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Collections including paper arxiv:2212.05055

A collection of arXiv papers from Chip Huyen's AI Engineering organized by chapter and ordered by when each appears in the book.

Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning

Paper • 2211.04325 • Published Oct 26, 2022
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Paper • 1810.04805 • Published Oct 11, 2018 • 17
On the Opportunities and Risks of Foundation Models

Paper • 2108.07258 • Published Aug 16, 2021
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

Paper • 2204.07705 • Published Apr 16, 2022 • 1

Foundation AI Papers

Curated List of Must-Reads on LLM reasoning at Temus AI team

Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Paper • 2310.04406 • Published Oct 6, 2023 • 9
Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15, 2024 • 105
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization

Paper • 2402.09320 • Published Feb 14, 2024 • 6
Self-Discover: Large Language Models Self-Compose Reasoning Structures

Paper • 2402.03620 • Published Feb 6, 2024 • 115

Model Architecture

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

Paper • 2212.05055 • Published Dec 9, 2022 • 5

Papers - University of Georgia Tech

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

Paper • 2212.05055 • Published Dec 9, 2022 • 5
Social Skill Training with Large Language Models

Paper • 2404.04204 • Published Apr 5, 2024 • 16

Papers - T5 - MoE

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

Paper • 2212.05055 • Published Dec 9, 2022 • 5

Upcycling Papers

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

Paper • 2212.05055 • Published Dec 9, 2022 • 5
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks

Paper • 2401.02731 • Published Jan 5, 2024 • 2

Papers - Google

Lumiere: A Space-Time Diffusion Model for Video Generation

Paper • 2401.12945 • Published Jan 23, 2024 • 85
Long-form factuality in large language models

Paper • 2403.18802 • Published Mar 27, 2024 • 25
ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion

Paper • 2403.18818 • Published Mar 27, 2024 • 26
TC4D: Trajectory-Conditioned Text-to-4D Generation

Paper • 2403.17920 • Published Mar 26, 2024 • 18

Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping

Paper • 2402.14083 • Published Feb 21, 2024 • 48
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Paper • 2305.13245 • Published May 22, 2023 • 5
Training a T5 Using Lab-sized Resources

Paper • 2208.12097 • Published Aug 25, 2022 • 1
Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Paper • 2212.05055 • Published Dec 9, 2022 • 5

Larimar: Large Language Models with Episodic Memory Control

Paper • 2403.11901 • Published Mar 18, 2024 • 33
Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Paper • 2212.05055 • Published Dec 9, 2022 • 5
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

Paper • 2404.02258 • Published Apr 2, 2024 • 104
Multi-Head Mixture-of-Experts

Paper • 2404.15045 • Published Apr 23, 2024 • 60

Papers - Image - MoE

Robust Mixture-of-Expert Training for Convolutional Neural Networks

Paper • 2308.10110 • Published Aug 19, 2023 • 2
HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion

Paper • 2308.06512 • Published Aug 12, 2023 • 2
Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts

Paper • 2309.04354 • Published Sep 8, 2023 • 15
Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Paper • 2212.05055 • Published Dec 9, 2022 • 5

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