Interpretability

AntTech

Apr 17, 2024 · Artificial Intelligence

LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs

LLMRG introduces a novel framework that leverages large language models to construct personalized reasoning graphs, integrating chain reasoning, self‑verification, divergent extension, and knowledge‑base self‑improvement, thereby enhancing recommendation accuracy, interpretability, and performance across multiple benchmark datasets without additional user or item information.

AIInterpretabilityRecommendation systems

0 likes · 9 min read

LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs

DataFunTalk

Mar 14, 2023 · Artificial Intelligence

Review of Deep Learning Model Evolution and Future Trends

The article reviews the past six years of deep‑learning model development, highlighting patterns such as increasing scale, growing universality, limited interpretability, and challenges in efficiency, while forecasting future directions like more efficient architectures, enhanced perception, multimodal capabilities, integration with life sciences, and the emergence of general‑purpose intelligent agents, and concludes with a promotion for a deep‑learning practice ebook.

AI TrendsDeep LearningInterpretability

0 likes · 6 min read

Review of Deep Learning Model Evolution and Future Trends

Model Perspective

Oct 18, 2022 · Artificial Intelligence

Unlocking Nonlinear Insights: A Practical Guide to Generalized Additive Models (GAM)

Generalized Additive Models (GAM) extend linear regression by using smooth, non‑parametric functions and link functions to capture complex nonlinear relationships, offering flexible estimation via backfitting and local scoring, while balancing interpretability and computational cost, as illustrated through a calcium‑intake health example.

BackfittingGAMInterpretability

0 likes · 8 min read

Unlocking Nonlinear Insights: A Practical Guide to Generalized Additive Models (GAM)

DataFunTalk

Oct 22, 2021 · Artificial Intelligence

Applying AI Techniques to Credit Reporting and Risk Modeling: Model Structure, Pre‑training, Ranking and Interpretability

This article presents a comprehensive overview of how AI technologies are applied to credit reporting and loan risk modeling, detailing data characteristics, end‑to‑end model architectures, pre‑training strategies, risk‑ranking methods, and interpretability techniques for financial risk assessment.

AIInterpretabilityRisk Ranking

0 likes · 17 min read

Applying AI Techniques to Credit Reporting and Risk Modeling: Model Structure, Pre‑training, Ranking and Interpretability

DataFunSummit

Oct 22, 2021 · Artificial Intelligence

Applying AI Techniques to Credit Reporting and Risk Modeling

This article presents a comprehensive overview of how AI technologies are applied to credit reporting, covering data characteristics, end‑to‑end model architectures, pre‑training strategies, risk ranking objectives, and interpretability methods to improve financial risk assessment.

AIInterpretabilitycredit risk

0 likes · 16 min read

Applying AI Techniques to Credit Reporting and Risk Modeling

DataFunTalk

Mar 23, 2021 · Artificial Intelligence

Explainability in Graph Neural Networks: A Taxonomic Survey

This article surveys recent advances in graph neural network explainability, systematically categorizing instance‑level and model‑level methods, reviewing datasets, evaluation metrics, and proposing new benchmark graph datasets for interpretable GNN research, and highlighting future research directions.

GNNGraph Neural NetworksInterpretability

0 likes · 40 min read

Explainability in Graph Neural Networks: A Taxonomic Survey

DataFunTalk

Mar 14, 2021 · Artificial Intelligence

A Review of Medical Domain Sentiment Analysis: Interpretability, Contextual Aspect‑Sentiment Relations, Noisy Labels, and Domain Lexicon Construction

This article reviews recent research on medical sentiment analysis, covering interpretability of neural models, contextual aspect‑sentiment interactions, strategies for handling noisy labels, and methods for building domain‑specific sentiment lexicons, highlighting challenges and proposed solutions.

Deep LearningInterpretabilityaspect‑based sentiment

0 likes · 19 min read

A Review of Medical Domain Sentiment Analysis: Interpretability, Contextual Aspect‑Sentiment Relations, Noisy Labels, and Domain Lexicon Construction

DataFunTalk

Sep 26, 2020 · Artificial Intelligence

What Makes a Good Model? Understanding Model Concepts, Types, and Evaluation in Data Science

This article explores the definition of a model, distinguishes business, data, and function models, discusses criteria for a good model—including performance, fidelity to real‑world relationships, and interpretability—and examines why a universal model does not exist, all within the context of data science and AI.

AIInterpretabilityMachine Learning

0 likes · 18 min read

What Makes a Good Model? Understanding Model Concepts, Types, and Evaluation in Data Science

Tencent Cloud Developer

Sep 23, 2020 · Artificial Intelligence

NLP Model Interpretability: White-box and Black-box Methods and Business Applications

The article reviews NLP interpretability techniques, contrasting white‑box approaches that probe model internals such as neuron analysis, diagnostic classifiers, and attention with black‑box strategies like rationales, adversarial testing, and local surrogates, and argues that black‑box methods are generally more practical for business deployment despite offering shallower insights.

Adversarial TestingBERTDeep Learning

0 likes · 12 min read

NLP Model Interpretability: White-box and Black-box Methods and Business Applications