% Converted from the original thebibliography block in section5/aml_notes.tex.

@misc{alexnet,
  note = {Krizhevsky A, Sutskever I, Hinton G E. ImageNet Classification with Deep Convolutional Neural Networks. NeurIPS, 2012.}
}

@misc{vgg,
  note = {Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition (VGG). ICLR, 2015.}
}

@misc{resnet,
  note = {He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. CVPR, 2016.}
}

@misc{vit,
  note = {Dosovitskiy A, et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR, 2021.}
}

@misc{simclr,
  note = {Chen T, et al. A Simple Framework for Contrastive Learning of Visual Representations (SimCLR). ICML, 2020.}
}

@misc{moco,
  note = {He K, et al. Momentum Contrast for Unsupervised Visual Representation Learning (MoCo). CVPR, 2020.}
}

@misc{dino,
  note = {Caron M, et al. Emerging Properties in Self-Supervised Vision Transformers (DINO). ICCV, 2021.}
}

@misc{cpc,
  note = {van den Oord A, Li Y, Vinyals O. Representation Learning with Contrastive Predictive Coding (CPC). arXiv:1807.03748, 2018.}
}

@misc{wav2vec2,
  note = {Baevski A, Zhou H, Mohamed A, Auli M. wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations. NeurIPS, 2020.}
}

@misc{hubert,
  note = {Hsu W N, et al. HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2021.}
}

@misc{data2vec,
  note = {Baevski A, et al. data2vec: A General Framework for Self-Supervised Learning in Speech, Vision and Language. ICML, 2022.}
}

@misc{mae,
  note = {He K, et al. Masked Autoencoders Are Scalable Vision Learners (MAE). CVPR, 2022.}
}

@misc{beit,
  note = {Bao H, Dong L, Piao S, Wei F. BEiT: BERT Pre-Training of Image Transformers. ICLR, 2022.}
}

@misc{clip,
  note = {Radford A, et al. Learning Transferable Visual Models From Natural Language Supervision (CLIP). ICML, 2021.}
}

@misc{align,
  note = {Jia C, et al. Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision (ALIGN). ICML, 2021.}
}

@misc{siglip,
  note = {Zhai X, et al. Sigmoid Loss for Language Image Pre-Training (SigLIP). ICCV, 2023.}
}

@misc{waco,
  note = {Ouyang S, Ye R, Li L. WACO: Word-Aligned Contrastive Learning for Speech Translation. ACL, 2023.}
}

@misc{clap,
  note = {Elizalde B, Deshmukh S, Al Ismail M, Wang H. CLAP: Learning Audio Concepts From Natural Language Supervision. ICASSP, 2023.}
}

@misc{flamingo,
  note = {Alayrac J B, et al. Flamingo: a Visual Language Model for Few-Shot Learning. NeurIPS, 2022.}
}

@misc{blip2,
  note = {Li J, et al. BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models. ICML, 2023.}
}

@misc{llava,
  note = {Liu H, et al. Visual Instruction Tuning (LLaVA). NeurIPS, 2023.}
}

@misc{instructblip,
  note = {Dai W, et al. InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning. NeurIPS, 2023.}
}

@misc{qwenvl,
  note = {Bai J, et al. Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond. arXiv:2308.12966, 2023.}
}

@misc{whisper,
  note = {Radford A, et al. Robust Speech Recognition via Large-Scale Weak Supervision (Whisper). ICML, 2023.}
}

@misc{speechgpt,
  note = {Zhang D, et al. SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities. Findings of EMNLP, 2023.}
}

@misc{qwenaudio,
  note = {Chu Y, et al. Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models. arXiv:2311.07919, 2023.}
}

@misc{salmonn,
  note = {Tang C, et al. SALMONN: Towards Generic Hearing Abilities for Large Language Models. ICLR, 2024.}
}

@misc{emu,
  note = {Sun Q, et al. Emu: Generative Pretraining in Multimodality. ICLR, 2024.}
}

@misc{gan,
  note = {Goodfellow I, et al. Generative Adversarial Nets. NeurIPS, 2014.}
}

@misc{vae,
  note = {Kingma D P, Welling M. Auto-Encoding Variational Bayes (VAE). ICLR, 2014.}
}

@misc{ddpm,
  note = {Ho J, Jain A, Abbeel P. Denoising Diffusion Probabilistic Models (DDPM). NeurIPS, 2020.}
}

@misc{cfg,
  note = {Ho J, Salimans T. Classifier-Free Diffusion Guidance. arXiv:2207.12598, 2022.}
}

@misc{dalle,
  note = {Ramesh A, et al. Zero-Shot Text-to-Image Generation (DALL·E). ICML, 2021.}
}

@misc{glide,
  note = {Nichol A, et al. GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models. ICML, 2022.}
}

@misc{dalle2,
  note = {Ramesh A, et al. Hierarchical Text-Conditional Image Generation with CLIP Latents (DALL·E 2). arXiv:2204.06125, 2022.}
}

@misc{imagen,
  note = {Saharia C, et al. Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (Imagen). NeurIPS, 2022.}
}

@misc{ldm,
  note = {Rombach R, et al. High-Resolution Image Synthesis with Latent Diffusion Models (Stable Diffusion). CVPR, 2022.}
}

@misc{sdedit,
  note = {Meng C, et al. SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations. ICLR, 2022.}
}

@misc{p2p,
  note = {Hertz A, et al. Prompt-to-Prompt Image Editing with Cross Attention Control. ICLR, 2023.}
}

@misc{ip2p,
  note = {Brooks T, Holynski A, Efros A A. InstructPix2Pix: Learning to Follow Image Editing Instructions. CVPR, 2023.}
}

@misc{controlnet,
  note = {Zhang L, Rao A, Agrawala M. Adding Conditional Control to Text-to-Image Diffusion Models (ControlNet). ICCV, 2023.}
}

@misc{tacotron2,
  note = {Shen J, et al. Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions (Tacotron 2). ICASSP, 2018.}
}

@misc{fastspeech2,
  note = {Ren Y, et al. FastSpeech 2: Fast and High-Quality End-to-End Text to Speech. ICLR, 2021.}
}

@misc{vits,
  note = {Kim J, Kong J, Son J. Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech (VITS). ICML, 2021.}
}

@misc{valle,
  note = {Wang C, et al. Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers (VALL-E). arXiv:2301.02111, 2023.}
}

@misc{nist-synthetic,
  note = {National Institute of Standards and Technology. Reducing Risks Posed by Synthetic Content. NIST AI 100-4, 2024.}
}

@misc{dreambooth,
  note = {Ruiz N, et al. DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. CVPR, 2023.}
}

@misc{audiogen,
  note = {Kreuk F, et al. AudioGen: Textually Guided Audio Generation. ICLR, 2023.}
}

@misc{audioldm,
  note = {Liu H, et al. AudioLDM: Text-to-Audio Generation with Latent Diffusion Models. ICML, 2023.}
}

@misc{makeanaudio,
  note = {Huang R, et al. Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models. ICML, 2023.}
}

@misc{makeanaudio2,
  note = {Huang J, et al. Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation. arXiv:2305.18474, 2023.}
}

@misc{audit,
  note = {Wang Y, et al. AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models. NeurIPS, 2023.}
}

@misc{codi,
  note = {Tang Z, et al. Any-to-Any Generation via Composable Diffusion (CoDi). NeurIPS, 2023.}
}
