Tagged articles

Uncertainty Quantification

8 articles · Page 1 of 1

Oct 24, 2026 · Artificial Intelligence

TabClustPFN: Out-of-the-Box Tabular Clustering with Probabilistic Cluster Count

Researchers from Renmin University, Rutgers, NUS, and MBZUAI introduce TabClustPFN, a prior-data fitted network that performs zero-shot clustering on tabular data via a single forward pass, automatically estimating the number of clusters and providing a probability distribution over candidate cluster counts, achieving competitive performance on 44 real-world datasets without per-dataset training.

Cluster Count EstimationNeurIPS 2026PFN

0 likes · 10 min read

TabClustPFN: Out-of-the-Box Tabular Clustering with Probabilistic Cluster Count

Machine Heart

Oct 22, 2026 · Artificial Intelligence

SpanUQ: Span-Level LLM Uncertainty Estimation 10-20x Faster Than Sampling

This NeurIPS 2026 paper introduces Span-Level Uncertainty Estimation (SLUE) and SpanUQ, a lightweight probe that detects semantic spans and quantifies their uncertainty in a single forward pass, achieving 10-20x speedup over sampling-based methods while providing fine-grained, actionable uncertainty signals for downstream decision-making.

LLM UncertaintyMulti-Agent SystemsNeurIPS 2026

0 likes · 31 min read

SpanUQ: Span-Level LLM Uncertainty Estimation 10-20x Faster Than Sampling

Machine Learning Algorithms & Natural Language Processing

Oct 5, 2026 · Artificial Intelligence

Diffusion Reward Models: Learning Human Preference Distributions Beyond Scalar Scores

This paper introduces Diffusion Reward Models (DRM) that replace the scalar value head with a Diffusion Transformer to model full reward distributions, capturing multimodal human disagreement, enabling uncertainty-aware decisions, test-time scaling via repeated reward sampling, and improving downstream RLHF performance.

Distribution ModelingHuman Preference LearningMultimodal Distributions

0 likes · 20 min read

Diffusion Reward Models: Learning Human Preference Distributions Beyond Scalar Scores

IT Services Circle

Jul 1, 2026 · Artificial Intelligence

Why Gaussian Processes Beat Neural Networks for Small‑Sample Regression with Uncertainty

This article explains how Gaussian Process Regression (GPR) provides a principled Bayesian alternative to neural networks for small‑sample regression, delivering both accurate predictions and calibrated uncertainty by defining a prior over functions, using kernel composition, marginal likelihood optimization, and efficient numerical techniques.

Gaussian ProcessKernel MethodsPython

0 likes · 22 min read

Why Gaussian Processes Beat Neural Networks for Small‑Sample Regression with Uncertainty

AI Engineer Programming

Jun 29, 2026 · Artificial Intelligence

Managing LLM Hallucinations: Strategies, Metrics, and Layered Controls

The article examines why large language models hallucinate, categorizes factual, faithfulness, and reasoning hallucinations, critiques existing benchmarks, and proposes a layered governance framework—including training‑time RLHF/DPO, retrieval‑augmented generation, post‑generation verification, uncertainty quantification, and compliance considerations—to mitigate risks in production systems.

LLMRLHFUncertainty Quantification

0 likes · 13 min read

Managing LLM Hallucinations: Strategies, Metrics, and Layered Controls

AI Agent Research Hub

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.

DeepXDEPINNsUncertainty Quantification

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 RetrievalLLMUncertainty Quantification

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