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Collections including paper arxiv:2403.07508

Papers - MoE - Research

Adaptive sequential Monte Carlo by means of mixture of experts

Paper • 1108.2836 • Published Aug 14, 2011 • 2
Convergence Rates for Mixture-of-Experts

Paper • 1110.2058 • Published Oct 10, 2011 • 2
Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs

Paper • 2310.12008 • Published Oct 18, 2023 • 2
Enhancing NeRF akin to Enhancing LLMs: Generalizable NeRF Transformer with Mixture-of-View-Experts

Paper • 2308.11793 • Published Aug 22, 2023 • 2

Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts

Paper • 2009.10622 • Published Sep 22, 2020 • 1
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models

Paper • 2401.15947 • Published Jan 29 • 47
MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts

Paper • 2401.04081 • Published Jan 8 • 68
MoE-Infinity: Activation-Aware Expert Offloading for Efficient MoE Serving

Paper • 2401.14361 • Published Jan 25 • 2

ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment

Paper • 2403.05135 • Published Mar 8 • 40
VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models

Paper • 2403.05438 • Published Mar 8 • 15
MoAI: Mixture of All Intelligence for Large Language and Vision Models

Paper • 2403.07508 • Published Mar 12 • 73
Branch-Train-MiX: Mixing Expert LLMs into a Mixture-of-Experts LLM

Paper • 2403.07816 • Published Mar 12 • 37

MM-LLMs: Recent Advances in MultiModal Large Language Models

Paper • 2401.13601 • Published Jan 24 • 41
MoAI: Mixture of All Intelligence for Large Language and Vision Models

Paper • 2403.07508 • Published Mar 12 • 73

Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters

Paper • 2403.02677 • Published Mar 5 • 16
Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models

Paper • 2403.03003 • Published Mar 5 • 8
InfiMM-HD: A Leap Forward in High-Resolution Multimodal Understanding

Paper • 2403.01487 • Published Mar 3 • 14
VisionLLaMA: A Unified LLaMA Interface for Vision Tasks

Paper • 2403.00522 • Published Mar 1 • 40

Interesting things.

AtP*: An efficient and scalable method for localizing LLM behaviour to components

Paper • 2403.00745 • Published Mar 1 • 8
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 574
MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT

Paper • 2402.16840 • Published Feb 26 • 23
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens

Paper • 2402.13753 • Published Feb 21 • 106

Beyond Language Models: Byte Models are Digital World Simulators

Paper • 2402.19155 • Published Feb 29 • 46
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29 • 50
VisionLLaMA: A Unified LLaMA Interface for Vision Tasks

Paper • 2403.00522 • Published Mar 1 • 40
Resonance RoPE: Improving Context Length Generalization of Large Language Models

Paper • 2403.00071 • Published Feb 29 • 19

applicable to dryft

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 574
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

Paper • 2402.14905 • Published Feb 22 • 81
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information

Paper • 2402.13616 • Published Feb 21 • 44
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers

Paper • 2402.19479 • Published Feb 29 • 30

MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs

Paper • 2402.15627 • Published Feb 23 • 32
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Paper • 2402.17177 • Published Feb 27 • 88
Beyond Language Models: Byte Models are Digital World Simulators

Paper • 2402.19155 • Published Feb 29 • 46
Hydragen: High-Throughput LLM Inference with Shared Prefixes

Paper • 2402.05099 • Published Feb 7 • 17

Divide-or-Conquer? Which Part Should You Distill Your LLM?

Paper • 2402.15000 • Published Feb 22 • 22
Adding NVMe SSDs to Enable and Accelerate 100B Model Fine-tuning on a Single GPU

Paper • 2403.06504 • Published Mar 11 • 52
MoAI: Mixture of All Intelligence for Large Language and Vision Models

Paper • 2403.07508 • Published Mar 12 • 73
AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks

Paper • 2403.14468 • Published Mar 21 • 18

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