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Generative models have achieved remarkable success in image, video, and text domains.
Principal components analysis (pca)
Andrzej Maćkiewicz and Waldemar Ratajczak · 1993
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts · 2013
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Conditional Generative Adversarial Nets, November 2014
Mehdi Mirza and Simon Osindero · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Large-scale Classification of Fine-Art Paintings: Learning The Right Metric on The Right Feature, May 2015
Babak Saleh and Ahmed Elgammal · 2015
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Learning Structured Output Representation using Deep Conditional Generative Models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Image Style Transfer Using Convolutional Neural Networks
Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge · 2016
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David Ha, Andrew Dai, and Quoc V Le · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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An overview of gradient descent optimization algorithms
Sebastian Ruder · 2016
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Attribute2Image: Conditional Image Generation from Visual Attributes, October 2016
Xinchen Yan, Jimei Yang, Kihyuk Sohn, and Honglak Lee · 2016
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Sts benchmark
Daniel Cer, Mona Diab, Eneko Agirre, Inigo Lopez-Gazpio, and Lucia Specia · 2017
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, July 2017
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Mrpc: An r package for accurate inference of causal graphs, 2018
Md. Bahadur Badsha, Evan A Martin, and Audrey Qiuyan Fu · 2018
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How to Read Paintings: Semantic Art Understanding with Multi-Modal Retrieval, October 2018
Noa Garcia and George Vogiatzis · 2018
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Image-to-Image Translation with Conditional Adversarial Networks, November 2018
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
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Learning to Learn with Generative Models of Neural Network Checkpoints, September 2022
William Peebles, Ilija Radosavovic, Tim Brooks, Alexei A. Efros, and Jitendra Malik · 2022
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Hierarchical Text-Conditional Image Generation with CLIP Latents, April 2022
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-Resolution Image Synthesis with Latent Diffusion Models, April 2022
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding, May 2022
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi · 2022
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Hyper-representations as generative models: Sampling unseen neural network weights
Konstantin Schürholt, Boris Knyazev, Xavier Giró-i Nieto, and Damian Borth · 2022
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A Style-Based Generator Architecture for Generative Adversarial Networks, March 2019
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Denoising Diffusion Probabilistic Models, December 2020
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, August 2020
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros · 2020
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Diffusion Models Beat GANs on Image Synthesis, June 2021
Prafulla Dhariwal and Alex Nichol · 2021
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Denoising Diffusion Implicit Models, October 2022
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Diffusion Models for Medical Anomaly Detection, October 2022
Julia Wolleb, Florentin Bieder, Robin Sandkühler, and Philippe C. Cattin · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
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Hyperdiffusion: Generating implicit neural fields with weight-space diffusion
Ziya Erkoç, Fangchang Ma, Qi Shan, Matthias Nießner, and Angela Dai · 2023
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Score-Based Diffusion Models as Principled Priors for Inverse Imaging, August 2023
Berthy T. Feng, Jamie Smith, Michael Rubinstein, Huiwen Chang, Katherine L. Bouman, and William T. Freeman · 2023
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Scalable Diffusion Models with Transformers, March 2023
William Peebles and Saining Xie · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
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Pixart-$\alpha$: Fast training of diffusion transformer for photorealistic text-to-image synthesis
Junsong Chen, Jincheng YU, Chongjian GE, Lewei Yao, Enze Xie, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li · 2024
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Multiverse: Exposing large language model alignment problems in diverse worlds
Xiaolong Jin, Zhuo Zhang, and Xiangyu Zhang · 2024
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Sora, 2024
OpenAI · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Diffusion-based neural network weights generation
Bedionita Soro, Bruno Andreis, Hayeon Lee, Song Chong, Frank Hutter, and Sung Ju Hwang · 2024
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Kai Wang, Zhaopan Xu, Yukun Zhou, Zelin Zang, Trevor Darrell, Zhuang Liu, and Yang You · 2024
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