uncertainty quantification — 3 Technical Articles

Feb 22, 2026 · Artificial Intelligence

Roadmap for Physics‑Informed Machine Learning: Lessons from the 2021 Nature Review

This review of the 2021 Nature Reviews Physics article maps the emerging field of physics‑informed machine learning, outlines three bias pathways for embedding physics, compares PINNs, Neural Operators and other methods, discusses software ecosystems, practical guidelines, and future research directions.

DeepXDENeural OperatorsPINNs

0 likes · 38 min read

Roadmap for Physics‑Informed Machine Learning: Lessons from the 2021 Nature Review

PaperAgent

Jan 5, 2026 · Artificial Intelligence

How QuCo‑RAG Replaces Model Confidence with Objective Evidence to Cut Hallucinations

QuCo‑RAG introduces a dynamic retrieval‑augmented generation framework that quantifies uncertainty using pre‑training corpus statistics, replacing unreliable model confidence with objective frequency and co‑occurrence evidence, achieving millisecond‑level hallucination detection, superior multi‑hop QA performance, and cross‑model transferability across various LLMs.

Dynamic RetrievalHallucination DetectionLLM

0 likes · 9 min read

How QuCo‑RAG Replaces Model Confidence with Objective Evidence to Cut Hallucinations

Bighead's Algorithm Notes

Oct 2, 2025 · Artificial Intelligence

FinZero: Multimodal Large‑Model Reasoning for Financial Time‑Series Forecasting

FinZero is a multimodal large‑model that leverages a 30‑billion‑parameter Qwen2.5‑VL backbone fine‑tuned with the UARPO strategy on the FVLDB dataset, enabling accurate financial time‑series prediction, uncertainty quantification, and outperforming larger models such as GPT‑4o by about 13.5% in high‑confidence groups.

FinZeroGPT-4o comparisonQwen2.5-VL-3B

0 likes · 10 min read

FinZero: Multimodal Large‑Model Reasoning for Financial Time‑Series Forecasting