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Mahalanobis distance

3 articles · Page 1 of 1

Sep 30, 2026 · Artificial Intelligence

ME-Decoding: Reframing LLM Decoding as Ensemble Pruning with Semantic Redundancy Control

Renmin University researchers propose ME-Decoding, a method that selects candidate tokens by jointly optimizing probability confidence and semantic diversity via Mahalanobis distance, outperforming probability-only truncation on reasoning and open-ended generation across multiple models and temperatures with only 3% latency overhead.

EMNLP 2026LLM decodingMahalanobis distance

0 likes · 11 min read

ME-Decoding: Reframing LLM Decoding as Ensemble Pruning with Semantic Redundancy Control

Machine Heart

May 22, 2026 · Artificial Intelligence

Breaking the Echo Chamber: MP‑MoE Introduces Ensemble‑Pruning for Diverse Experts

The paper presents MP‑MoE, a new Mixture‑of‑Experts architecture that replaces top‑k routing with Mahalanobis‑based ensemble pruning, explicitly encouraging expert diversity via a co‑occurrence matrix, and uses an efficient greedy algorithm with incremental Cholesky updates, achieving higher performance with minimal training overhead and no inference cost.

Expert DiversityICML 2026MP-MoE

0 likes · 8 min read

Breaking the Echo Chamber: MP‑MoE Introduces Ensemble‑Pruning for Diverse Experts

Qunar Tech Salon

Mar 14, 2015 · Artificial Intelligence

Common Distance and Similarity Measures in Machine Learning and Data Mining

This article reviews the most frequently used distance and similarity formulas in machine learning and data mining, explaining their definitions, mathematical properties, practical examples, and when each metric is appropriate for measuring differences between data points or probability distributions.

KL DivergenceMahalanobis distancecosine similarity

0 likes · 13 min read

Common Distance and Similarity Measures in Machine Learning and Data Mining