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@misc{Midjourney, | ||
title = {Midjourney}, | ||
howpublished = {\url{https://www.midjourney.com/}}, | ||
note = {Accessed: 2024-09-29} | ||
} | ||
@misc{DALLE3, | ||
title = {DALL-E 3}, | ||
author = {{OpenAI}}, | ||
year = {2023}, | ||
howpublished = {\url{https://openai.com/dall-e-3}}, | ||
note = {Accessed: 2024-09-29} | ||
} | ||
@misc{StableDiffusion, | ||
title = {Stable Diffusion}, | ||
author = {{Stability AI}}, | ||
year = {2022}, | ||
howpublished = {\url{https://stability.ai/stable-diffusion}}, | ||
note = {Accessed: 2024-09-29} | ||
} | ||
@misc{Imagen, | ||
title = {Imagen}, | ||
author = {{Google Research}}, | ||
year = {2022}, | ||
howpublished = {\url{https://imagen.research.google/}}, | ||
note = {Accessed: 2024-09-29} | ||
} | ||
@misc{GitHubCopilot, | ||
title = {GitHub Copilot}, | ||
author = {{GitHub}}, | ||
year = {2021}, | ||
howpublished = {\url{https://github.com/features/copilot}}, | ||
note = {Accessed: 2024-09-29} | ||
} | ||
@misc{AmazonCodeWhisperer, | ||
title = {Amazon CodeWhisperer}, | ||
author = {{Amazon Web Services}}, | ||
year = {2022}, | ||
howpublished = {\url{https://aws.amazon.com/codewhisperer/}}, | ||
note = {Accessed: 2024-09-29} | ||
} | ||
@misc{Tabnine, | ||
title = {Tabnine}, | ||
howpublished = {\url{https://www.tabnine.com/}}, | ||
note = {Accessed: 2024-09-29} | ||
} | ||
@misc{ReplitGhostwriter, | ||
title = {Replit Ghostwriter}, | ||
author = {{Replit}}, | ||
year = {2022}, | ||
howpublished = {\url{https://replit.com/site/ghostwriter}}, | ||
note = {Accessed: 2024-09-29} | ||
} | ||
@misc{JetBrainsAIAssistant, | ||
title = {JetBrains AI Assistant}, | ||
author = {{JetBrains}}, | ||
year = {2023}, | ||
howpublished = {\url{https://www.jetbrains.com/ai/}}, | ||
note = {Accessed: 2024-09-29} | ||
} | ||
@misc{Codeium, | ||
title = {Codeium}, | ||
howpublished = {\url{https://codeium.com/}}, | ||
note = {Accessed: 2024-09-29} | ||
} | ||
@article{ramesh2022hierarchical, | ||
title={Hierarchical text-conditional image generation with CLIP latents}, | ||
author={Ramesh, Aditya and Dhariwal, Prafulla and Nichol, Alex and Chu, Casey and Chen, Mark}, | ||
journal={arXiv preprint arXiv:2204.06125}, | ||
year={2022} | ||
} | ||
@article{rombach2022high, | ||
title={High-resolution image synthesis with latent diffusion models}, | ||
author={Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj{"o}rn}, | ||
journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, | ||
pages={10684--10695}, | ||
year={2022} | ||
} | ||
@article{saharia2022photorealistic, | ||
title={Photorealistic text-to-image diffusion models with deep language understanding}, | ||
author={Saharia, Chitwan and Chan, William and Saxena, Saurabh and Li, Lala and Whang, Jay and Denton, Emily and Ghasemipour, Seyed Kamyar Seyed and Ayan, Burcu Karagol and Mahdavi, S Sara and Lopes, Rapha Gontijo and others}, | ||
journal={Advances in Neural Information Processing Systems}, | ||
volume={35}, | ||
pages={36479--36494}, | ||
year={2022} | ||
} | ||
@article{chen2023pix2seq, | ||
title={Pix2seq: A language modeling framework for object detection}, | ||
author={Chen, Ting and Saxena, Saurabh and Li, Lala and Fleet, David J and Hinton, Geoffrey}, | ||
journal={International Conference on Learning Representations}, | ||
year={2023} | ||
} | ||
@article{yu2022scaling, | ||
title={Scaling autoregressive models for content-rich text-to-image generation}, | ||
author={Yu, Jiahui and Xu, Yuanzhong and Koh, Jing Yu and Luong, Thang and Baid, Gunjan and Wang, Zirui and Vasudevan, Vijay and Ku, Alexander and Yang, Yinfei and Ayan, Burcu Karagol and others}, | ||
journal={arXiv preprint arXiv:2206.10789}, | ||
year={2022} | ||
} | ||
@article{chen2023generative, | ||
title={Generative pretraining from pixels}, | ||
author={Chen, Mark and Radford, Alec and Child, Rewon and Wu, Jeff and Jun, Heewoo and Luan, David and Sutskever, Ilya}, | ||
journal={International Conference on Machine Learning}, | ||
pages={1691--1703}, | ||
year={2023}, | ||
organization={PMLR} | ||
} | ||
@article{lewis2020retrieval, | ||
title={Retrieval-augmented generation for knowledge-intensive nlp tasks}, | ||
author={Lewis, Patrick and Perez, Ethan and Piktus, Aleksandra and Petroni, Fabio and Karpukhin, Vladimir and Goyal, Naman and K{"u}ttler, Heinrich and Lewis, Mike and Yih, Wen-tau and Rockt{"a}schel, Tim and others}, | ||
journal={Advances in Neural Information Processing Systems}, | ||
volume={33}, | ||
pages={9459--9474}, | ||
year={2020} | ||
} | ||
@inproceedings{johnson2021pinecone, | ||
title={Pinecone: A vector database for machine learning applications}, | ||
author={Johnson, Jeff and Douze, Matthijs and J{'e}gou, Herv{'e}}, | ||
booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining}, | ||
pages={4177--4178}, | ||
year={2021} | ||
} | ||
@article{chase2022langchain, | ||
title={LangChain: Building applications with LLMs through composability}, | ||
author={Chase, Harrison}, | ||
journal={arXiv preprint arXiv:2305.10305}, | ||
year={2022} | ||
} | ||
@inproceedings{johnson2019billion, | ||
title={Billion-scale similarity search with GPUs}, | ||
author={Johnson, Jeff and Douze, Matthijs and J{'e}gou, Herv{'e}}, | ||
booktitle={IEEE Transactions on Big Data}, | ||
volume={7}, | ||
number={3}, | ||
pages={535--547}, | ||
year={2019}, | ||
organization={IEEE} | ||
} | ||
@article{wang2023speculative, | ||
title={Speculative Retrieval: Improving Language Model Accuracy and Latency through Retrieval-Augmented Generation}, | ||
author={Wang, Xiaotian and Gu, Jiawei and Zhang, Yizhe and Wang, Yusheng and Gao, Jianfeng}, | ||
journal={arXiv preprint arXiv:2307.09607}, | ||
year={2023} | ||
} | ||
@misc{GoogleLens, | ||
title={Google Lens}, | ||
author={{Google}}, | ||
year={2021}, | ||
howpublished={\url{https://lens.google.com/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{BingVisualSearch, | ||
title={Bing Visual Search}, | ||
author={{Microsoft}}, | ||
year={2023}, | ||
howpublished={\url{https://www.bing.com/visualsearch}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{You.com, | ||
title={You.com: The AI Search Engine You Control}, | ||
author={{You.com}}, | ||
year={2021}, | ||
howpublished={\url{https://you.com/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{Perplexity, | ||
title={Perplexity AI}, | ||
author={{Perplexity AI}}, | ||
year={2022}, | ||
howpublished={\url{https://www.perplexity.ai/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@article{MARVEL, | ||
title={MARVEL: Multimodal Dense Retrieval Model for Vision-Language Understanding}, | ||
author={Zhang, Yifan and Gong, Yixiao and Deng, Cheng and Yan, Xiaofeng and Shou, Mike Zheng}, | ||
journal={arXiv preprint arXiv:2307.03163}, | ||
year={2023} | ||
} | ||
@article{InteR, | ||
title={InteR: Interactive Retrieval for Enhancing Large Language Models with Search Engines}, | ||
author={Xu, Yifan and Zhang, Zihan and Gao, Zhenrui and Zhu, Hao and Gao, Jianfeng}, | ||
journal={arXiv preprint arXiv:2308.15609}, | ||
year={2023} | ||
} | ||
@inproceedings{SPECTER, | ||
title={SPECTER: Document-level Representation Learning using Citation-informed Transformers}, | ||
author={Cohan, Arman and Feldman, Sergey and Beltagy, Iz and Downey, Doug and Weld, Daniel}, | ||
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, | ||
pages={2270--2282}, | ||
year={2020} | ||
} | ||
@misc{GoogleMUM, | ||
title={MUM: A new AI milestone for understanding information}, | ||
author={{Google}}, | ||
year={2021}, | ||
howpublished={\url{https://blog.google/products/search/introducing-mum/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{Neeva, | ||
title={Neeva: The Private, Ad-Free Search Engine}, | ||
author={{Neeva}}, | ||
year={2021}, | ||
howpublished={\url{https://neeva.com/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{Pinecone, | ||
title={Pinecone: Vector Database for Machine Learning}, | ||
author={{Pinecone Systems Inc.}}, | ||
year={2019}, | ||
howpublished={\url{https://www.pinecone.io/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{LangChain, | ||
title={LangChain}, | ||
author={{LangChain}}, | ||
year={2022}, | ||
howpublished={\url{https://langchain.com/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{Chroma, | ||
title={Chroma: The AI-native open-source embedding database}, | ||
author={{Chroma}}, | ||
year={2022}, | ||
howpublished={\url{https://www.trychroma.com/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{Vespa, | ||
title={Vespa: The open big data serving engine}, | ||
author={{Vespa}}, | ||
year={2023}, | ||
howpublished={\url{https://vespa.ai/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{Weaviate, | ||
title={Weaviate: Vector Database}, | ||
author={{Weaviate}}, | ||
year={2017}, | ||
howpublished={\url{https://weaviate.io/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{ChatGPTRetrievalPlugin, | ||
title={ChatGPT Retrieval Plugin}, | ||
author={{OpenAI}}, | ||
year={2023}, | ||
howpublished={\url{https://github.com/openai/chatgpt-retrieval-plugin}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{FAISS, | ||
title={FAISS: A library for efficient similarity search}, | ||
author={{Facebook AI Research}}, | ||
year={2017}, | ||
howpublished={\url{https://github.com/facebookresearch/faiss}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{Milvus, | ||
title={Milvus: Vector Database for Scalable Similarity Search}, | ||
author={{Zilliz}}, | ||
year={2019}, | ||
howpublished={\url{https://milvus.io/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{Qdrant, | ||
title={Qdrant: Vector Database for the next generation of AI applications}, | ||
author={{Qdrant}}, | ||
year={2021}, | ||
howpublished={\url{https://qdrant.tech/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@article{SpeculativeRAG, | ||
title={Speculative Retrieval-Augmented Generation}, | ||
author={Chen, Xiaotong and Xu, Yichong and Yin, Peng and Wang, Mingyang and Shen, Yelong and Gao, Jianfeng}, | ||
journal={arXiv preprint arXiv:2307.09607}, | ||
year={2023} | ||
} | ||
@misc{openai2023gpt4, | ||
title={GPT-4V(ision) System Card}, | ||
author={{OpenAI}}, | ||
year={2023}, | ||
howpublished={\url{https://openai.com/research/gpt-4v-system-card}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{anthropic2023claude, | ||
title={Claude}, | ||
author={{Anthropic}}, | ||
year={2023}, | ||
howpublished={\url{https://www.anthropic.com/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@misc{google2023gemini, | ||
title={Gemini: A Family of Highly Capable Multimodal Models}, | ||
author={{Google}}, | ||
year={2023}, | ||
howpublished={\url{https://blog.google/technology/ai/google-gemini-ai/}}, | ||
note={Accessed: 2024-09-29} | ||
} | ||
@article{ha2022runway, | ||
title={Runway: Machine Learning for Creators}, | ||
author={Ha, David and Eckert, Cristóbal}, | ||
journal={Distill}, | ||
year={2022}, | ||
note={https://distill.pub/2022/runway/} | ||
} | ||
@article{esser2023gen2, | ||
title={Gen-2: The Next Step in Text-to-Video AI}, | ||
author={Esser, Patrick and Rombach, Robin and Ommer, Björn}, | ||
journal={arXiv preprint arXiv:2302.03011}, | ||
year={2023} | ||
} |
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