#interpretableAI
🌟🤖📝 **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
🧵👇
November 20, 2024 at 12:25 PM
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
TetrisCNN: Interpretable Phase Detection in Quantum Simulators via Sparse Convolutional Networks
arxiv.org
September 18, 2026 at 5:29 AM
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
APA PsycNet
doi.org
November 20, 2024 at 12:25 PM
My book 'Mastering Modern Time Series Forecasting : The Complete Guide to Statistical, Machine Learning & Deep Learning Models in Python' -> valeman.gumroad.com/...

#timeseries #machinelearning #forecasting #shapelets #interpretableAI #predictivemaintenance #neuralnetworks
Mastering Modern Time Series Forecasting : The Complete Guide to Statistical, Machine Learning & Deep Learning Models in Python
📘 Mastering Modern Time Series Forecasting (early access)The book trusted by data science leaders in 100+ countries. Unlock the toolkit behind today’s most powerful forecasting systems. 💸 Pricing 🎉 Standard Edition Price: $40 | Minimum: $35Will increase to $80+ as content grows. A tremendous amount of work and expertise has gone into this book, which is designed to deliver exponential improvement to your forecasting skills, your company's bottom line and ROI, and your career. Forecasting is one of the most in-demand skills across nearly every industry today. As the content continues to grow, if you find value in it—or simply want to support the project—you're welcome to contribute whatever it’s worth to you ❤️. 🧠 Why This Book Stands Out🔑 Forecasting models are only 5% of the equation.The other 95%? It’s the hard-earned knowledge of metrics, validation, deployment, failure modes, and real-world constraints — insights that are often missing or buried in internet noise and social media fluff.🔍 It starts with what actually matters: solid foundations.Learn how to properly evaluate forecasts, recognize when they're failing, and build with confidence — not on shaky assumptions, but on methods that stand up to real-world pressure.💎 You’ll also learn how to assess the forecastability of a time series — a critical step for managing your time, setting stakeholder expectations, and realistically estimating how far forecasting accuracy can be pushed before diminishing returns kick in.🧠 Built for understanding — not just coding.Go beyond black-box code. Grasp model mechanics and decision-making logic to truly understand how and why things work.💻 Clear, transparent, production-ready code.No obfuscation, no throwaway scripts. Every example is fully documented, reusable, and ready for real-world use.🔄 Continuously improved through real feedback.This is a living resource shaped by an active community of readers. Many improvements and additions come directly from their thoughtful feedback — and all readers get lifetime updates, including new chapters and bonus tools. Thank you to all contributors — your insights are recognized and appreciated in the book.📚 Comprehensive, real-world coverage.From classical time series models to deep learning and forecasting-specific transformers (FTSMs), the book covers a wide range — but always with a practical lens. Every method has been tested in production or validated against strong academic benchmarks. No fluff, just tools that work.📈 Real ROI — for your company and your career.Readers often see immediate improvements in model accuracy, interpretability, and stakeholder trust. No more silent failures or fragile production systems. This book helps you build forecasting solutions that earn trust, drive business results, and accelerate your career.✍️ About the AuthorWritten by Valeriy Manokhin, PhD, MBA, CQF — a seasoned forecasting expert, data scientist, and machine learning researcher with publications in top academic journals.Valeriy has advised both startups and large enterprises, helping them build and rebuild forecasting systems at scale. He has led successful forecasting initiatives for global organizations — including winning competitive tenders from multinational companies, outperforming major consulting firms like BCG and specialized AI startups focused on forecasting. He has delivered production-grade solutions for industry leaders such as Stanley Black & Decker and GfK.His methods have driven multimillion-dollar business impact, and his training programs have reached professionals in over 40 countries. This book is now used in more than 100+ countries and has become a #1-ranked title in Machine Learning, Forecasting, and Time Series across major platforms.🌍 Trusted By and Taught ToValeriy’s expertise is trusted by leaders at:Amazon, Apple, Google, Meta, Nike, BlackRock, Morgan Stanley, Target, NTT Data, Mars Inc., Lidl, Publicis Sapient, and more.His frameworks are followed by professionals from:University of Chicago, KTH (Sweden), UBC (Canada), DTU (Denmark), and other world-class institutions.👤 Students include:VPs of Engineering, AI Leads, Principal & Lead Data Scientists, ML Engineers, Consultants, Professors, Founders, Researchers, and PhD students.🎓 Want a Live, Interactive Learning Experience?Pair this book with the Modern Forecasting Mastery course on Maven.Join live cohort sessions with Valeriy, get direct feedback, and build models with peers.Next cohort → maven.com/valeriy-manokhin/modern-forecasting-mastery📦 What You Get📥 Instant access to the book — start reading immediately.🔄 Free updates — including new chapters, bug fixes, and bonus content.💬 Exclusive access to the private Discord community — connect with fellow readers, get additional materials, early bonuses, special discounts, and join live events with the author.🔓 Pro Edition Bonus Pack (Early Access – $65) 🔥🔥🔥 Includes everything above, plus:✅ Premium Forecasting Templates — plug-and-play workflows✅ Extended Case Studies — deep analyses across major industries✅ Cheat Sheets & Flashcards — quick-reference model guides and best practices✅ Behind-the-Scenes Notebooks — annotated walkthroughs and exploratory pipelines✅ Forecast Model Selection Toolkit — Python notebooks to benchmark, optimize, and compare📈 Ideal for professionals and teams who want to build and deploy faster—and sidestep the guesswork.https://valeman.gumroad.com/l/MasteringModernTimeSeriesForecastingPro💸 New Pricing effective 16th June - grab your copy before price increase 🎉 Standard Edition Price: $45 | Minimum: $39Will increase to $80+ as content grows.If you find value or simply want to support the project, feel free to pay what it’s worth to you ❤️Ready to take your forecasting skills from stats to neural nets, and from theory to real-world deployment?👉 Hit “Buy Now” and start mastering forecasting like never before.
valeman.gumroad.com
July 14, 2025 at 1:42 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
September 20, 2025 at 12:38 PM
How can #AI make seizure detection more transparent? 🧠
Explore how a Variational Autoencoder helps interpret #SEEG data for #Epilepsy care — blending precision and explainability.
👉 Read more: www.neuroelectrics.com/blog/variati...
#SeizureDetection #DeepLearning #InterpretableAI #Neurotech
Variational Autoencoder for Interpretable Seizure Onset Phase Detection in Epilepsy
Drug-resistant epilepsy often requires precise identification of seizure onset zones using SEEG recordings. This article presents a Variational Autoencoder–based deep learning framework that detects a...
www.neuroelectrics.com
November 18, 2025 at 9:55 AM
'Deep Learning-Enabled Interpretable Down Syndrome Detection Model' - research sponsored by the King Salman Center for #DisabilityResearch - on #ScienceOpen:

🖇️ #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
September 3, 2025 at 12:24 PM
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
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
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
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
ICE-T is a new prompting method that boosts AI accuracy and transparency, outperforming zero-shot learning—especially in regulated, high-stakes fields.
#interpretableai
Improving AI Accuracy and Interpretability with ICE-T
hackernoon.com
June 11, 2025 at 2:58 PM
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 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
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
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
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 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
Learn how to prompt LLMs, convert their outputs into feature vectors, and train a classifier using verbalized responses for predictive tasks.
#interpretableai
Using LLMs for Downstream Classification: Prompt, Verbalize, Train
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
How ICE-T Trains LLMs with Yes/No Questions for Better Classification
hackernoon.com
June 10, 2025 at 11:14 PM
LLMs struggle with interpretability, overconfidence, and flawed explanations—key hurdles to using them in high-stakes domains like medicine or science. #interpretableai
Why Interpreting LLMs Is Still So Hard
hackernoon.com
June 10, 2025 at 11:13 PM
Explore advanced prompting and in-context learning strategies that improve the reasoning and performance of large language models during inference.
#interpretableai
How Prompting and In-Context Learning Improve LLM Performance
hackernoon.com
June 10, 2025 at 11:13 PM