PROPHET, a CT-visible spatial phenotype associated with pathologically confirmed extrapancreatic perineural invasion (EPNI) may improve care for patients with pancreatic ductal adenocarcinoma https://pubs.rsna.org/doi/10.1148/ryai.260357 #PancreaticCancer #InterpretableAI #ML
November 3, 2026 at 3:15 PM
PROPHET, a CT-visible spatial phenotype associated with pathologically confirmed extrapancreatic perineural invasion (EPNI) may improve care for patients with pancreatic ductal adenocarcinoma https://pubs.rsna.org/doi/10.1148/ryai.260357 #PancreaticCancer #InterpretableAI #ML
Anatomy-informed CT Spatial Phenotype Associated with Extrapancreatic Perineural Invasion in Resected Pancreatic Ductal Adenocarcinoma https://pubs.rsna.org/doi/10.1148/ryai.260357 #PDAC #InterpretableAI #AI
November 4, 2026 at 11:15 PM
Anatomy-informed CT Spatial Phenotype Associated with Extrapancreatic Perineural Invasion in Resected Pancreatic Ductal Adenocarcinoma https://pubs.rsna.org/doi/10.1148/ryai.260357 #PDAC #InterpretableAI #AI
Anatomy-informed CT Spatial Phenotype Associated with Extrapancreatic Perineural Invasion in Resected Pancreatic Ductal Adenocarcinoma https://pubs.rsna.org/doi/10.1148/ryai.260357 #PancreaticCancer #InterpretableAI #MachineLearning
October 28, 2026 at 11:15 PM
Anatomy-informed CT Spatial Phenotype Associated with Extrapancreatic Perineural Invasion in Resected Pancreatic Ductal Adenocarcinoma https://pubs.rsna.org/doi/10.1148/ryai.260357 #PancreaticCancer #InterpretableAI #MachineLearning
New interpretable experiential learning model uses state history and global feedback to learn transition graphs with utility and evidence counts. Tested on Atari Breakout, it matches certain…
#InterpretableAI #ReinforcementLearning #OpenSourceAI #MachineLearning
https://arxiv.org/abs/2605.00940
#InterpretableAI #ReinforcementLearning #OpenSourceAI #MachineLearning
https://arxiv.org/abs/2605.00940
October 19, 2026 at 10:01 AM
New interpretable experiential learning model uses state history and global feedback to learn transition graphs with utility and evidence counts. Tested on Atari Breakout, it matches certain…
#InterpretableAI #ReinforcementLearning #OpenSourceAI #MachineLearning
https://arxiv.org/abs/2605.00940
#InterpretableAI #ReinforcementLearning #OpenSourceAI #MachineLearning
https://arxiv.org/abs/2605.00940
Sparse convolutional neural network identifies phase transitions in experimental quantum simulators by learning interpretable latent representations as measurable spin correlators, bridging black-box predictions with explainable physics.
#QuantumSimulation #InterpretableAI #Research
#QuantumSimulation #InterpretableAI #Research
TetrisCNN: Interpretable Phase Detection in Quantum Simulators via Sparse Convolutional Networks
arxiv.org
September 18, 2026 at 5:29 AM
Sparse convolutional neural network identifies phase transitions in experimental quantum simulators by learning interpretable latent representations as measurable spin correlators, bridging black-box predictions with explainable physics.
#QuantumSimulation #InterpretableAI #Research
#QuantumSimulation #InterpretableAI #Research
A parallel post on LinkedIn: www.linkedin.com/posts/stefan...
Stefan Herzog on LinkedIn: #boosting #boost #ai #xai #interpretableai #iai #boosting #competences
🌟🤖📝
#BOOSTING human competences with interpretable and explainable artificial intelligence
How can AI #BOOST human decision-making instead of replacing…
www.linkedin.com
February 28, 2025 at 3:19 PM
A parallel post on LinkedIn: www.linkedin.com/posts/stefan...
12/ Let’s rethink the future of human-AI collaboration. 🤝
Herzog, S. M., & Franklin, M. (2024). Boosting human competences with interpretable and explainable artificial intelligence. Decision, 11(4), 493–510. doi.org/10.1037/dec0...
#AI #XAI #InterpretableAI #IAI #boosting #competences
Herzog, S. M., & Franklin, M. (2024). Boosting human competences with interpretable and explainable artificial intelligence. Decision, 11(4), 493–510. doi.org/10.1037/dec0...
#AI #XAI #InterpretableAI #IAI #boosting #competences
APA PsycNet
doi.org
November 20, 2024 at 12:25 PM
12/ Let’s rethink the future of human-AI collaboration. 🤝
Herzog, S. M., & Franklin, M. (2024). Boosting human competences with interpretable and explainable artificial intelligence. Decision, 11(4), 493–510. doi.org/10.1037/dec0...
#AI #XAI #InterpretableAI #IAI #boosting #competences
Herzog, S. M., & Franklin, M. (2024). Boosting human competences with interpretable and explainable artificial intelligence. Decision, 11(4), 493–510. doi.org/10.1037/dec0...
#AI #XAI #InterpretableAI #IAI #boosting #competences
🌟🤖📝 **Boosting human competences with interpretable and explainable artificial intelligence**
How can AI *boost* human decision-making instead of replacing it? We talk about this in our new paper.
doi.org/10.1037/dec0...
#AI #XAI #InterpretableAI #IAI #boosting #competences
🧵👇
How can AI *boost* human decision-making instead of replacing it? We talk about this in our new paper.
doi.org/10.1037/dec0...
#AI #XAI #InterpretableAI #IAI #boosting #competences
🧵👇
November 20, 2024 at 12:25 PM
🌟🤖📝 **Boosting human competences with interpretable and explainable artificial intelligence**
How can AI *boost* human decision-making instead of replacing it? We talk about this in our new paper.
doi.org/10.1037/dec0...
#AI #XAI #InterpretableAI #IAI #boosting #competences
🧵👇
How can AI *boost* human decision-making instead of replacing it? We talk about this in our new paper.
doi.org/10.1037/dec0...
#AI #XAI #InterpretableAI #IAI #boosting #competences
🧵👇
Paper: nature.com/articles/s4200…
Code: github.com/ohsu-cedar-com�#InterpretableMLr#AIinCancerI#SingleCelli#MultiOmicsu#InterpretableAIr#CancerGenomicsr#OpenSciencee#Bioinformaticsf#ComputationalBiologya#MachineLearningeLearning
@commsbio.nature.com @ohsunews.bsky.social @ohsuknight.bsky.social
Code: github.com/ohsu-cedar-com�#InterpretableMLr#AIinCancerI#SingleCelli#MultiOmicsu#InterpretableAIr#CancerGenomicsr#OpenSciencee#Bioinformaticsf#ComputationalBiologya#MachineLearningeLearning
@commsbio.nature.com @ohsunews.bsky.social @ohsuknight.bsky.social
August 15, 2025 at 10:02 AM
'Deep Learning-Enabled Interpretable Down Syndrome Detection Model' - research sponsored by the King Salman Center for #DisabilityResearch - on #ScienceOpen:
🖇️ #DownSyndrome #AIinMedicine #InterpretableAI #MedicalDiagnostics
🖇️ #DownSyndrome #AIinMedicine #InterpretableAI #MedicalDiagnostics
Deep Learning-Enabled Interpretable Down Syndrome Detection Model
<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d11196367e124">Down syndrome (DS) is a genetic condition characterized by distinct facial features
and...
www.scienceopen.com
August 19, 2025 at 12:48 PM
'Deep Learning-Enabled Interpretable Down Syndrome Detection Model' - research sponsored by the King Salman Center for #DisabilityResearch - on #ScienceOpen:
🖇️ #DownSyndrome #AIinMedicine #InterpretableAI #MedicalDiagnostics
🖇️ #DownSyndrome #AIinMedicine #InterpretableAI #MedicalDiagnostics
Dive deeper into the details here: buff.ly/W0SKVbn
#MedicalAI #InterpretableAI #HealthcareTech #ActuarialScience #TrustworthyAI
#MedicalAI #InterpretableAI #HealthcareTech #ActuarialScience #TrustworthyAI
September 3, 2025 at 12:24 PM
Dive deeper into the details here: buff.ly/W0SKVbn
#MedicalAI #InterpretableAI #HealthcareTech #ActuarialScience #TrustworthyAI
#MedicalAI #InterpretableAI #HealthcareTech #ActuarialScience #TrustworthyAI
The Sum-of-Parts (SOP) framework turns any differentiable model into a neural network, learning feature groups and achieving state-of-the-art results on vision and language benchmarks. Read more: https://getnews.me/sum-of-parts-framework-boosts-interpretable-neural-networks/ #interpretableai #sann
October 8, 2025 at 11:29 AM
The Sum-of-Parts (SOP) framework turns any differentiable model into a neural network, learning feature groups and achieving state-of-the-art results on vision and language benchmarks. Read more: https://getnews.me/sum-of-parts-framework-boosts-interpretable-neural-networks/ #interpretableai #sann
Scientists unveiled a technique that extracts a sparse basis from CNN feature spaces, improving interpretability without manual labels. Tests on ResNet and VGG matched probing performance. Read more: https://getnews.me/interpretable-basis-extraction-for-visual-ai-explanations/ #interpretableai #ai
September 25, 2025 at 11:10 AM
Scientists unveiled a technique that extracts a sparse basis from CNN feature spaces, improving interpretability without manual labels. Tests on ResNet and VGG matched probing performance. Read more: https://getnews.me/interpretable-basis-extraction-for-visual-ai-explanations/ #interpretableai #ai
Researchers mapped 6.6 K ViT features via sparse autoencoders and proposed a residual replacement model that swaps updates for interpretable linear combos. Read more: https://getnews.me/study-deciphers-vision-transformers-via-residual-replacement/ #visiontransformers #interpretableai
September 24, 2025 at 10:01 PM
Researchers mapped 6.6 K ViT features via sparse autoencoders and proposed a residual replacement model that swaps updates for interpretable linear combos. Read more: https://getnews.me/study-deciphers-vision-transformers-via-residual-replacement/ #visiontransformers #interpretableai
Neural Logic Networks now support NOT gates and bias terms, enabling transparent IF‑THEN rules for tabular data. The open‑source code was released on 11 Aug 2025. https://getnews.me/neural-logic-networks-boost-interpretable-ai-classification/ #neurallogicnetworks #interpretableai #opensource
September 20, 2025 at 12:38 PM
Neural Logic Networks now support NOT gates and bias terms, enabling transparent IF‑THEN rules for tabular data. The open‑source code was released on 11 Aug 2025. https://getnews.me/neural-logic-networks-boost-interpretable-ai-classification/ #neurallogicnetworks #interpretableai #opensource
6/ What about interpretable models? They are often more trustworthy but often cannot be trained by federated learning. 🌲 FedCT doesn't discriminate against interpretable models. Works with decision trees, XGBoost, etc. Quality similar to centralized training. #InterpretableAI
January 17, 2025 at 10:29 AM
6/ What about interpretable models? They are often more trustworthy but often cannot be trained by federated learning. 🌲 FedCT doesn't discriminate against interpretable models. Works with decision trees, XGBoost, etc. Quality similar to centralized training. #InterpretableAI
ICE-T is a new prompting method that boosts AI accuracy and transparency, outperforming zero-shot learning—especially in regulated, high-stakes fields.
#interpretableai
#interpretableai
Improving AI Accuracy and Interpretability with ICE-T
hackernoon.com
June 11, 2025 at 2:58 PM
ICE-T is a new prompting method that boosts AI accuracy and transparency, outperforming zero-shot learning—especially in regulated, high-stakes fields.
#interpretableai
#interpretableai
Explore ICE-T method limitations, future research directions, and reproducibility details for enhancing LLM binary classification accuracy and interpretability. #interpretableai
ICE-T’s Challenges and Paths for Future Development
hackernoon.com
June 11, 2025 at 2:24 PM
Explore ICE-T method limitations, future research directions, and reproducibility details for enhancing LLM binary classification accuracy and interpretability. #interpretableai
Explore the ICE-T method’s key questions used for patient assessment across drug abuse, alcohol use, medical decisions, and other clinical tasks. #interpretableai
Key Questions in the ICE-T Method for Patient Assessment
hackernoon.com
June 11, 2025 at 2:24 PM
Explore the ICE-T method’s key questions used for patient assessment across drug abuse, alcohol use, medical decisions, and other clinical tasks. #interpretableai
ICE-T outperforms zero-shot methods, significantly boosting µF1 scores in GPT-3.5 and GPT-4 across diverse classification tasks and datasets. #interpretableai
ICE-T Outperforms Zero-Shot in NLP Tasks Across Multiple Domains
hackernoon.com
June 10, 2025 at 11:15 PM
ICE-T outperforms zero-shot methods, significantly boosting µF1 scores in GPT-3.5 and GPT-4 across diverse classification tasks and datasets. #interpretableai
LLMs generate yes/no questions to improve binary classification. We test classifiers and analyze µF1 performance using GPT-4 and GPT-3.5 outputs. #interpretableai
Improving Binary Classification with LLM-Generated Questions
hackernoon.com
June 10, 2025 at 11:15 PM
LLMs generate yes/no questions to improve binary classification. We test classifiers and analyze µF1 performance using GPT-4 and GPT-3.5 outputs. #interpretableai
Explore 3 labeled NLP datasets for binary classification: medical advice, human rights violations, and unfair contract terms in online ToS. #interpretableai
Medical and Legal Text Datasets for Binary Classification Tasks
hackernoon.com
June 10, 2025 at 11:15 PM
Explore 3 labeled NLP datasets for binary classification: medical advice, human rights violations, and unfair contract terms in online ToS. #interpretableai
Explore annotated datasets used for text classification across domains—medical records, climate reports, and political tweets on Catalan independence. #interpretableai
Diverse NLP Datasets for Real-World Text Classification
hackernoon.com
June 10, 2025 at 11:14 PM
Explore annotated datasets used for text classification across domains—medical records, climate reports, and political tweets on Catalan independence. #interpretableai
Learn how to prompt LLMs, convert their outputs into feature vectors, and train a classifier using verbalized responses for predictive tasks.
#interpretableai
#interpretableai
Using LLMs for Downstream Classification: Prompt, Verbalize, Train
hackernoon.com
June 10, 2025 at 11:14 PM
Learn how to prompt LLMs, convert their outputs into feature vectors, and train a classifier using verbalized responses for predictive tasks.
#interpretableai
#interpretableai
Learn how the ICE-T system trains language models using yes/no questions, converting answers into feature vectors for classifier training. #interpretableai
How ICE-T Trains LLMs with Yes/No Questions for Better Classification
hackernoon.com
June 10, 2025 at 11:14 PM
Learn how the ICE-T system trains language models using yes/no questions, converting answers into feature vectors for classifier training. #interpretableai