BibBase brubaker, m

2023 (6)

Can deep neural networks for intrinsic image decomposition model human lightness constancy?. Flachot, A.; Patel, J.; Patel, K.; Wallis, T. S.; Brubaker, M. A; Brainard, D. H; and Murray, R. F Journal of Vision, 23(9): 5467–5467. 2023.
link bibtex

@article{Flachot2023Can,
  title={Can deep neural networks for intrinsic image decomposition model human lightness constancy?},
  author={Flachot, Alban and Patel, Jaykishan and Patel, Khushbu and Wallis, Tom SA and Brubaker, Marcus A and Brainard, David H and Murray, Richard F},
  journal={Journal of Vision},
  volume={23},
  number={9},
  pages={5467--5467},
  year={2023},
  publisher={The Association for Research in Vision and Ophthalmology}
}

A deep convolutional neural network trained to infer surface reflectance is deceived by mid-level lightness illusions. Patel, J.; Flachot, A.; Vazquez-Corral, J.; Brainard, D. H; Wallis, T. S.; Brubaker, M. A; and Murray, R. F Journal of Vision, 23(9): 4817–4817. 2023.
link bibtex

@article{Patel2023Deep,
  title={A deep convolutional neural network trained to infer surface reflectance is deceived by mid-level lightness illusions},
  author={Patel, Jaykishan and Flachot, Alban and Vazquez-Corral, Javier and Brainard, David H and Wallis, Thomas SA and Brubaker, Marcus A and Murray, Richard F},
  journal={Journal of Vision},
  volume={23},
  number={9},
  pages={4817--4817},
  year={2023},
  publisher={The Association for Research in Vision and Ophthalmology}
}

Long-Term Photometric Consistent Novel View Synthesis with Diffusion Models. Yu, J. J.; Forghani, F.; Derpanis, K. G.; and Brubaker, M. A. In Proceedings of the International Conference on Computer Vision (ICCV), 2023.

Long-Term Photometric Consistent Novel View Synthesis with Diffusion Models [link]

arxiv link bibtex 15 downloads

@inproceedings{Yu2023LongTerm,
      title={Long-Term Photometric Consistent Novel View Synthesis with Diffusion Models}, 
      author={Jason J. Yu and Fereshteh Forghani and Konstantinos G. Derpanis and Marcus A. Brubaker},
      year={2023},
      booktitle=ICCV,
      eprint={2304.10700},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url_arXiv = {https://arxiv.org/abs/2304.10700}
}

Reference-guided Controllable Inpainting of Neural Radiance Fields. Mirzaei, A.; Aumentado-Armstrong, T.; Brubaker, M. A.; Kelly, J.; Levinshtein, A.; Derpanis, K. G.; and Gilitschenski, I. In Proceedings of the International Conference on Computer Vision (ICCV), 2023.

Reference-guided Controllable Inpainting of Neural Radiance Fields [link]

arxiv

website link bibtex 2 downloads

@inproceedings{Mirzaei2023ReferenceGuided,
      title={Reference-guided Controllable Inpainting of Neural Radiance Fields}, 
      author={Ashkan Mirzaei and Tristan Aumentado-Armstrong and Marcus A. Brubaker and Jonathan Kelly and Alex Levinshtein and Konstantinos G. Derpanis and Igor Gilitschenski},
      year={2023},
      booktitle=ICCV,
      eprint={2304.09677},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url_arXiv = {https://arxiv.org/abs/2304.09677},
      url_Website = {https://ashmrz.github.io/reference-guided-3d/}
}

SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields [link]

arxiv link bibtex 6 downloads

Exploring Exchangeable Dataset Amortization for Bayesian Posterior Inference. In ICML Workshop on Structured Probabilistic Inference & Generative Modelling, 2023.
link bibtex

2022 (9)

Efficient CDF Approximations for Normalizing Flows. Sastry, C. S.; Lehrmann, A.; Brubaker, M. A.; and Radovic, A. Transactions on Machine Learning Research (TMLR). 2022.

Efficient CDF Approximations for Normalizing Flows [link]

Paper

arxiv link bibtex 19 downloads

@article{Sastry2022Efficient,
      title={{Efficient CDF Approximations for Normalizing Flows}},
      author={Chandramouli Shama Sastry and Andreas Lehrmann and Marcus A. Brubaker and Alexander Radovic},
      journal=TMLR,
      year={2022},
      eprint={2202.11322},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://openreview.net/forum?id=LnjclqBl8R},
      url_arXiv={https://arxiv.org/abs/2202.11322}
}

Residual Multiplicative Filter Networks for Multiscale Reconstruction. Shekarforoush, S.; Lindell, D. B.; Fleet, D. J.; and Brubaker, M. A. In Neural Information Processing Systems (NeurIPS), 2022.

Residual Multiplicative Filter Networks for Multiscale Reconstruction [link]

arxiv link bibtex 7 downloads

@inproceedings{Shekarforoush2022ResidualMFN,
      title = {Residual Multiplicative Filter Networks for Multiscale Reconstruction},
      author = {Shekarforoush, Shayan and Lindell, David B. and Fleet, David J. and Brubaker, Marcus A.},
      year = {2022},
      booktitle=NeurIPS,
      eprint={2206.00746},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url_arXiv = {https://arxiv.org/abs/2206.00746},
}

Neural Image Representations for Multi-Image Fusion and Layer Separation. Nam, S.; Brubaker, M. A.; and Brown, M. S. In Proceedings of the European Conference on Computer Vision (ECCV), 2022.

Neural Image Representations for Multi-Image Fusion and Layer Separation [link]

arxiv

website link bibtex 8 downloads

@inproceedings{Nam2022Neural,
      title={Neural Image Representations for Multi-Image Fusion and Layer Separation}, 
      author={Seonghyeon Nam and Marcus A. Brubaker and Michael S. Brown},
      year={2022},
      booktitle=ECCV,
      eprint={2108.01199},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url_arXiv = {https://arxiv.org/abs/2108.01199},
      url_Website = {http://snam.ml/research/nir}
}

Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images. Maleky, A.; Kousha, S.; Brown, M. S.; and Brubaker, M. A. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images [link]

arxiv

website link bibtex 31 downloads

@inproceedings{Maleky2022Noise2NoiseFlow,
	title={{Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images}},
	author={Ali Maleky and Shayan Kousha and Michael S. Brown and Marcus A. Brubaker},
	year={2022},
	booktitle=CVPR,
	eprint={2206.01103},
	archivePrefix={arXiv},
	primaryClass={cs.CV},
	url_arXiv = {https://arxiv.org/abs/2206.01103},
	url_Website = {https://yorkucvil.github.io/Noise2NoiseFlow/},
}

Modeling sRGB Camera Noise with Normalizing Flows. Kousha, S.; Maleky, A.; Brown, M. S.; and Brubaker, M. A. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

Modeling sRGB Camera Noise with Normalizing Flows [link]

arxiv

website link bibtex 10 downloads

@inproceedings{Kousha2022ModelingsRGB,
	title={{Modeling sRGB Camera Noise with Normalizing Flows}},
	author={Shayan Kousha and Ali Maleky and Michael S. Brown and Marcus A. Brubaker},
	year={2022},
	booktitle=CVPR,
	eprint={2206.00812},
	archivePrefix={arXiv},
	primaryClass={cs.CV},
	url_arXiv = {https://arxiv.org/abs/2206.00812},
	url_Website = {https://yorkucvil.github.io/sRGBNoise/},
}

Learning sRGB-to-Raw De-rendering with Content-Aware Metadata. Nam, S.; Punnappurath, A.; Brubaker, M. A.; and Brown, M. S. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

Learning sRGB-to-Raw De-rendering with Content-Aware Metadata [link]

arxiv

code link bibtex 3 downloads

@inproceedings{Nam2022Learning,
	title={{Learning sRGB-to-Raw De-rendering with Content-Aware Metadata}},
	author={Seonghyeon Nam and Abhijith Punnappurath and Marcus A. Brubaker and Michael S. Brown},
	year={2022},
	booktitle=CVPR,
	eprint={2206.01813},
	archivePrefix={arXiv},
	primaryClass={eess.IV},
	url_arXiv = {https://arxiv.org/abs/2206.01813},
	url_Code = {https://github.com/SamsungLabs/content-aware-metadata},
}

Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals. Brofos, J. A.; Gabrié, M.; Brubaker, M. A.; and Lederman, R. R. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals [link]

arxiv link bibtex 2 downloads

@inproceedings{Brofos2021Adaptation,
      title={Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals}, 
      author={James A. Brofos and Marylou Gabri\'{e} and Marcus A. Brubaker and Roy R. Lederman},
      year={2022},
      booktitle=AISTATS,
      eprint={2110.13216},
      archivePrefix={arXiv},
      primaryClass={stat.CO},
      url_arXiv={https://arxiv.org/abs/2110.13216}
}

Auto White-Balance Correction for Mixed-Illuminant Scenes. Afifi, M.; Brubaker, M. A.; and Brown, M. S. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), 2022.

Auto White-Balance Correction for Mixed-Illuminant Scenes [link]

arxiv

code link bibtex 9 downloads

@inproceedings{AfifiWACV2021Auto,
      title={Auto White-Balance Correction for Mixed-Illuminant Scenes}, 
      author={Mahmoud Afifi and Marcus A. Brubaker and Michael S. Brown},
      year={2022},
      booktitle=WACV,
      eprint={2109.08750},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url_arXiv = {https://arxiv.org/abs/2109.08750},
      url_Code = {https://github.com/mahmoudnafifi/mixedillWB}
}

Physics aware inference for the cryo-EM inverse problem: anisotropic network model heterogeneity, global pose and microscope defocus. Woollard, G.; Shekarforoush, S.; Wood, F.; Brubaker, M. A.; and Duc, K. D. In NeurIPS Workshop on Machine Learning for Structural Biology, 2022.

Physics aware inference for the cryo-EM inverse problem: anisotropic network model heterogeneity, global pose and microscope defocus [link]

paper link bibtex 2 downloads

@inproceedings{woollard2022physics,
  title={Physics aware inference for the cryo-EM inverse problem: anisotropic network model heterogeneity, global pose and microscope defocus},
  author={Woollard, Geoffrey and Shekarforoush, Shayan and Wood, Frank and Brubaker, Marcus A. and Duc, Khanh Dao},
  booktitle={{NeurIPS Workshop on Machine Learning for Structural Biology}},
  year={2022},
  url_Paper={https://openreview.net/pdf?id=g0UBB4QLiEQ}
}

2021 (10)

Continuous Latent Process Flows. Deng, R.; Brubaker, M. A.; Mori, G.; and Lehrmann, A. In Neural Information Processing Systems (NeurIPS), 2021.

arxiv link bibtex 13 downloads

@inproceedings{DengNeurIPS2021Continuous,
  title={Continuous Latent Process Flows},
  author={Ruizhi Deng and Marcus A. Brubaker and Greg Mori and Andreas Lehrmann},
  year={2021},
  booktitle = NeurIPS,
  eprint={2106.15580},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url_arXiv = {https://arxiv.org/abs/2106.15580},
}

HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms. Afifi, M.; Brubaker, M. A.; and Brown, M. S. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms [link]

arxiv link bibtex 20 downloads

@inproceedings{Afifi2021HistoGAN,
  title={HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms}, 
  author={Mahmoud Afifi and Marcus A. Brubaker and Michael S. Brown},
  booktitle=CVPR,
  year={2021},
  eprint={2011.11731},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url_arXiv = {https://arxiv.org/abs/2010.13821},
}

Manifold Density Estimation via Generalized Dequantization. Brofos, J. A.; Brubaker, M. A.; and Lederman, R. R. In ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 2021.

Manifold Density Estimation via Generalized Dequantization [link]

arxiv

code link bibtex 7 downloads

@inproceedings{Brofos2021ICMLWINNFManifold,
  title={Manifold Density Estimation via Generalized Dequantization},
  author={James A. Brofos and Marcus A. Brubaker and Roy R. Lederman},
  year={2021},
  booktitle = {{ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models}},
  eprint={2102.07143},
  archivePrefix={arXiv},
  primaryClass={stat.ML},
  url_arXiv = {https://arxiv.org/abs/2102.07143},
  url_Code = {https://github.com/JamesBrofos/Manifold-Dequantization}
}

Agent Forecasting at Flexible Horizons using ODE Flows. Radovic, A.; He, J.; Ramanan, J.; Brubaker, M. A.; and Lehrmann, A. In ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models, 2021.

Agent Forecasting at Flexible Horizons using ODE Flows [link]

paper link bibtex

@inproceedings{Radovic2021ICMLWINNFAgent,
  title={Agent Forecasting at Flexible Horizons using ODE Flows},
  author={Alexander Radovic and Jiawei He and Janahan Ramanan and Marcus A. Brubaker and Andreas Lehrmann},
  year={2021},
  booktitle = {{ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models}},
  url_Paper = {https://openreview.net/forum?id=MvjsWTCfXpA},
}

Continuous Latent Process Flows. Deng, R.; Brubaker, M. A.; Mori, G.; and Lehrmann, A. In ICML Workshop on Time Series, 2021.

arxiv link bibtex 13 downloads

@inproceedings{Deng2021ICMLWTSContinuous,
  title={Continuous Latent Process Flows},
  author={Ruizhi Deng and Marcus A. Brubaker and Greg Mori and Andreas Lehrmann},
  year={2021},
  booktitle = {{ICML Workshop on Time Series}},
  eprint={2106.15580},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url_arXiv = {https://arxiv.org/abs/2106.15580},
}

Zero-shot Learning with Class Description Regularization. Kousha, S.; and Brubaker, M. A. In CVPR Workshop on Fine-Grained Visual Categorization, 2021.

Zero-shot Learning with Class Description Regularization [link]

arxiv link bibtex 4 downloads

@inproceedings{Kousha2021CVPRWFGVCZeroshot,
  title={Zero-shot Learning with Class Description Regularization},
  author={Shayan Kousha and Marcus A. Brubaker},
  year={2021},
  booktitle = {{CVPR Workshop on Fine-Grained Visual Categorization}},
  eprint={2106.16108},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url_arXiv = {https://arxiv.org/abs/2106.16108},
}

Equivariant Finite Normalizing Flows. Bose, A. J.; Brubaker, M. A.; and Kobyzev, I. 2021.

Equivariant Finite Normalizing Flows [link]

arxiv link bibtex 1 download

@misc{Bose2021EquivariantFlows,
  author = {Bose, Avishek Joey and Brubaker, Marcus A. and Kobyzev, Ivan},
  title = {Equivariant Finite Normalizing Flows},
  eprint={2110.08649},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  year = {2021},
  url_arXiv = {https://arxiv.org/abs/2110.08649},
}

CAMS: Color-Aware Multi-Style Transfer. Afifi, M.; Abuolaim, A.; Hussien, M.; Brubaker, M. A.; and Brown, M. S. 2021.

CAMS: Color-Aware Multi-Style Transfer [link]

arxiv

code link bibtex 5 downloads

@misc{Afifi2021CAMS,
      title={CAMS: Color-Aware Multi-Style Transfer}, 
      author={Mahmoud Afifi and Abdullah Abuolaim and Mostafa Hussien and Marcus A. Brubaker and Michael S. Brown},
      year={2021},
      eprint={2106.13920},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url_arXiv = {https://arxiv.org/abs/2106.13920},
      url_Code = {https://github.com/mahmoudnafifi/color-aware-style-transfer}
}

Differential Privacy Tutorial II: Machine Learning and Data Generation. Sharma, G.; Hegde, N.; Prince, S. J.; and Brubaker, M. A. March 2021.

Differential Privacy Tutorial II: Machine Learning and Data Generation [link]

blog link bibtex 7 downloads

@misc{SharmaDPTutorialPartII2021,
    title={{Differential Privacy Tutorial II: Machine Learning and Data Generation}},
    author={Guarav Sharma and Nidhi Hegde and Simon J.D. Prince and Marcus A. Brubaker},
    month=Mar,
    year={2021},
    url_blog={https://www.borealisai.com/en/blog/tutorial-13-differential-privacy-ii-machine-learning-and-data-generation/},
}

Differential Privacy Tutorial I: Introduction. Brubaker, M. A.; and Prince, S. J. February 2021.

Differential Privacy Tutorial I: Introduction [link]

blog link bibtex 9 downloads

@misc{BrubakerDPTutorialPartI2021,
    title={{Differential Privacy Tutorial I: Introduction}},
    author={Marcus A. Brubaker and Simon J.D. Prince},
    month=Feb,
    year={2021},
    url_blog={https://www.borealisai.com/en/blog/tutorial-12-differential-privacy-i-introduction/}
}

2020 (8)

Normalizing Flows: An Introduction and Review of Current Methods. Kobyzev, I.; Prince, S. J.; and Brubaker, M. A. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). 2020.

Normalizing Flows: An Introduction and Review of Current Methods [link]

arxiv link bibtex 37 downloads

@article{KobyzevPAMI2020,
  title={Normalizing Flows: An Introduction and Review of Current Methods},
  author={Kobyzev, Ivan and Prince, Simon J.D. and Brubaker, Marcus A.},
  journal=PAMI,
  year={2020},
  eprint={1908.09257},
  archivePrefix={arXiv},
  primaryClass={stat.ML},
  url_arXiv={https://arxiv.org/abs/1908.09257}
}

Probabilistic Character Motion Synthesis using a Hierarchical Deep Latent Variable Model. Ghorbani, S.; Wloka, C.; Etemad, A.; Brubaker, M. A.; and Troje, N. F. Proceedings of Symposium on Computer Animation (SCA) in Computer Graphics Forum, 39(8). 2020.