2Uadmde

Applying Explainable Machine Learning to Classify Smoking Status from Basic Health Biological Signals Read the Article here: bit.ly/3GRBEXf #Explainablemachinelearning #Featureimportance #Machinelearning #Modelprediction #Smoking #Biomedical #Pharmacology

At @FraunhoferITWM for ISOLDE conference. Looking forward to meeting in person again many colleagues and friends. Looking forward to discussing with them about some deep links between Location and #ExplainableMachineLearning I am working with @jasoneramirez + @DoloresRomeroM

I had the honor to give a plenary lecture in Optimization 2023. Thanks to Luís Gouveia, Agostinho Agra and Cristina Requejo (@RequejoCris) for giving me the chance the present our latest research on the interface between #ExplainableMachineLearning and #Optimization (1/2)

It is time for Emilio Carrizosa @emiliocarrizosa to delight us with his talk on Group #CounterfactualExplanations: An #ExplainableMachineLearning Problem Addressed By #MathematicalOptimization Methods #bidas5 @BCAMBilbao #orms #XAI

Have a look at #DukeUniversity’s winning entry in our #explainablemachinelearning challenge. They replaced the #blackbox with an interpretable model that was 74% accurate. bit.ly/2Uadmde

Explainable machine learning for public policy: use cases, gaps, & research directions @AmareKas, Kit T. Rodolfa, @HemankLamba & @RayidGhani → doi.org/10.1017/dap.20… #ExplainableMachineLearning #MachineLearning #PublicPolicy #ML #ExplainableML #InterpretableML @HeinzCollege

#highlycitedpaper Using Explainable Machine Learning to Improve Intensive Care Unit Alarm Systems mdpi.com/1424-8220/21/2… @UVIGOir #Alarms #ExplainableMachineLearning

💡Highly Cited and Hot Papers in 2022 (mdpi.com/journal/make/a…) 📌No. 8 "#ExplainableMachineLearning Reveals Capabilities, Redundancy, and Limitations of a Geospatial #AirQuality Benchmark #Dataset" 📎mdpi.com/2504-4990/4/1/8

📈 Highly Viewed Papers in 2022 📌No. 10 "#ExplainableMachineLearning Reveals Capabilities, Redundancy, and Limitations of a Geospatial #AirQuality Benchmark #Dataset" Authors: Scarlet Stadtler, Clara Betancourt and Ribana Roscher #Views: 4042 📎mdpi.com/2504-4990/4/1/8

📈Top Cited Papers in Volume 4, Issue 1 (March 2022) 📌No. 3 "#ExplainableMachineLearning Reveals Capabilities, Redundancy, and Limitations of a Geospatial #AirQuality Benchmark Dataset" #Views: 2464 #Citations: 3 📎mdpi.com/2504-4990/4/1/8 #neuralnetwork #randomforest #XAI

Check this newly published article "Towards Faithful Local Explanations: Leveraging SVM to Interpret Black-Box Machine Learning Models" at brnw.ch/21wWEYa Authors: Jiaxiang Xu et al. #mdpisymmetry #explainablemachinelearning @2024_HUST

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