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Title: GitHub - adderbyte/Vision-Language-Models-ReadingList: Reading List for Vision Language Models

Open Graph Title: GitHub - adderbyte/Vision-Language-Models-ReadingList: Reading List for Vision Language Models

X Title: GitHub - adderbyte/Vision-Language-Models-ReadingList: Reading List for Vision Language Models

Description: Reading List for Vision Language Models. Contribute to adderbyte/Vision-Language-Models-ReadingList development by creating an account on GitHub.

Open Graph Description: Reading List for Vision Language Models. Contribute to adderbyte/Vision-Language-Models-ReadingList development by creating an account on GitHub.

X Description: Reading List for Vision Language Models. Contribute to adderbyte/Vision-Language-Models-ReadingList development by creating an account on GitHub.

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https://patch-diff.githubusercontent.com/adderbyte/Vision-Language-Models-ReadingList#preliminaries
Attention is all you needhttps://arxiv.org/pdf/1706.03762.pdf
Attention is all you need; Łukasz Kaiser | Masterclasshttps://www.youtube.com/watch?v=rBCqOTEfxvg
Attenttion Lilian Weng Bloghttps://lilianweng.github.io/posts/2018-06-24-attention/
Transformers Family Lilian Weng Bloghttps://lilianweng.github.io/posts/2020-04-07-the-transformer-family/
An introduction to Transformers Prof. Turnerhttps://arxiv.org/pdf/2304.10557.pdf
Neural Machine Translation by jointly learning to align and translate (2014)https://arxiv.org/abs/1409.0473
A survey of transformers (2021)https://arxiv.org/abs/2106.04554
Annotated Transformerhttps://nlp.seas.harvard.edu/2018/04/03/attention.html
Annotated transformer 2https://nlp.seas.harvard.edu/annotated-transformer/
LLM Reading listhttps://github.com/evanmiller/LLM-Reading-List
Auto-regressive Modelshttps://www.georgeho.org/deep-autoregressive-models/
A practical survey on Faster and lighter Transformer (2021)https://arxiv.org/abs/2103.14636
Efficient Transformer: A survey (2020)https://arxiv.org/abs/2009.06732
LoRA: Low-Rank Adaptation of language models (2021) https://arxiv.org/pdf/2106.09685.pdf
Deep Unsupervised Learning using Nonequilibrium Thermodynamics (2015)https://arxiv.org/abs/1503.03585
Scalable Diffusion Models with Transformers (2022)https://arxiv.org/abs/2212.09748
Diffusion Models: A comprehensive survey (2022)https://arxiv.org/abs/2209.00796
Diffusion models Lilian Weng Blog https://lilianweng.github.io/posts/2021-07-11-diffusion-models/
Score-Based Generative Modelling Yang Song Bloghttps://yang-song.net/blog/2021/score/
Consistency models 2023https://arxiv.org/abs/2303.01469
Tutorial for Diffusion for Imaging and Visionhttps://arxiv.org/abs/2403.18103
Survey of Contrastive Learning (Sept. 2020)https://arxiv.org/pdf/2010.05113.pdf
Simple Framework for Contrastive Learning (2020) https://arxiv.org/pdf/2002.05709.pdf
Self-supervised learning: Generative and Contrastive (2021)https://arxiv.org/pdf/2006.08218.pdf
Contrastive Learning for NLP (Full Reading list)https://github.com/ryanzhumich/Contrastive-Learning-NLP-Papers?tab=readme-ov-file#contrastive-learning-objective
Reinforcement Learning Sutton&Bartohttps://www.andrew.cmu.edu/course/10-703/textbook/BartoSutton.pdf
Reinforcement Learning: Bit by Bithttps://arxiv.org/pdf/2103.04047.pdf
A survey on model-based learning (2022) Fan-Ming et al https://arxiv.org/pdf/2206.09328.pdf
Model-based reinforcement Learning: A survey (2022) Thomas et al https://arxiv.org/pdf/2006.16712.pdf
Deep Reinforcement Learning (2023)https://arxiv.org/pdf/2201.02135.pdf
Learning to summarize from human feedback (2022) https://arxiv.org/pdf/2009.01325.pdf
Deep Reinforcement Learning from Human Preferences (2017) https://arxiv.org/pdf/1706.03741.pdf
Training language models to follow human instructionshttps://arxiv.org/pdf/2203.02155.pdf
Survey on preference-based reinforcement learning methods (2017) https://www.jmlr.org/papers/volume18/16-634/16-634.pdf
Scaling law for reward model overoptimizationhttps://arxiv.org/pdf/2210.10760.pdf
RLHF Illustrations by hugging face and referenceshttps://huggingface.co/blog/rlhf
Red Teaming LLMshttps://arxiv.org/pdf/2209.07858.pdf
https://patch-diff.githubusercontent.com/adderbyte/Vision-Language-Models-ReadingList#vision-language-models
Intro to Vision-Language Modellinghttps://arxiv.org/pdf/2405.17247
Vision-Language Pre-Training: Basics, Recent Advances, and Future Trends https://arxiv.org/pdf/2210.09263
Foundations & Trends in Multimodal Machine Learning:Principles, Challenges, and Open Questionshttps://arxiv.org/pdf/2209.03430
Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directionshttps://arxiv.org/pdf/2404.07214
VLP: A survey on Vision-Language Pre-traininghttps://arxiv.org/pdf/2202.09061
Vision-Language Pretraining: Current Trends and the Futurehttps://aclanthology.org/2022.acl-tutorials.7.pdf
A generalist agenthttps://arxiv.org/abs/2205.06175
Scaling Agent Acrosss many Simulated Worlds (SIMA)https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/sima-generalist-ai-agent-for-3d-virtual-environments/Scaling%20Instructable%20Agents%20Across%20Many%20Simulated%20Worlds.pdf
SIMA - Blog posthttps://deepmind.google/discover/blog/sima-generalist-ai-agent-for-3d-virtual-environments/?utm_source=twitter&utm_medium=social&utm_campaign=SIMA/
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