REALM: Regime-Switching, Explainable, and Activation-Induced Linear Models

arXiv:2609.32141 · cs.LG, cs.AI, stat.ME, stat.ML · Submitted 2026-09-26 · Read on arXiv

cs.LG, cs.AI, stat.ME, stat.ML

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• AUC — 26× in this paper · explained in 2 episodes
• Interpretability — 20× in this paper · explained in 15 episodes
• Clustering — 17× in this paper · explained in 2 episodes
• Distillation — 14× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Ablation — 9× in this paper · explained in 1 episode
• Sparsity — 5× in this paper · explained in 4 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Mixture of Experts — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Accountability — 1× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Explainable Predictioninduced Input Clusters — 1× in this paper
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Mixture-of-Experts (MoE) Models — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes

Sources

• Explainable artificial intelligence (XAI): from inherent explainability to large language models
• ST-MoE: Designing Stable and Transferable Sparse Expert Models
• Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification
• Distilling the Knowledge in a Neural Network

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