explainable AI | BestHub

JD Retail Technology

Feb 26, 2024 · Artificial Intelligence

Explainable AI Forecasting and End-to-End Inventory Management in JD's Smart Supply Chain

The article details JD’s smart supply‑chain innovations, describing an explainable AI forecasting method that boosts prediction accuracy while maintaining interpretability, and an end‑to‑end inventory management model based on multi‑quantile RNNs that improves replenishment decisions, reduces costs, and enhances overall operational efficiency.

explainable AIforecastinginventory management

0 likes · 14 min read

Explainable AI Forecasting and End-to-End Inventory Management in JD's Smart Supply Chain

Baidu Tech Salon

Dec 14, 2023 · Artificial Intelligence

Baidu Research Institute 2023 Paper Sharing Session – Presented Papers Overview

The Baidu Research Institute’s 2023 Paper Sharing Session featured eight cutting‑edge papers—from semi‑supervised web‑search ranking and hierarchical reinforcement learning for autonomous intersections to spatial‑heterophily graph networks, a unified XAI benchmark, differentiable neuro‑symbolic KG reasoning, and novel stochastic‑gradient and neural‑field loss analyses—showcasing advances across AI, data mining, and computer vision.

Artificial IntelligenceGraph Neural NetworksKnowledge Graphs

0 likes · 10 min read

Baidu Research Institute 2023 Paper Sharing Session – Presented Papers Overview

Rare Earth Juejin Tech Community

Aug 6, 2023 · Artificial Intelligence

Explaining Image Recognition: Logistic Regression and Convolutional Neural Networks

This article introduces the principles of image recognition, compares traditional logistic regression with convolutional neural networks, demonstrates their implementation using Python code, visualizes model weights, and explains key concepts such as padding, convolution, pooling, receptive fields, and multi‑layer feature extraction.

Image Recognitionconvolutional neural networkexplainable AI

0 likes · 12 min read

Explaining Image Recognition: Logistic Regression and Convolutional Neural Networks

DataFunTalk

Aug 4, 2023 · Artificial Intelligence

Self‑Explaining Natural Language Models: Collaborative Game Rationalization and Solutions for Spurious Correlations

The article reviews the growing importance of model explainability in high‑risk domains, analyzes the challenges of large language models, introduces the collaborative game‑theoretic RNP framework, and presents three mitigation strategies—Folded Rationalization, Decoupled Rationalization, and Multi‑Generator Rationalization—along with experimental results and future research directions.

Collaborative RationalizationLipschitz ContinuitySelf-Explaining Models

0 likes · 15 min read

Self‑Explaining Natural Language Models: Collaborative Game Rationalization and Solutions for Spurious Correlations

DataFunTalk

Apr 28, 2023 · Artificial Intelligence

Causal Inference and Uplift Modeling for Insurance Recommendation and Explainability

This article explains how uplift sensitivity prediction, Bayesian causal networks, and decision‑path construction are applied to improve insurance product, coupon, and copy recommendations on the Fliggy platform, detailing modeling approaches, evaluation metrics, and practical outcomes of the causal inference framework.

AB testingbayesian networkscausal inference

0 likes · 16 min read

Causal Inference and Uplift Modeling for Insurance Recommendation and Explainability

Python Programming Learning Circle

Mar 21, 2023 · Artificial Intelligence

A Survey of 10 Python Libraries for Explainable AI (XAI)

This article introduces Explainable AI (XAI), outlines its importance, describes a step-by-step workflow, and reviews ten Python libraries—including SHAP, LIME, ELI5, Shapash, Anchors, BreakDown, Interpret‑Text, AI Explainability 360, OmniXAI, and XAI—providing usage examples and code snippets.

LibrariesXAIexplainable AI

0 likes · 12 min read

A Survey of 10 Python Libraries for Explainable AI (XAI)

DataFunTalk

Nov 4, 2022 · Artificial Intelligence

Explainable Knowledge Graph Reasoning: Background, Advances, Motivation, Recent Research, and Outlook

This article reviews explainable knowledge graph reasoning, covering its background, core concepts, downstream applications, major reasoning methods, motivations for interpretability, recent advances such as hierarchical and Bayesian reinforcement learning, meta‑path mining, and future research directions.

explainable AIgraph reasoninghierarchical RL

0 likes · 18 min read

Explainable Knowledge Graph Reasoning: Background, Advances, Motivation, Recent Research, and Outlook

Model Perspective

Oct 31, 2022 · Artificial Intelligence

Understanding SHAP: How Shapley Values Explain Black‑Box Models

This article explains the SHAP (Shapley Additive Explanation) method, its theoretical foundations in game theory, the computation of Shapley Values, various algorithmic approximations like TreeSHAP and DeepSHAP, practical code examples, and the strengths and limitations of using SHAP for model interpretability.

SHAPShapley Valuesexplainable AI

0 likes · 11 min read

Understanding SHAP: How Shapley Values Explain Black‑Box Models

Model Perspective

Oct 9, 2022 · Artificial Intelligence

Why Model Interpretability Matters: Tackling the Black‑Box Problem in AI

This article explains the challenges of black‑box machine‑learning models, illustrates real‑world banking examples, and introduces explainable AI techniques such as intrinsic vs. post‑hoc and local vs. global explanations to improve trust, safety, and fairness.

AI ethicsblack-box modelsexplainable AI

0 likes · 13 min read

Why Model Interpretability Matters: Tackling the Black‑Box Problem in AI

AntTech

Jul 18, 2022 · Artificial Intelligence

Trusted AI Research at Ant Group: Advances in Computer Vision, Watermark Defense, Robust Machine Learning, and Explainable NLG

Ant Group’s security labs present a series of cutting‑edge AI research achievements—including hierarchical multi‑granular classification for computer vision, watermark‑vaccine defenses, multi‑modal document understanding, robust and explainable machine learning, and logic‑driven data‑to‑text generation—highlighting their commitment to trustworthy and secure AI applications.

AI safetyData2TextRobust Machine Learning

0 likes · 12 min read

Trusted AI Research at Ant Group: Advances in Computer Vision, Watermark Defense, Robust Machine Learning, and Explainable NLG

DataFunTalk

Jan 3, 2020 · Artificial Intelligence

Survey of Machine Learning Model Interpretability Techniques

This article provides a comprehensive survey of model interpretability in machine learning, covering its importance, evaluation criteria, and a wide range of techniques such as permutation importance, partial dependence plots, ICE, LIME, SHAP, RETAIN, and LRP, along with practical code examples and visualizations.

ICELIMEPDP

0 likes · 39 min read

Survey of Machine Learning Model Interpretability Techniques

DataFunTalk

Feb 20, 2019 · Artificial Intelligence

Recommendation Reasoning and Its Path Toward Future AI

This article explores why recommendation systems need reasoning, how recommendation reasoning connects to future strong AI, discusses explainability, causal inference, graph-based reasoning, and the philosophical underpinnings of AI, while also reflecting on practical examples from Hulu's recommendation platform.

Recommendation systemscausal reasoningexplainable AI

0 likes · 25 min read

Recommendation Reasoning and Its Path Toward Future AI

JD Tech

May 24, 2018 · Artificial Intelligence

AI-Enabled Security: JD Security’s DEF CON China Presentation on Explainable AI for Security

JD Security showcased its AI‑driven security research at DEF CON China, presenting three accepted papers and a collaborative AI safety report with Penn State, detailing a black‑box explanation method using Gaussian‑mixture models to make deep‑learning decisions transparent for security applications.

AIDef Condeep learning

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AI-Enabled Security: JD Security’s DEF CON China Presentation on Explainable AI for Security