Copertina

What Machine Learning Tells Us About the Mathematical Structure of Concepts

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Otsuka, Jun
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

What Can String Probability Tell Us About Grammaticality?
di: Hu, Jennifer, et al.
Pubblicazione: (2025)

What Radio Waves Tell Us about Sleep
di: He, Hao, et al.
Pubblicazione: (2024)

What LLMs Think When You Don't Tell Them What to Think About?
di: Kwon, Yongchan, et al.
Pubblicazione: (2026)

Position: What Can Large Language Models Tell Us about Time Series Analysis
di: Jin, Ming, et al.
Pubblicazione: (2024)

What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks
di: Sokol, Kacper, et al.
Pubblicazione: (2024)

Towards Understanding What State Space Models Learn About Code
di: Wu, Jiali, et al.
Pubblicazione: (2026)

When the Inference Meets the Explicitness or Why Multimodality Can Make Us Forget About the Perfect Predictor
di: Domínguez-Vidal, J. E., et al.
Pubblicazione: (2026)

What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code
di: Zhao, Yuze, et al.
Pubblicazione: (2026)

What Papers Don't Tell You: Recovering Tacit Knowledge for Automated Paper Reproduction
di: Li, Lehui, et al.
Pubblicazione: (2026)

Trained Models Tell Us How to Make Them Robust to Spurious Correlation without Group Annotation
di: Ghaznavi, Mahdi, et al.
Pubblicazione: (2024)

What Retractions Tell Us About Research Integrity in Mexican Academia
di: Karina Ordoñez‐Torres, et al.
Pubblicazione: (2025)

Mathematics and Machine Creativity: A Survey on Bridging Mathematics with AI
di: Liang, Shizhe, et al.
Pubblicazione: (2024)

Argumentative Human-AI Decision-Making: Toward AI Agents That Reason With Us, Not For Us
di: Vasileiou, Stylianos Loukas, et al.
Pubblicazione: (2026)

Telling Human and Machine Handwriting Apart
di: Leiva, Luis A., et al.
Pubblicazione: (2026)

Learning Hierarchically Structured Concepts
di: Lynch, Nancy, et al.
Pubblicazione: (2019)

What an Autonomous Agent Discovers About Molecular Transformer Design: Does It Transfer?
di: Wijaya, Edward
Pubblicazione: (2026)

What Legislators Tell Us About Legislatures: Evidence From PELA‐USAL
di: Manuel Alcántara Sáez, et al.
Pubblicazione: (2025)

What Digital Competency Tells Us About E‐Learning Satisfaction of Pre‐Service Teachers
di: Serkan Boyraz, et al.
Pubblicazione: (2024)

What Makes In-context Learning Effective for Mathematical Reasoning: A Theoretical Analysis
di: Liu, Jiayu, et al.
Pubblicazione: (2024)

Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation
di: Chu, Yun-Wei, et al.
Pubblicazione: (2024)

What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Models
di: Zhu, Shengqi, et al.
Pubblicazione: (2024)

ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models
di: Wu, Yanan, et al.
Pubblicazione: (2024)

From Abstract to Contextual: What LLMs Still Cannot Do in Mathematics
di: Cao, Bowen, et al.
Pubblicazione: (2026)

Generative Discrimination: What Happens When Generative AI Exhibits Bias, and What Can Be Done About It
di: Hacker, Philipp
Pubblicazione: (2024)

RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library
di: Wang, Jiapeng, et al.
Pubblicazione: (2025)

Tell Me What You Don't Know: Enhancing Refusal Capabilities of Role-Playing Agents via Representation Space Analysis and Editing
di: Liu, Wenhao, et al.
Pubblicazione: (2024)

AI Agent Systems for Supply Chains: Structured Decision Prompts and Memory Retrieval
di: Yoshizato, Konosuke, et al.
Pubblicazione: (2026)

Should We be Pedantic About Reasoning Errors in Machine Translation?
di: Bao, Calvin, et al.
Pubblicazione: (2026)

What Can DKIST/DL-NIRSP Tell Us About Quiet-Sun Magnetism?
di: Liu, Jiayi, et al.
Pubblicazione: (2024)

What Artificial Neural Networks Can Tell Us About Human Language Acquisition
di: Warstadt, Alex, et al.
Pubblicazione: (2022)

What Do Klein et al. Tell Us About Test Scores in Texas?
di: Laurence A. Toenjes
Pubblicazione: (2005)

What Do Monte Carlo Simulations Tell Us About Compressible Ising Models?
di: D. P. Landau
Pubblicazione: (2006)

What Do Machine Learning Researchers Mean by "Reproducible"?
di: Raff, Edward, et al.
Pubblicazione: (2024)

Rescaling Confidence: What Scale Design Reveals About LLM Metacognition
di: Dai, Yuyang
Pubblicazione: (2026)

About the Unreal
di: Beverley, John, et al.
Pubblicazione: (2025)

The MedPerturb Dataset: What Non-Content Perturbations Reveal About Human and Clinical LLM Decision Making
di: Gourabathina, Abinitha, et al.
Pubblicazione: (2025)

CORE: Concept-Oriented Reinforcement for Bridging the Definition-Application Gap in Mathematical Reasoning
di: Gao, Zijun, et al.
Pubblicazione: (2025)

The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning
di: Zimmer, Max, et al.
Pubblicazione: (2026)

Mining the Mind: What 100M Beliefs Reveal About Frontier LLM Knowledge
di: Ghosh, Shrestha, et al.
Pubblicazione: (2025)

ICE-T: A Multi-Faceted Concept for Teaching Machine Learning
di: Krone, Hendrik, et al.
Pubblicazione: (2024)