Paper Citation Record · LEDGER

Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 38 inbound Pith citation observations for arXiv:2012.09816.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2012.09816 v3

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measured 38 of 38 standing notices

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measured 38 of 38 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:55:13.736803Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

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Outbound references

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Pith citing papers

Observation fa908253-a6f3-49aa-9a8a-f735aef5c6eb · inbound

Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition cites this paper.

Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 20

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TinyStories: How Small Can Language Models Be and Still Speak Coherent English? cites this paper.

TinyStories: How Small Can Language Models Be and Still Speak Coherent English? Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2

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Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models cites this paper.

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 13

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Observation b33b96a4-3cf4-41af-84b2-d8c087597fa8 · inbound

Self-Improvement in Language Models: The Sharpening Mechanism cites this paper.

Self-Improvement in Language Models: The Sharpening Mechanism Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2022

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On Local Overfitting and Forgetting in Deep Neural Networks cites this paper.

On Local Overfitting and Forgetting in Deep Neural Networks Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 1

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Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification cites this paper.

Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 36

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Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment cites this paper.

Efficient Logit-based Knowledge Distillation of Deep Spiking Neural Networks for Full-Range Timestep Deployment Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2

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sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging cites this paper.

sDREAMER: Self-distilled Mixture-of-Modality-Experts Transformer for Automatic Sleep Staging Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions cites this paper.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2

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Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? cites this paper.

Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2024

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Observation 028bb0b8-5f8f-4062-8df1-89ad6f74f7bb · inbound

Right Time to Learn:Promoting Generalization via Bio-inspired Spacing Effect in Knowledge Distillation cites this paper.

Right Time to Learn:Promoting Generalization via Bio-inspired Spacing Effect in Knowledge Distillation Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2015

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Model Fusion via Neuron Transplantation cites this paper.

Model Fusion via Neuron Transplantation Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

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Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods cites this paper.

Understanding Overadaptation in Supervised Fine-Tuning: The Role of Ensemble Methods Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 3

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Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models cites this paper.

Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2

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Observation 187ee80c-5b8b-4d2e-bde7-363c5159e6eb · inbound

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning cites this paper.

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 3

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Distillation of atomistic foundation models across architectures and chemical domains cites this paper.

Distillation of atomistic foundation models across architectures and chemical domains Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 31

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Observation b0c73fea-4d4e-4224-a0ba-7902fca8efe4 · inbound

Learning Causality for Modern Machine Learning cites this paper.

Learning Causality for Modern Machine Learning Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2017

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Machine Understanding of Scientific Language cites this paper.

Machine Understanding of Scientific Language Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 4

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Observation c799125e-ab5f-436e-9539-60df4ff2888d · inbound

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation cites this paper.

Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 20

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Dark Side of Modalities: Reinforced Multimodal Distillation for Multimodal Knowledge Graph Reasoning cites this paper.

Dark Side of Modalities: Reinforced Multimodal Distillation for Multimodal Knowledge Graph Reasoning Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 1

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SDD: Self-Degraded Defense against Malicious Fine-tuning cites this paper.

SDD: Self-Degraded Defense against Malicious Fine-tuning Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

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Provable Knowledge Acquisition and Extraction in One-Layer Transformers cites this paper.

Provable Knowledge Acquisition and Extraction in One-Layer Transformers Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2

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Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models cites this paper.

Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 4

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Muon in Associative Memory Learning: Training Dynamics and Scaling Laws cites this paper.

Muon in Associative Memory Learning: Training Dynamics and Scaling Laws Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 3

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Aggregate Models, Not Explanations: Improving Feature Importance Estimation cites this paper.

Aggregate Models, Not Explanations: Improving Feature Importance Estimation Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 1

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FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation cites this paper.

FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 1

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Benign Overfitting in Adversarial Training for Vision Transformers cites this paper.

Benign Overfitting in Adversarial Training for Vision Transformers Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 43

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Experience Sharing in Mutual Reinforcement Learning for Heterogeneous Language Models cites this paper.

Experience Sharing in Mutual Reinforcement Learning for Heterogeneous Language Models Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 3

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Hierarchical Mixture-of-Experts with Two-Stage Optimization cites this paper.

Hierarchical Mixture-of-Experts with Two-Stage Optimization Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 1

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Generative Diffusion Prior Distillation for Long-Context Knowledge Transfer cites this paper.

Generative Diffusion Prior Distillation for Long-Context Knowledge Transfer Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 1

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Quantifying and Defending against the Privacy Risk in Logit-based Federated Learning cites this paper.

Quantifying and Defending against the Privacy Risk in Logit-based Federated Learning Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2

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Muon Learns More Robust and Transferable Features than Adam cites this paper.

Muon Learns More Robust and Transferable Features than Adam Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 127

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Convergence of Gradient Descent for General Neural Network Architectures Beyond the NTK Regime cites this paper.

Convergence of Gradient Descent for General Neural Network Architectures Beyond the NTK Regime Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 70

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Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 47

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Multi-Turn On-Policy Distillation with Prefix Replay cites this paper.

Multi-Turn On-Policy Distillation with Prefix Replay Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 48

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A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactions cites this paper.

A Unified Approach to Interpreting Knowledge Distillation for Large Language Models via Interactions Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

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SPRKD: Effective Knowledge Distillation for Deep Neural Networks via Saddle Region Approximation cites this paper.

SPRKD: Effective Knowledge Distillation for Deep Neural Networks via Saddle Region Approximation Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

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V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control cites this paper.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 142

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