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Conceptual Foundations of Knowledge Distillation The Teacher-Student Paradigm: An Intellectual History Knowledge Distillation (KD) is a model compression and knowledge transfer technique framed within the “teacher-student” paradigm.1 In this framework, Read More …
The Imperative for Model Compression in Modern Deep Learning The discipline of model compression has transitioned from a niche optimization concern to a critical enabler for the practical deployment of Read More …
Section 1: The Principle and Genesis of Knowledge Distillation 1.1. The Imperative for Model Efficiency: Computational Constraints in Modern AI The field of artificial intelligence has witnessed remarkable progress, largely Read More …
The Anatomy of LLM Inference and Its Intrinsic Bottlenecks The deployment of Large Language Models (LLMs) in production environments has shifted the focus of the machine learning community from training-centric Read More …
Section 1: The Imperative for Model Compression in the Era of Large-Scale AI 1.1 The Paradox of Scale in Modern AI The contemporary landscape of artificial intelligence is dominated by Read More …
Executive Summary The proliferation of the Internet of Things (IoT) and the demand for real-time, privacy-preserving artificial intelligence (AI) have catalyzed a paradigm shift from cloud-centric computation to on-device AI, Read More …
Introduction: Beyond Classical Knowledge Distillation Knowledge Distillation (KD) has emerged as a cornerstone technique in machine learning, fundamentally addressing the tension between model performance and deployment efficiency.1 As deep neural Read More …