‘knowledge distillation’ tag

See Also
Gwern
- “Research Ideas”, Gwern 2017
Links
Miscellaneous
Bibliography

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See Also

Gwern

“Research Ideas”, Gwern 2017

Research Ideas

Links

“LoLCATs: On Low-Rank Linearizing of Large Language Models”, Zhang et al 2024

LoLCATs: On Low-Rank Linearizing of Large Language Models

“The Mamba in the Llama: Distilling and Accelerating Hybrid Models”, Wang et al 2024

The Mamba in the Llama: Distilling and Accelerating Hybrid Models

“Gemma 2: Improving Open Language Models at a Practical Size”, Riviere et al 2024

Gemma 2: Improving Open Language Models at a Practical Size

“Scaling the Codebook Size of VQGAN to 100,000 With a Utilization Rate of 99%”, Zhu et al 2024

Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%

“From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step”, Deng et al 2024

From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step

“Streamlining Redundant Layers to Compress Large Language Models”, Chen et al 2024

Streamlining Redundant Layers to Compress Large Language Models

“SDXS: Real-Time One-Step Latent Diffusion Models With Image Conditions”, Song et al 2024

SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions

“Do Not Worry If You Do Not Have Data: Building Pretrained Language Models Using Translationese”, Doshi et al 2024

Do Not Worry if You Do Not Have Data: Building Pretrained Language Models Using Translationese

“CLLMs: Consistency Large Language Models”, Kou et al 2024

CLLMs: Consistency Large Language Models

“Bridging the Gap: Sketch to Color Diffusion Model With Semantic Prompt Learning”, Wang et al 2024

Bridging the Gap: Sketch to Color Diffusion Model with Semantic Prompt Learning

“Improving Text Embeddings With Large Language Models”, Wang et al 2023

Improving Text Embeddings with Large Language Models

“ReST Meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent”, Aksitov et al 2023

ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent

“ByteDance Is Secretly Using OpenAI’s Tech to Build a Competitor”, Heath 2023

ByteDance is secretly using OpenAI’s tech to build a competitor

“SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration”, Duckworth et al 2023

SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration

“Beyond Human Data: Scaling Self-Training for Problem-Solving With Language Models (ReST^EM)”, Singh et al 2023

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models (ReST^EM)

“Generative Models: What Do They Know? Do They Know Things? Let’s Find Out!”, Du et al 2023

Generative Models: What do they know? Do they know things? Let’s find out!

“Efficient Transformer Knowledge Distillation: A Performance Review”, Brown et al 2023

Efficient Transformer Knowledge Distillation: A Performance Review

“Implicit Chain-Of-Thought Reasoning via Knowledge Distillation”, Deng et al 2023

Implicit Chain-of-Thought Reasoning via Knowledge Distillation

“Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling”, Gandhi et al 2023

Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling

“HyperFields: Towards Zero-Shot Generation of NeRFs from Text”, Babu et al 2023

HyperFields: Towards Zero-Shot Generation of NeRFs from Text

“Polynomial Time Cryptanalytic Extraction of Neural Network Models”, Shamir et al 2023

Polynomial Time Cryptanalytic Extraction of Neural Network Models

“OSD: Online Speculative Decoding”, Liu et al 2023

OSD: Online Speculative Decoding

“ReST: Reinforced Self-Training (ReST) for Language Modeling”, Gulcehre et al 2023

ReST: Reinforced Self-Training (ReST) for Language Modeling

“Composable Function-Preserving Expansions for Transformer Architectures”, Gesmundo & Maile 2023

Composable Function-preserving Expansions for Transformer Architectures

“Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events”, Gu et al 2023

Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events

“Explaining Competitive-Level Programming Solutions Using LLMs”, Li et al 2023

Explaining Competitive-Level Programming Solutions using LLMs

“GKD: Generalized Knowledge Distillation for Auto-Regressive Sequence Models”, Agarwal et al 2023

GKD: Generalized Knowledge Distillation for Auto-regressive Sequence Models

“WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia”, Semnani et al 2023

WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia

“VanillaNet: the Power of Minimalism in Deep Learning”, Chen et al 2023

VanillaNet: the Power of Minimalism in Deep Learning

“Mimetic Initialization of Self-Attention Layers”, Trockman & Kolter 2023

Mimetic Initialization of Self-Attention Layers

“TinyStories: How Small Can Language Models Be and Still Speak Coherent English?”, Eldan & Li 2023

TinyStories: How Small Can Language Models Be and Still Speak Coherent English?

“Dr. LLaMa: Improving Small Language Models in Domain-Specific QA via Generative Data Augmentation”, Guo et al 2023

Dr. LLaMa: Improving Small Language Models in Domain-Specific QA via Generative Data Augmentation

“Distilling Step-By-Step! Outperforming Larger Language Models With Less Training Data and Smaller Model Sizes”, Hsieh et al 2023

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

“LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions”, Wu et al 2023

LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions

“Learning Agile Soccer Skills for a Bipedal Robot With Deep Reinforcement Learning”, Haarnoja et al 2023

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning

“A Cookbook of Self-Supervised Learning”, Balestriero et al 2023

A Cookbook of Self-Supervised Learning

“KD-DLGAN: Data Limited Image Generation via Knowledge Distillation”, Cui et al 2023

KD-DLGAN: Data Limited Image Generation via Knowledge Distillation

“TRACT: Denoising Diffusion Models With Transitive Closure Time-Distillation”, Berthelot et al 2023

TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation

“Learning Humanoid Locomotion With Transformers”, Radosavovic et al 2023

Learning Humanoid Locomotion with Transformers

“Consistency Models”, Song et al 2023

Consistency Models

“ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”, Azerbayev et al 2023

ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics

“Scaling Vision Transformers to 22 Billion Parameters”, Dehghani et al 2023

Scaling Vision Transformers to 22 Billion Parameters

“BMT: Binarized Neural Machine Translation”, Zhang et al 2023

BMT: Binarized Neural Machine Translation

“Use GPT-3 Incorrectly: Reduce Costs 40× and Increase Speed by 5×”, Pullen 2023

Use GPT-3 incorrectly: reduce costs 40× and increase speed by 5×

“TinyMIM: An Empirical Study of Distilling MIM Pre-Trained Models”, Ren et al 2023

TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models

“Sparse Upcycling: Training Mixture-Of-Experts from Dense Checkpoints”, Komatsuzaki et al 2022

Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

“Solving Math Word Problems With Process & Outcome-Based Feedback”, Uesato et al 2022

Solving math word problems with process & outcome-based feedback

“Distilled DeepConsensus: Knowledge Distillation for Fast and Accurate DNA Sequence Correction”, Belyaeva et al 2022

Distilled DeepConsensus: Knowledge distillation for fast and accurate DNA sequence correction

“MaskDistill: A Unified View of Masked Image Modeling”, Anonymous 2022

MaskDistill: A Unified View of Masked Image Modeling

“EVA: Exploring the Limits of Masked Visual Representation Learning at Scale”, Fang et al 2022

EVA: Exploring the Limits of Masked Visual Representation Learning at Scale

“Legged Locomotion in Challenging Terrains Using Egocentric Vision”, Agarwal et al 2022

Legged Locomotion in Challenging Terrains using Egocentric Vision

“EDiff-I: Text-To-Image Diffusion Models With an Ensemble of Expert Denoisers”, Balaji et al 2022

eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

“Fast DistilBERT on CPUs”, Shen et al 2022

Fast DistilBERT on CPUs

“Large Language Models Can Self-Improve”, Huang et al 2022

Large Language Models Can Self-Improve

“Exclusive Supermask Subnetwork Training for Continual Learning”, Yadav & Bansal 2022

Exclusive Supermask Subnetwork Training for Continual Learning

“The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes”, Kocsis et al 2022

The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes

“On Distillation of Guided Diffusion Models”, Meng et al 2022

On Distillation of Guided Diffusion Models

“Small Character Models Match Large Word Models for Autocomplete Under Memory Constraints”, Jawahar et al 2022

Small Character Models Match Large Word Models for Autocomplete Under Memory Constraints

“Omnigrok: Grokking Beyond Algorithmic Data”, Liu et al 2022

Omnigrok: Grokking Beyond Algorithmic Data

“Human-Level Atari 200× Faster”, Kapturowski et al 2022

Human-level Atari 200× faster

“On the Effectiveness of Compact Biomedical Transformers (✱BioBERT)”, Rohanian et al 2022

On the Effectiveness of Compact Biomedical Transformers (✱BioBERT)

“Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members”, Cornelisse et al 2022

Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members

“Re2G: Retrieve, Rerank, Generate”, Glass et al 2022

Re2G: Retrieve, Rerank, Generate

“Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems”, FitzGerald et al 2022

Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems

“SBERT Studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features”, Opitz & Frank 2022

SBERT studies Meaning Representations: Decomposing Sentence Embeddings into Explainable Semantic Features

“ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers”, Yao et al 2022

ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers

“Dataset Condensation via Efficient Synthetic-Data Parameterization”, Kim et al 2022

Dataset Condensation via Efficient Synthetic-Data Parameterization

“UViM: A Unified Modeling Approach for Vision With Learned Guiding Codes”, Kolesnikov et al 2022

UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes

“Dialog Inpainting: Turning Documents into Dialogues”, Dai et al 2022

Dialog Inpainting: Turning Documents into Dialogues

“Solving ImageNet: a Unified Scheme for Training Any Backbone to Top Results”, Ridnik et al 2022

Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results

“STaR: Bootstrapping Reasoning With Reasoning”, Zelikman et al 2022

STaR: Bootstrapping Reasoning With Reasoning

“Knowledge Distillation: Bad Models Can Be Good Role Models”, Kaplun et al 2022

Knowledge Distillation: Bad Models Can Be Good Role Models

“PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression”, Vo et al 2022

PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression

“Self-Distilled StyleGAN: Towards Generation from Internet Photos”, Mokady et al 2022

Self-Distilled StyleGAN: Towards Generation from Internet Photos

“AutoDistil: Few-Shot Task-Agnostic Neural Architecture Search for Distilling Large Language Models”, Xu et al 2022

AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models

“DeepSpeed-MoE: Advancing Mixture-Of-Experts Inference and Training to Power Next-Generation AI Scale”, Rajbhandari et al 2022

DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

“Microdosing: Knowledge Distillation for GAN Based Compression”, Helminger et al 2022

Microdosing: Knowledge Distillation for GAN based Compression

“ERNIE 3.0 Titan: Exploring Larger-Scale Knowledge Enhanced Pre-Training for Language Understanding and Generation”, Wang et al 2021

ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation

“Amortized Noisy Channel Neural Machine Translation”, Pang et al 2021

Amortized Noisy Channel Neural Machine Translation

“Causal Distillation for Language Models”, Wu et al 2021

Causal Distillation for Language Models

“Extrapolating from a Single Image to a Thousand Classes Using Distillation”, Asano & Saeed 2021

Extrapolating from a Single Image to a Thousand Classes using Distillation

“Prune Once for All: Sparse Pre-Trained Language Models”, Zafrir et al 2021

Prune Once for All: Sparse Pre-Trained Language Models

“Training Verifiers to Solve Math Word Problems”, Cobbe et al 2021

Training Verifiers to Solve Math Word Problems

“Wav2CLIP: Learning Robust Audio Representations From CLIP”, Wu et al 2021

Wav2CLIP: Learning Robust Audio Representations From CLIP

“When in Doubt, Summon the Titans: Efficient Inference With Large Models”, Rawat et al 2021

When in Doubt, Summon the Titans: Efficient Inference with Large Models

“Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora”, Jin et al 2021

Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora

“Symbolic Knowledge Distillation: from General Language Models to Commonsense Models”, West et al 2021

Symbolic Knowledge Distillation: from General Language Models to Commonsense Models

“Language Modeling via Learning to Rank”, Frydenlund et al 2021

Language Modeling via Learning to Rank

“Beyond Pick-And-Place: Tackling Robotic Stacking of Diverse Shapes”, Lee et al 2021

Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes

“Unsupervised Neural Machine Translation With Generative Language Models Only”, Han et al 2021

Unsupervised Neural Machine Translation with Generative Language Models Only

“OTTER: Data Efficient Language-Supervised Zero-Shot Recognition With Optimal Transport Distillation”, Wu et al 2021

OTTER: Data Efficient Language-Supervised Zero-Shot Recognition with Optimal Transport Distillation

“Progressive Distillation for Fast Sampling of Diffusion Models”, Salimans & Ho 2021

Progressive Distillation for Fast Sampling of Diffusion Models

“On the Interplay Between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis”, Lai et al 2021

On the Interplay Between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis

“ZSD-YOLO: Zero-Shot YOLO Detection Using Vision-Language Knowledge Distillation”, Xie & Zheng 2021

ZSD-YOLO: Zero-Shot YOLO Detection using Vision-Language Knowledge Distillation

“Beyond Distillation: Task-Level Mixture-Of-Experts (TaskMoE) for Efficient Inference”, Kudugunta et al 2021

Beyond Distillation: Task-level Mixture-of-Experts (TaskMoE) for Efficient Inference

“SPLADE V2: Sparse Lexical and Expansion Model for Information Retrieval”, Formal et al 2021

SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval

“KroneckerBERT: Learning Kronecker Decomposition for Pre-Trained Language Models via Knowledge Distillation”, Tahaei et al 2021

KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation

“Multi-Task Self-Training for Learning General Representations”, Ghiasi et al 2021

Multi-Task Self-Training for Learning General Representations

“Dataset Distillation With Infinitely Wide Convolutional Networks”, Nguyen et al 2021

Dataset Distillation with Infinitely Wide Convolutional Networks

“Knowledge-Adaptation Priors”, Khan & Swaroop 2021

Knowledge-Adaptation Priors

“Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better”, Menghani 2021

Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better

“Knowledge Distillation: A Good Teacher Is Patient and Consistent”, Beyer et al 2021

Knowledge distillation: A good teacher is patient and consistent

“ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training”, Touvron et al 2021

ResMLP: Feedforward networks for image classification with data-efficient training

“DINO: Emerging Properties in Self-Supervised Vision Transformers”, Caron et al 2021

DINO: Emerging Properties in Self-Supervised Vision Transformers

“Zero-Shot Detection via Vision and Language Knowledge Distillation”, Gu et al 2021

Zero-Shot Detection via Vision and Language Knowledge Distillation

“Data-Efficient Language-Supervised Zero-Shot Learning With Self-Distillation”, Cheng et al 2021

Data-Efficient Language-Supervised Zero-Shot Learning with Self-Distillation

“ALD: Efficient Transformers in Reinforcement Learning Using Actor-Learner Distillation”, Parisotto & Salakhutdinov 2021

ALD: Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation

“KiloNeRF: Speeding up Neural Radiance Fields With Thousands of Tiny MLPs”, Reiser et al 2021

KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs

“China’s GPT-3? BAAI Introduces Superscale Intelligence Model ‘Wu Dao 1.0’: The Beijing Academy of Artificial Intelligence (BAAI) Releases Wu Dao 1.0, China’s First Large-Scale Pretraining Model.”, Synced 2021

China’s GPT-3? BAAI Introduces Superscale Intelligence Model ‘Wu Dao 1.0’: The Beijing Academy of Artificial Intelligence (BAAI) releases Wu Dao 1.0, China’s first large-scale pretraining model.

“Distilling Large Language Models into Tiny and Effective Students Using PQRNN”, Kaliamoorthi et al 2021

Distilling Large Language Models into Tiny and Effective Students using pQRNN

“Training Data-Efficient Image Transformers & Distillation through Attention”, Touvron et al 2020

Training data-efficient image transformers & distillation through attention

“Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning”, Allen-Zhu & Li 2020

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

“Towards Playing Full MOBA Games With Deep Reinforcement Learning”, Ye et al 2020

Towards Playing Full MOBA Games with Deep Reinforcement Learning

“A Primer in BERTology: What We Know about How BERT Works”, Rogers et al 2020

A Primer in BERTology: What we know about how BERT works

“Dataset Meta-Learning from Kernel Ridge-Regression”, Nguyen et al 2020

Dataset Meta-Learning from Kernel Ridge-Regression

“TernaryBERT: Distillation-Aware Ultra-Low Bit BERT”, Zhang et al 2020

TernaryBERT: Distillation-aware Ultra-low Bit BERT

“SimCLRv2: Big Self-Supervised Models Are Strong Semi-Supervised Learners”, Chen et al 2020

SimCLRv2: Big Self-Supervised Models are Strong Semi-Supervised Learners

“Movement Pruning: Adaptive Sparsity by Fine-Tuning”, Sanh et al 2020

Movement Pruning: Adaptive Sparsity by Fine-Tuning

“General Purpose Text Embeddings from Pre-Trained Language Models for Scalable Inference”, Du et al 2020

General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference

“Cryptanalytic Extraction of Neural Network Models”, Carlini et al 2020

Cryptanalytic Extraction of Neural Network Models

“MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers”, Wang et al 2020

MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

“Towards a Conversational Agent That Can Chat About…Anything”, Adiwardana & Luong 2020

Towards a Conversational Agent that Can Chat About…Anything

“Understanding the Generalization of ‘Lottery Tickets’ in Neural Networks”, Morcos & Tian 2019

Understanding the generalization of ‘lottery tickets’ in neural networks

“Self-Training With Noisy Student Improves ImageNet Classification”, Xie et al 2019

Self-training with Noisy Student improves ImageNet classification

“On Warm-Starting Neural Network Training”, Ash & Adams 2019

On Warm-Starting Neural Network Training

“DistilBERT, a Distilled Version of BERT: Smaller, Faster, Cheaper and Lighter”, Sanh et al 2019

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

“TinyBERT: Distilling BERT for Natural Language Understanding”, Jiao et al 2019

TinyBERT: Distilling BERT for Natural Language Understanding

“Smaller, Faster, Cheaper, Lighter: Introducing DistilGPT, a Distilled Version of GPT”, Sanh 2019

Smaller, faster, cheaper, lighter: Introducing DistilGPT, a distilled version of GPT

“Well-Read Students Learn Better: On the Importance of Pre-Training Compact Models”, Turc et al 2019

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

“ICML 2019 Notes”, Abel 2019

ICML 2019 Notes

“NoGAN: Decrappification, DeOldification, and Super Resolution”, Antic et al 2019

NoGAN: Decrappification, DeOldification, and Super Resolution

“Mask-Predict: Parallel Decoding of Conditional Masked Language Models”, Ghazvininejad et al 2019

Mask-Predict: Parallel Decoding of Conditional Masked Language Models

“Distilling Policy Distillation”, Czarnecki et al 2019

Distilling Policy Distillation

“Compressing GANs Using Knowledge Distillation”, Aguinaldo et al 2019

Compressing GANs using Knowledge Distillation

“Neural Probabilistic Motor Primitives for Humanoid Control”, Merel et al 2018

Neural probabilistic motor primitives for humanoid control

“Dataset Distillation”, Wang et al 2018

Dataset Distillation

“Exploration by Random Network Distillation”, Burda et al 2018

Exploration by Random Network Distillation

“OCD: Optimal Completion Distillation for Sequence Learning”, Sabour et al 2018

OCD: Optimal Completion Distillation for Sequence Learning

“Network Recasting: A Universal Method for Network Architecture Transformation”, Yu et al 2018

Network Recasting: A Universal Method for Network Architecture Transformation

“ClariNet: Parallel Wave Generation in End-To-End Text-To-Speech”, Ping et al 2018

ClariNet: Parallel Wave Generation in End-to-End Text-to-Speech

“Self-Net: Lifelong Learning via Continual Self-Modeling”, Camp et al 2018

Self-Net: Lifelong Learning via Continual Self-Modeling

“Self-Distillation: Born Again Neural Networks”, Furlanello et al 2018

Self-distillation: Born Again Neural Networks

“Kickstarting Deep Reinforcement Learning”, Schmitt et al 2018

Kickstarting Deep Reinforcement Learning

“Faster Gaze Prediction With Dense Networks and Fisher Pruning”, Theis et al 2018

Faster gaze prediction with dense networks and Fisher pruning

“Parallel WaveNet: Fast High-Fidelity Speech Synthesis”, Oord et al 2017

Parallel WaveNet: Fast High-Fidelity Speech Synthesis

“Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN”, Gao et al 2017

Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN

“Policy Optimization by Genetic Distillation”, Gangwani & Peng 2017

Policy Optimization by Genetic Distillation

“N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning”, Ashok et al 2017

N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning

“Training Shallow and Thin Networks for Acceleration via Knowledge Distillation With Conditional Adversarial Networks”, Xu et al 2017

Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks

“Distral: Robust Multitask Reinforcement Learning”, Teh et al 2017

Distral: Robust Multitask Reinforcement Learning

“Biased Importance Sampling for Deep Neural Network Training”, Katharopoulos & Fleuret 2017

Biased Importance Sampling for Deep Neural Network Training

“Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer”, Zagoruyko & Komodakis 2016

Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

“FractalNet: Ultra-Deep Neural Networks without Residuals”, Larsson et al 2016

FractalNet: Ultra-Deep Neural Networks without Residuals

“Do Deep Convolutional Nets Really Need to Be Deep and Convolutional?”, Urban et al 2016

Do Deep Convolutional Nets Really Need to be Deep and Convolutional?

“Face Model Compression by Distilling Knowledge from Neurons”, Luo et al 2016

Face Model Compression by Distilling Knowledge from Neurons

“Policy Distillation”, Rusu et al 2015

Policy Distillation

“Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning”, Parisotto et al 2015

Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

“Net2Net: Accelerating Learning via Knowledge Transfer”, Chen et al 2015

Net2Net: Accelerating Learning via Knowledge Transfer

“Bayesian Dark Knowledge”, Korattikara et al 2015

Bayesian Dark Knowledge

“Distilling the Knowledge in a Neural Network”, Hinton et al 2015

Distilling the Knowledge in a Neural Network

“FitNets: Hints for Thin Deep Nets”, Romero et al 2014

FitNets: Hints for Thin Deep Nets

“Do Deep Nets Really Need to Be Deep?”, Ba & Caruana 2013

Do Deep Nets Really Need to be Deep?

“Model Compression”, Bucila 2006

Model Compression

“Learning Complex, Extended Sequences Using the Principle of History Compression”, Schmidhuber 1992

Learning Complex, Extended Sequences Using the Principle of History Compression

“Dota 2 With Large Scale Deep Reinforcement Learning § Pg11”, Rerun 2024 (page 11 org openai)

Dota 2 with Large Scale Deep Reinforcement Learning § pg11⁠:

View PDF:

/doc/www/arxiv.org/2dcf2c6e7f5e36e4ae4e9e3a498d0b2124399287.pdf#page=11&org=openai

“Google DeepMind’s Grandmaster-Level Chess Without Search”

Google DeepMind’s Grandmaster-Level Chess Without Search

“From Vision to Language: Semi-Supervised Learning in Action…at Scale”

From Vision to Language: Semi-Supervised Learning in Action…at Scale

Wikipedia

Knowledge distillation

Miscellaneous

Bibliography

https://arxiv.org/abs/2408.15237: “The Mamba in the Llama: Distilling and Accelerating Hybrid Models”, Junxiong Wang, Daniele Paliotta, Avner May, Alexander M. Rush, Tri Dao

link-bibliography
https://arxiv.org/abs/2408.00118#google: “Gemma 2: Improving Open Language Models at a Practical Size”, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, Peter Liu, Pouya Tafti, Abe Friesen, Michelle Casbon, Sabela Ramos, Ravin Kumar, Charline Le Lan, Sammy Jerome, Anton Tsitsulin, Nino Vieillard, Piotr Stanczyk, Sertan Girgin, Nikola Momchev, Matt Hoffman, Shantanu Thakoor, Jean-Bastien Grill, Behnam Neyshabur, Olivier Bachem, Alanna Walton, Aliaksei Severyn, Alicia Parrish, Aliya Ahmad, Allen Hutchison, Alvin Abdagic, Amanda Carl, Amy Shen, Andy Brock, Andy Coenen, Anthony Laforge, Antonia Paterson, Ben Bastian, Bilal Piot, Bo Wu, Brandon Royal, Charlie Chen, Chintu Kumar, Chris Perry, Chris Welty, Christopher A. Choquette-Choo, Danila Sinopalnikov, David Weinberger, Dimple Vijaykumar, Dominika Rogozińska, Dustin Herbison, Elisa Bandy, Emma Wang, Eric Noland, Erica Moreira, Evan Senter, Evgenii Eltyshev, Francesco Visin, Gabriel Rasskin, Gary Wei, Glenn Cameron, Gus Martins, Hadi Hashemi, Hanna Klimczak-Plucińska, Harleen Batra, Harsh Dhand, Ivan Nardini, Jacinda Mein, Jack Zhou, James Svensson, Jeff Stanway, Jetha Chan, Jin Peng Zhou, Joana Carrasqueira, Joana Iljazi, Jocelyn Becker, Joe Fernandez, Joost van Amersfoort, Josh Gordon, Josh Lipschultz, Josh Newlan, Ju-yeong Ji, Kareem Mohamed, Kartikeya Badola, Kat Black, Katie Millican, Keelin McDonell, Kelvin Nguyen, Kiranbir Sodhia, Kish Greene, Lars Lowe Sjoesund, Lauren Usui, Laurent Sifre, Lena Heuermann, Leticia Lago, Lilly McNealus, Livio Baldini Soares, Logan Kilpatrick, Lucas Dixon, Luciano Martins, Machel Reid, Manvinder Singh, Mark Iverson, Martin Görner, Mat Velloso, Mateo Wirth, Matt Davidow, Matt Miller, Matthew Rahtz, Matthew Watson, Meg Risdal, Mehran Kazemi, Michael Moynihan, Ming Zhang, Minsuk Kahng, Minwoo Park, Mofi Rahman, Mohit Khatwani, Natalie Dao, Nenshad Bardoliwalla, Nesh Devanathan, Neta Dumai, Nilay Chauhan, Oscar Wahltinez, Pankil Botarda, Parker Barnes, Paul Barham, Paul Michel, Pengchong Jin, Petko Georgiev, Phil Culliton, Pradeep Kuppala, Ramona Comanescu, Ramona Merhej, Reena Jana, Reza Ardeshir Rokni, Rishabh Agarwal, Ryan Mullins, Samaneh Saadat, Sara Mc Carthy, Sarah Cogan, Sarah Perrin, Sébastien M. R. Arnold, Sebastian Krause, Shengyang Dai, Shruti Garg, Shruti Sheth, Sue Ronstrom, Susan Chan, Timothy Jordan, Ting Yu, Tom Eccles, Tom Hennigan, Tomas Kocisky, Tulsee Doshi, Vihan Jain, Vikas Yadav, Vilobh Meshram, Vishal Dharmadhikari, Warren Barkley, Wei Wei, Wenming Ye, Woohyun Han, Woosuk Kwon, Xiang Xu, Zhe Shen, Zhitao Gong, Zichuan Wei, Victor Cotruta, Phoebe Kirk, Anand Rao, Minh Giang, Ludovic Peran, Tris Warkentin, Eli Collins, Joelle Barral, Zoubin Ghahramani, Raia Hadsell, D. Sculley, Jeanine Banks, Anca Dragan, Slav Petrov, Oriol Vinyals, Jeff Dean, Demis Hassabis, Koray Kavukcuoglu, Clement Farabet, Elena Buchatskaya, Sebastian Borgeaud, Noah Fiedel, Armand Joulin, Kathleen Kenealy, Robert Dadashi, Alek Andreev

link-bibliography
https://arxiv.org/abs/2406.11837: “Scaling the Codebook Size of VQGAN to 100,000 With a Utilization Rate of 99%”, Lei Zhu, Fangyun Wei, Yanye Lu, Dong Chen

link-bibliography
https://arxiv.org/abs/2405.14838: “From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step”, Yuntian Deng, Yejin Choi, Stuart Shieber

link-bibliography
https://arxiv.org/abs/2312.06585#deepmind: “Beyond Human Data: Scaling Self-Training for Problem-Solving With Language Models (ReST^EM)”, Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

link-bibliography
https://arxiv.org/abs/2311.13657: “Efficient Transformer Knowledge Distillation: A Performance Review”, Nathan Brown, Ashton Williamson, Tahj Anderson, Logan Lawrence

link-bibliography
https://arxiv.org/abs/2310.08708: “Polynomial Time Cryptanalytic Extraction of Neural Network Models”, Adi Shamir, Isaac Canales-Martinez, Anna Hambitzer, Jorge Chavez-Saab, Francisco Rodrigez-Henriquez, Nitin Satpute

link-bibliography
https://arxiv.org/abs/2307.06439#microsoft: “Distilling Large Language Models for Biomedical Knowledge Extraction: A Case Study on Adverse Drug Events”, Yu Gu, Sheng Zhang, Naoto Usuyama, Yonas Woldesenbet, Cliff Wong, Praneeth Sanapathi, Mu Wei, Naveen Valluri, Erika Strandberg, Tristan Naumann, Hoifung Poon

link-bibliography
https://arxiv.org/abs/2305.12972: “VanillaNet: the Power of Minimalism in Deep Learning”, Hanting Chen, Yunhe Wang, Jianyuan Guo, Dacheng Tao

link-bibliography
https://arxiv.org/abs/2305.09828: “Mimetic Initialization of Self-Attention Layers”, Asher Trockman, J. Zico Kolter

link-bibliography
https://arxiv.org/abs/2305.07759#microsoft: “TinyStories: How Small Can Language Models Be and Still Speak Coherent English?”, Ronen Eldan, Yuanzhi Li

link-bibliography
https://arxiv.org/abs/2305.07804: “Dr. LLaMa: Improving Small Language Models in Domain-Specific QA via Generative Data Augmentation”, Zhen Guo, Peiqi Wang, Yanwei Wang, Shangdi Yu

link-bibliography
https://arxiv.org/abs/2305.02301#google: “Distilling Step-By-Step! Outperforming Larger Language Models With Less Training Data and Smaller Model Sizes”, Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, Tomas Pfister

link-bibliography
https://arxiv.org/abs/2304.13653#deepmind: “Learning Agile Soccer Skills for a Bipedal Robot With Deep Reinforcement Learning”, Tuomas Haarnoja, Ben Moran, Guy Lever, Sandy H. Huang, Dhruva Tirumala, Markus Wulfmeier, Jan Humplik, Saran Tunyasuvunakool, Noah Y. Siegel, Roland Hafner, Michael Bloesch, Kristian Hartikainen, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber, Nicole Hurley, Francesco Nori, Raia Hadsell, Nicolas Heess

link-bibliography
https://arxiv.org/abs/2303.01469#openai: “Consistency Models”, Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever

link-bibliography
https://arxiv.org/abs/2302.12433: “ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”, Zhangir Azerbayev, Bartosz Piotrowski, Hailey Schoelkopf, Edward W. Ayers, Dragomir Radev, Jeremy Avigad

link-bibliography
https://arxiv.org/abs/2302.05442#google: “Scaling Vision Transformers to 22 Billion Parameters”, Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetić, Dustin Tran, Thomas Kipf, Mario Lučić, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, Neil Houlsby

link-bibliography
https://arxiv.org/abs/2302.04907#google: “BMT: Binarized Neural Machine Translation”, Yichi Zhang, Ankush Garg, Yuan Cao, Łukasz Lew, Behrooz Ghorbani, Zhiru Zhang, Orhan Firat

link-bibliography
https://arxiv.org/abs/2301.01296#microsoft: “TinyMIM: An Empirical Study of Distilling MIM Pre-Trained Models”, Sucheng Ren, Fangyun Wei, Zheng Zhang, Han Hu

link-bibliography
https://arxiv.org/abs/2212.05055#google: “Sparse Upcycling: Training Mixture-Of-Experts from Dense Checkpoints”, Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme Ruiz, Basil Mustafa, Joshua Ainslie, Yi Tay, Mostafa Dehghani, Neil Houlsby

link-bibliography
https://openreview.net/forum?id=wmGlMhaBe0: “MaskDistill: A Unified View of Masked Image Modeling”, Anonymous

link-bibliography
https://arxiv.org/abs/2211.07636#baai: “EVA: Exploring the Limits of Masked Visual Representation Learning at Scale”, Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun, Ledell Wu, Xinggang Wang, Tiejun Huang, Xinlong Wang, Yue Cao

link-bibliography
https://arxiv.org/abs/2211.07638: “Legged Locomotion in Challenging Terrains Using Egocentric Vision”, Ananye Agarwal, Ashish Kumar, Jitendra Malik, Deepak Pathak

link-bibliography
https://arxiv.org/abs/2211.01324#nvidia: “EDiff-I: Text-To-Image Diffusion Models With an Ensemble of Expert Denoisers”, Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, Ming-Yu Liu

link-bibliography
https://arxiv.org/abs/2210.11610#google: “Large Language Models Can Self-Improve”, Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han

link-bibliography
https://arxiv.org/abs/2210.03142#google: “On Distillation of Guided Diffusion Models”, Chenlin Meng, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans

link-bibliography
https://arxiv.org/abs/2210.01117: “Omnigrok: Grokking Beyond Algorithmic Data”, Ziming Liu, Eric J. Michaud, Max Tegmark

link-bibliography
https://arxiv.org/abs/2209.07550#deepmind: “Human-Level Atari 200× Faster”, Steven Kapturowski, Víctor Campos, Ray Jiang, Nemanja Rakićević, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia

link-bibliography
https://arxiv.org/abs/2207.06300#ibm: “Re2G: Retrieve, Rerank, Generate”, Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Rajaram Naik, Pengshan Cai, Alfio Gliozzo

link-bibliography
https://arxiv.org/abs/2206.07808#amazon: “Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems”, Jack FitzGerald, Shankar Ananthakrishnan, Konstantine Arkoudas, Davide Bernardi, Abhishek Bhagia, Claudio Delli Bovi, Jin Cao, Rakesh Chada, Amit Chauhan, Luoxin Chen, Anurag Dwarakanath, Satyam Dwivedi, Turan Gojayev, Karthik Gopalakrishnan, Thomas Gueudre, Dilek Hakkani-Tur, Wael Hamza, Jonathan Hueser, Kevin Martin Jose, Haidar Khan, Beiye Liu, Jianhua Lu, Alessandro Manzotti, Pradeep Natarajan, Karolina Owczarzak, Gokmen Oz, Enrico Palumbo, Charith Peris, Chandana Satya Prakash, Stephen Rawls, Andy Rosenbaum, Anjali Shenoy, Saleh Soltan, Mukund Harakere Sridhar, Liz Tan, Fabian Triefenbach, Pan Wei, Haiyang Yu, Shuai Zheng, Gokhan Tur, Prem Natarajan

link-bibliography
https://arxiv.org/abs/2206.01861#microsoft: “ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers”, Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He

link-bibliography
https://arxiv.org/abs/2205.09073#google: “Dialog Inpainting: Turning Documents into Dialogues”, Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, Kelvin Guu

link-bibliography
https://arxiv.org/abs/2204.03475#alibaba: “Solving ImageNet: a Unified Scheme for Training Any Backbone to Top Results”, Tal Ridnik, Hussam Lawen, Emanuel Ben-Baruch, Asaf Noy

link-bibliography
https://arxiv.org/abs/2202.12211#google: “Self-Distilled StyleGAN: Towards Generation from Internet Photos”, Ron Mokady, Michal Yarom, Omer Tov, Oran Lang, Daniel Cohen-Or, Tali Dekel, Michal Irani, Inbar Mosseri

link-bibliography
https://arxiv.org/abs/2201.05596#microsoft: “DeepSpeed-MoE: Advancing Mixture-Of-Experts Inference and Training to Power Next-Generation AI Scale”, Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He

link-bibliography
https://arxiv.org/abs/2111.05754: “Prune Once for All: Sparse Pre-Trained Language Models”, Ofir Zafrir, Ariel Larey, Guy Boudoukh, Haihao Shen, Moshe Wasserblat

link-bibliography
https://arxiv.org/abs/2110.14168#openai: “Training Verifiers to Solve Math Word Problems”, Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman

link-bibliography
https://arxiv.org/abs/2110.06961: “Language Modeling via Learning to Rank”, Arvid Frydenlund, Gagandeep Singh, Frank Rudzicz

link-bibliography
https://openreview.net/forum?id=G89-1yZLFHk: “OTTER: Data Efficient Language-Supervised Zero-Shot Recognition With Optimal Transport Distillation”, Bichen Wu, Ruizhe Cheng, Peizhao Zhang, Peter Vajda, Joseph E. Gonzalez

link-bibliography
https://arxiv.org/abs/2109.12066: “ZSD-YOLO: Zero-Shot YOLO Detection Using Vision-Language Knowledge Distillation”, Johnathan Xie, Shuai Zheng

link-bibliography
https://arxiv.org/abs/2109.06243#huawei: “KroneckerBERT: Learning Kronecker Decomposition for Pre-Trained Language Models via Knowledge Distillation”, Marzieh S. Tahaei, Ella Charlaix, Vahid Partovi Nia, Ali Ghodsi, Mehdi Rezagholizadeh

link-bibliography
https://arxiv.org/abs/2106.05237#google: “Knowledge Distillation: A Good Teacher Is Patient and Consistent”, Lucas Beyer, Xiaohua Zhai, Amélie Royer, Larisa Markeeva, Rohan Anil, Alexander Kolesnikov

link-bibliography
https://arxiv.org/abs/2104.14294#facebook: “DINO: Emerging Properties in Self-Supervised Vision Transformers”, Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Arm Holdings, Joulin

link-bibliography
https://arxiv.org/abs/2104.13921#google: “Zero-Shot Detection via Vision and Language Knowledge Distillation”, Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, Yin Cui

link-bibliography
https://arxiv.org/abs/2104.08945#facebook: “Data-Efficient Language-Supervised Zero-Shot Learning With Self-Distillation”, Ruizhe Cheng, Bichen Wu, Peizhao Zhang, Peter Vajda, Joseph E. Gonzalez

link-bibliography
https://syncedreview.com/2021/03/23/chinas-gpt-3-baai-introduces-superscale-intelligence-model-wu-dao-1-0/#baai: “China’s GPT-3? BAAI Introduces Superscale Intelligence Model ‘Wu Dao 1.0’: The Beijing Academy of Artificial Intelligence (BAAI) Releases Wu Dao 1.0, China’s First Large-Scale Pretraining Model.”, Synced

link-bibliography
https://arxiv.org/abs/2012.12877#facebook: “Training Data-Efficient Image Transformers & Distillation through Attention”, Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou

link-bibliography
https://arxiv.org/abs/2011.12692#tencent: “Towards Playing Full MOBA Games With Deep Reinforcement Learning”, Deheng Ye, Guibin Chen, Wen Zhang, Sheng Chen, Bo Yuan, Bo Liu, Jia Chen, Zhao Liu, Fuhao Qiu, Hongsheng Yu, Yinyuting Yin, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang, Wei Liu

link-bibliography
https://arxiv.org/abs/2002.10957#microsoft: “MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers”, Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, Ming Zhou

link-bibliography
https://research.google/blog/towards-a-conversational-agent-that-can-chat-aboutanything/: “Towards a Conversational Agent That Can Chat About…Anything”, Daniel Adiwardana, Thang Luong

link-bibliography
https://arxiv.org/abs/1911.04252#google: “Self-Training With Noisy Student Improves ImageNet Classification”, Qizhe Xie, Minh-Thang Luong, Eduard Hovy, Quoc V. Le

link-bibliography
https://arxiv.org/abs/1909.10351: “TinyBERT: Distilling BERT for Natural Language Understanding”, Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, Qun Liu

link-bibliography
https://david-abel.github.io/notes/icml_2019.pdf: “ICML 2019 Notes”, David Abel

link-bibliography
https://arxiv.org/abs/1902.02186#deepmind: “Distilling Policy Distillation”, Wojciech Marian Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant M. Jayakumar, Grzegorz Swirszcz, Max Jaderberg

link-bibliography
2016-luo.pdf: “Face Model Compression by Distilling Knowledge from Neurons”, Ping Luo, Zhenyao Zhu, Ziwei Liu, Xiaogang Wang, Xiaoou Tang

link-bibliography

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