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Diffusion models have achieved remarkable success in image and video generation.
Chaos in random neural networks
Haim Sompolinsky, Andrea Crisanti, and Hans-Jurgen Sommers · 1988
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Stochastic neural networks
Eugene Wong · 1991
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Feed forward neural networks with random weights
Wouter F Schmidt, Martin A Kraaijveld, Robert PW Duin, et al · 1992
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Neural network ensembles, cross validation, and active learning
Anders Krogh and Jesper Vedelsby · 1994
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Network information criterion-determining the number of hidden units for an artificial neural network model
Noboru Murata, Shuji Yoshizawa, and Shun-ichi Amari · 1994
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Equilibrium free-energy differences from nonequilibrium measurements: A master-equation approach
Christopher Jarzynski · 1997
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Practical variational inference for neural networks
Alex Graves · 2011
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Bayesian learning for neural networks
Radford M Neal · 2012
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Predicting parameters in deep learning
Misha Denil, Babak Shakibi, Laurent Dinh, Marc’Aurelio Ranzato, and Nando De Freitas · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 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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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Variational dropout and the local reparameterization trick
Durk P Kingma, Tim Salimans, and Max Welling · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Cited alongside, same era.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Cited alongside, same era.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Analytic-DPM: an analytic estimate of the optimal reverse variance in diffusion probabilistic models
Fan Bao, Chongxuan Li, Jun Zhu, and Bo Zhang · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Imagen video: High definition video generation with diffusion models
Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al · 2022
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A convnet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Hypernetworks
David Ha, Andrew M. Dai, and Quoc V. Le · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Cited alongside, same era.
SMASH: One-shot model architecture search through hypernetworks
Andrew Brock, Theo Lim, J.M. Ritchie, and Nick Weston · 2018
Cited alongside, same era.
Graph hypernetworks for neural architecture search
Chris Zhang, Mengye Ren, and Raquel Urtasun · 2018
Cited alongside, same era.
DPM-solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2022
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Learning to learn with generative models of neural network checkpoints
William Peebles, Ilija Radosavovic, Tim Brooks, Alexei Efros, and Jitendra Malik · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-resolution image synthesis with latent diffusion models
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
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 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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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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Hypertransformer: Model generation for supervised and semi-supervised few-shot learning
Andrey Zhmoginov, Mark Sandler, and Maksym Vladymyrov · 2022
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NeRN: Learning neural representations for neural networks
Maor Ashkenazi, Zohar Rimon, Ron Vainshtein, Shir Levi, Elad Richardson, Pinchas Mintz, and Eran Treister · 2023
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Diffusion-sdf: Conditional generative modeling of signed distance functions
Gene Chou, Yuval Bahat, and Felix Heide · 2023
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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
Berthy T Feng, Jamie Smith, Michael Rubinstein, Huiwen Chang, Katherine L Bouman, and William T Freeman · 2023
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Prompt-to-prompt image editing with cross-attention control
Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-or · 2023
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Can we scale transformers to predict parameters of diverse imagenet models?
Boris Knyazev, Doha Hwang, and Simon Lacoste-Julien · 2023
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Your diffusion model is secretly a zero-shot classifier
Alexander C Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, and Deepak Pathak · 2023
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Equivariant architectures for learning in deep weight spaces
Aviv Navon, Aviv Shamsian, Idan Achituve, Ethan Fetaya, Gal Chechik, and Haggai Maron · 2023
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Towards scalable and versatile weight space learning
Konstantin Schürholt, Michael W Mahoney, and Damian Borth · 2024
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Metadiff: Meta-learning with conditional diffusion for few-shot learning
Baoquan Zhang and Demin Yu · 2024
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