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Collections including paper arxiv:2308.00951

mixture-of-experts

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 6
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 5
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

MoEs papers reading list

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Paper • 1701.06538 • Published Jan 23, 2017 • 6
Sparse Networks from Scratch: Faster Training without Losing Performance

Paper • 1907.04840 • Published Jul 10, 2019 • 3
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Paper • 1910.02054 • Published Oct 4, 2019 • 5
A Mixture of h-1 Heads is Better than h Heads

Paper • 2005.06537 • Published May 13, 2020 • 2

AugmentedLearning

What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning

Paper • 2312.15685 • Published Dec 25, 2023 • 16
mistralai/Mixtral-8x7B-Instruct-v0.1

Text Generation • Updated Aug 19, 2024 • 557k • • 4.35k
microsoft/phi-2

Text Generation • Updated Apr 29, 2024 • 391k • • 3.29k
TinyLlama/TinyLlama-1.1B-Chat-v1.0

Text Generation • Updated Mar 17, 2024 • 1.13M • • 1.18k

From Sparse to Soft Mixtures of Experts

Paper • 2308.00951 • Published Aug 2, 2023 • 20

Foundation Machine Learning

From Sparse to Soft Mixtures of Experts

Paper • 2308.00951 • Published Aug 2, 2023 • 20
Neural Network Diffusion

Paper • 2402.13144 • Published Feb 20, 2024 • 95

QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models

Paper • 2310.16795 • Published Oct 25, 2023 • 27
Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

Paper • 2308.12066 • Published Aug 23, 2023 • 4
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference

Paper • 2303.06182 • Published Mar 10, 2023 • 1
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate

Paper • 2112.14397 • Published Dec 29, 2021 • 1

Woodpecker: Hallucination Correction for Multimodal Large Language Models

Paper • 2310.16045 • Published Oct 24, 2023 • 16
HallusionBench: You See What You Think? Or You Think What You See? An Image-Context Reasoning Benchmark Challenging for GPT-4V(ision), LLaVA-1.5, and Other Multi-modality Models

Paper • 2310.14566 • Published Oct 23, 2023 • 27
SILC: Improving Vision Language Pretraining with Self-Distillation

Paper • 2310.13355 • Published Oct 20, 2023 • 9
Conditional Diffusion Distillation

Paper • 2310.01407 • Published Oct 2, 2023 • 20

My Papers of Interest

Self-Alignment with Instruction Backtranslation

Paper • 2308.06259 • Published Aug 11, 2023 • 42
ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptation

Paper • 2308.03793 • Published Aug 4, 2023 • 11
From Sparse to Soft Mixtures of Experts

Paper • 2308.00951 • Published Aug 2, 2023 • 20
Revisiting DETR Pre-training for Object Detection

Paper • 2308.01300 • Published Aug 2, 2023 • 9

From Sparse to Soft Mixtures of Experts

Paper • 2308.00951 • Published Aug 2, 2023 • 20
LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition

Paper • 2307.13269 • Published Jul 25, 2023 • 32
S-LoRA: Serving Thousands of Concurrent LoRA Adapters

Paper • 2311.03285 • Published Nov 6, 2023 • 32

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