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Title: GitHub - SteveJayH/AdvancedML: Reading list for the Advanced Machine Learning Course

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Description: Reading list for the Advanced Machine Learning Course - GitHub - SteveJayH/AdvancedML: Reading list for the Advanced Machine Learning Course

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Mail addresses
sjhwang82@kaist.ac.kr
haebeom.lee@kaist.ac.kr
jinheon.baek@kaist.ac.kr
animecult@kaist.ac.kr
wyjeong@kaist.ac.kr

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https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#advanced-deep-learning--kaist
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https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#reading-list
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#bayesian-deep-learning
[Kingma and Welling 14]https://arxiv.org/pdf/1312.6114.pdf
[Kingma et al. 15]https://arxiv.org/pdf/1506.02557.pdf
[Blundell et al. 15]https://arxiv.org/pdf/1505.05424.pdf
[Gal and Ghahramani 16]http://proceedings.mlr.press/v48/gal16.pdf
[Liu et al. 16]https://papers.nips.cc/paper/6338-stein-variational-gradient-descent-a-general-purpose-bayesian-inference-algorithm.pdf
[Mandt et al. 17]https://www.jmlr.org/papers/volume18/17-214/17-214.pdf
[Kendal and Gal 17]https://papers.nips.cc/paper/7141-what-uncertainties-do-we-need-in-bayesian-deep-learning-for-computer-vision.pdf
[Gal et al. 17]https://papers.nips.cc/paper/6949-concrete-dropout.pdf
[Gal et al. 17]http://proceedings.mlr.press/v70/gal17a/gal17a.pdf
[Teye et al. 18]http://proceedings.mlr.press/v80/teye18a/teye18a.pdf
[Garnelo et al. 18]http://proceedings.mlr.press/v80/garnelo18a/garnelo18a.pdf
[Kim et al. 19]http://https://arxiv.org/pdf/1901.05761.pdf
[Sun et al. 19]https://arxiv.org/pdf/1903.05779.pdf
[Louizos et al. 19]http://papers.nips.cc/paper/9079-the-functional-neural-process.pdf
[Zhang et al. 20]https://openreview.net/forum?id=rkeS1RVtPS
[Amersfoort et al. 20]https://arxiv.org/pdf/2003.02037.pdf
[Dusenberry et al. 20]https://proceedings.icml.cc/static/paper_files/icml/2020/5657-Paper.pdf
[Wenzel et al. 20]https://proceedings.icml.cc/static/paper_files/icml/2020/3581-Paper.pdf
[Lee et al. 20]https://arxiv.org/abs/2008.02956
[Wilson et al. 20]https://proceedings.neurips.cc/paper/2020/file/322f62469c5e3c7dc3e58f5a4d1ea399-Paper.pdf
[Izmailov et al. 21]http://proceedings.mlr.press/v139/izmailov21a/izmailov21a.pdf
[Daxberger et al. 21]http://proceedings.mlr.press/v139/daxberger21a/daxberger21a.pdf
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#deep-generative-models
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#vaes-autoregressive-and-flow-based-generative-models
[Rezende and Mohamed 15]http://proceedings.mlr.press/v37/rezende15.pdf
[Germain et al. 15]http://proceedings.mlr.press/v37/germain15.pdf
[Kingma et al. 16]https://papers.nips.cc/paper/6581-improved-variational-inference-with-inverse-autoregressive-flow.pdf
[Oord et al. 16]http://proceedings.mlr.press/v48/oord16.pdf
[Dinh et al. 17]https://openreview.net/pdf?id=HkpbnH9lx
[Papamakarios et al. 17]https://papers.nips.cc/paper/6828-masked-autoregressive-flow-for-density-estimation.pdf
[Huang et al.18]http://proceedings.mlr.press/v80/huang18d/huang18d.pdf
[Kingma and Dhariwal 18]http://papers.nips.cc/paper/8224-glow-generative-flow-with-invertible-1x1-convolutions.pdf
[Ho et al. 19]http://proceedings.mlr.press/v97/ho19a/ho19a.pdf
[Chen et al. 19]https://papers.nips.cc/paper/9183-residual-flows-for-invertible-generative-modeling.pdf
[Tran et al. 19]https://papers.nips.cc/paper/9612-discrete-flows-invertible-generative-models-of-discrete-data.pdf
[Ping et al. 20]https://proceedings.icml.cc/static/paper_files/icml/2020/647-Paper.pdf
[Vahdat and Kautz 20]https://arxiv.org/pdf/2007.03898v1.pdf
[Ho et al. 20]https://proceedings.neurips.cc//paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf
[Song et al. 21]https://openreview.net/forum?id=PxTIG12RRHS
[Kosiorek et al. 21]http://proceedings.mlr.press/v139/kosiorek21a/kosiorek21a.pdf
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#generative-adversarial-networks
[Goodfellow et al. 14]https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
[Radford et al. 15]https://arxiv.org/abs/1511.06434
[Chen et al. 16]https://papers.nips.cc/paper/6399-infogan-interpretable-representation-learning-by-information-maximizing-generative-adversarial-nets.pdf
[Arjovsky et al. 17]http://proceedings.mlr.press/v70/arjovsky17a/arjovsky17a.pdf
[Zhu et al. 17]https://arxiv.org/pdf/1703.10593.pdf
[Zhang et al. 17]https://arxiv.org/pdf/1706.03850.pdf
[Karras et al. 18]https://openreview.net/forum?id=Hk99zCeAb
[Choi et al. 18]https://openaccess.thecvf.com/content_cvpr_2018/papers/Choi_StarGAN_Unified_Generative_CVPR_2018_paper.pdf
[Brock et al. 19]https://openreview.net/pdf?id=B1xsqj09Fm
[Karras et al. 19]http://openaccess.thecvf.com/content_CVPR_2019/papers/Karras_A_Style-Based_Generator_Architecture_for_Generative_Adversarial_Networks_CVPR_2019_paper.pdf
[Karras et al. 20]https://openaccess.thecvf.com/content_CVPR_2020/papers/Karras_Analyzing_and_Improving_the_Image_Quality_of_StyleGAN_CVPR_2020_paper.pdf
[Sinha et al. 20]https://proceedings.icml.cc/static/paper_files/icml/2020/1324-Paper.pdf
[Karras et al. 20]https://papers.nips.cc/paper/2020/file/8d30aa96e72440759f74bd2306c1fa3d-Paper.pdf
[Liu et al. 21]https://openreview.net/pdf?id=1Fqg133qRaI
[Hudson and Zitnick 21]https://arxiv.org/pdf/2103.01209.pdf
[Karras et al. 21]https://nvlabs-fi-cdn.nvidia.com/alias-free-gan/alias-free-gan-paper.pdf
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#score-based-generative-models
[Song and Ermon 19]https://proceedings.neurips.cc/paper/2019/file/3001ef257407d5a371a96dcd947c7d93-Paper.pdf
[Song and Ermon 20]https://papers.nips.cc/paper/2020/file/92c3b916311a5517d9290576e3ea37ad-Paper.pdf
[Ho et al. 20]https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf
[Song et al. 21]https://openreview.net/forum?id=PxTIG12RRHS
[Nichol and Dhariwal 21]https://proceedings.mlr.press/v139/nichol21a/nichol21a.pdf
[Vahdat et al. 21]https://proceedings.neurips.cc/paper/2021/file/5dca4c6b9e244d24a30b4c45601d9720-Paper.pdf
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#deep-reinforcement-learning
[Mnih et al. 13]https://arxiv.org/pdf/1312.5602.pdf
[Silver et al. 14]http://proceedings.mlr.press/v32/silver14.pdf
[Schulman et al. 15]https://arxiv.org/pdf/1502.05477.pdf
[Lillicrap et al. 16]https://arxiv.org/pdf/1509.02971.pdf
[Schaul et al. 16]https://arxiv.org/pdf/1511.05952.pdf
[Wang et al. 16]http://proceedings.mlr.press/v48/wangf16.pdf
[Mnih et al. 16]http://proceedings.mlr.press/v48/mniha16.pdf
[Schulman et al. 17]https://arxiv.org/pdf/1707.06347.pdf
[Nachum et al. 18]https://papers.nips.cc/paper/7591-data-efficient-hierarchical-reinforcement-learning.pdf
[Ha et al. 18]https://papers.nips.cc/paper/7512-recurrent-world-models-facilitate-policy-evolution.pdf
[Burda et al. 19]https://openreview.net/forum?id=rJNwDjAqYX
[Vinyals et al. 19]https://www.nature.com/articles/s41586-019-1724-z
[Bellemare et al. 19]https://papers.nips.cc/paper/8687-a-geometric-perspective-on-optimal-representations-for-reinforcement-learning.pdf
[Janner et al. 19]http://papers.nips.cc/paper/9416-when-to-trust-your-model-model-based-policy-optimization.pdf
[Fellows et al. 19]https://papers.nips.cc/paper/8934-virel-a-variational-inference-framework-for-reinforcement-learning.pdf
[Kumar et al. 19]https://proceedings.neurips.cc/paper/2019/file/c2073ffa77b5357a498057413bb09d3a-Paper.pdf
[Kaiser et al. 20]https://openreview.net/pdf?id=S1xCPJHtDB
[Agarwal et al. 20]https://proceedings.icml.cc/static/paper_files/icml/2020/5394-Paper.pdf
[Lee et al. 20]https://proceedings.icml.cc/static/paper_files/icml/2020/3705-Paper.pdf
[Kumar et al. 20]https://proceedings.neurips.cc/paper/2020/file/0d2b2061826a5df3221116a5085a6052-Paper.pdf
[Oh et al. 21]https://openreview.net/pdf?id=gJYlaqL8i8
[Yarats et al. 21]https://openreview.net/pdf?id=GY6-6sTvGaf
[Lee et al. 21]http://proceedings.mlr.press/v139/lee21g/lee21g.pdf
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#memory-and-computation-efficient-deep-learning
[Han et al. 15]https://papers.nips.cc/paper/5784-learning-both-weights-and-connections-for-efficient-neural-network.pdf
[Wen et al. 16]https://papers.nips.cc/paper/6504-learning-structured-sparsity-in-deep-neural-networks.pdf
[Han et al. 16]https://arxiv.org/pdf/1510.00149.pdf
[Molchanov et al. 17]https://arxiv.org/pdf/1701.05369.pdf
[Luizos et al. 17]https://papers.nips.cc/paper/6921-bayesian-compression-for-deep-learning.pdf
[Luizos et al. 18]https://openreview.net/pdf?id=H1Y8hhg0b
[Howard et al. 18]https://arxiv.org/pdf/1704.04861.pdf
[Frankle and Carbin 19]https://https://openreview.net/pdf?id=rJl-b3RcF7
[Lee et al. 19]https://openreview.net/pdf?id=B1VZqjAcYX
[Liu et al. 19]https://openreview.net/pdf?id=rJlnB3C5Ym
[Jung et al. 19]https://arxiv.org/abs/1808.05779
[Morcos et al. 19]https://papers.nips.cc/paper/8739-one-ticket-to-win-them-all-generalizing-lottery-ticket-initializations-across-datasets-and-optimizers.pdf
[Renda et al. 20]https://openreview.net/pdf?id=S1gSj0NKvB
[Frankle et al. 20]https://proceedings.icml.cc/static/paper_files/icml/2020/5787-Paper.pdf
[Tanaka et al. 20]https://proceedings.neurips.cc/paper/2020/file/46a4378f835dc8040c8057beb6a2da52-Paper.pdf
[van Baalen et al. 20]https://proceedings.neurips.cc/paper/2020/file/3f13cf4ddf6fc50c0d39a1d5aeb57dd8-Paper.pdf
[de Jorge et al. 21]https://openreview.net/pdf?id=9GsFOUyUPi
[Stock et al. 21]https://openreview.net/pdf?id=dV19Yyi1fS3
[Lee et al. 21]https://arxiv.org/abs/1911.12990
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#meta-learning
[Santoro et al. 16]http://proceedings.mlr.press/v48/santoro16.pdf
[Vinyals et al. 16]https://arxiv.org/pdf/1606.04080.pdf
[Edwards and Storkey 17]https://arxiv.org/pdf/1606.02185.pdf
[Finn et al. 17]https://arxiv.org/pdf/1703.03400.pdf
[Snell et al. 17]https://arxiv.org/pdf/1703.05175.pdf
[Nichol et al. 18]https://arxiv.org/pdf/1803.02999.pdf
[Lee and Choi 18]https://arxiv.org/abs/1801.05558
[Liu et al. 19]https://openreview.net/pdf?id=SyVuRiC5K7
[Gordon et al. 19]https://openreview.net/pdf?id=HkxStoC5F7
[Ravi and Beatson 19]https://openreview.net/pdf?id=rkgpy3C5tX
[Rakelly et al. 19]http://proceedings.mlr.press/v97/rakelly19a/rakelly19a.pdf
[Shu et al. 19]https://papers.nips.cc/paper/8467-meta-weight-net-learning-an-explicit-mapping-for-sample-weighting.pdf
[Finn et al. 19]http://proceedings.mlr.press/v97/finn19a/finn19a.pdf
[Lee et al. 20]https://openreview.net/pdf?id=rkeZIJBYvr
[Yin et al. 20]https://openreview.net/forum?id=BklEFpEYwS
[Raghu et al. 20]https://openreview.net/pdf?id=rkgMkCEtPB
[Iakovleva et al. 20]https://proceedings.icml.cc/static/paper_files/icml/2020/4070-Paper.pdf
[Bronskill et al. 20]https://proceedings.icml.cc/static/paper_files/icml/2020/2696-Paper.pdf
[Rajendran et al. 20]https://proceedings.neurips.cc//paper/2020/file/3e5190eeb51ebe6c5bbc54ee8950c548-Paper.pdf
[Lee et al. 21]https://openreview.net/forum?id=wS0UFjsNYjn
[Shin et al. 21]http://proceedings.mlr.press/v139/shin21a/shin21a.pdf
[Acar et al. 21]http://proceedings.mlr.press/v139/acar21b/acar21b.pdf
[Bai et al. 21]http://proceedings.mlr.press/v139/bai21a/bai21a.pdf
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#continual-learning
[Rusu et al. 16]https://arxiv.org/pdf/1606.04671.pdf
[Kirkpatrick et al. 17]https://arxiv.org/pdf/1612.00796.pdf
[Lee et al. 17]https://papers.nips.cc/paper/7051-overcoming-catastrophic-forgetting-by-incremental-moment-matching.pdf
[Shin et al. 17]https://papers.nips.cc/paper/6892-continual-learning-with-deep-generative-replay.pdf
[Lopez-Paz and Ranzato 17]https://papers.nips.cc/paper/7225-gradient-episodic-memory-for-continual-learning.pdf
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[Nguyen et al. 18]https://arxiv.org/pdf/1710.10628.pdf
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[Chaudhry et al. 19]https://openreview.net/pdf?id=Hkf2_sC5FX
[Rao et al. 19]https://papers.nips.cc/paper/8981-continual-unsupervised-representation-learning.pdf
[Rolnick et al. 19]https://papers.nips.cc/paper/8327-experience-replay-for-continual-learning.pdf
[Jerfel et al. 20]https://papers.nips.cc/paper/9112-reconciling-meta-learning-and-continual-learning-with-online-mixtures-of-tasks.pdf
[Yoon et al. 20]https://openreview.net/forum?id=r1gdj2EKPB
[Remasesh et al. 20]https://arxiv.org/pdf/2007.07400.pdf
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[Veinat et al. 21]https://openreview.net/pdf?id=EKV158tSfwv
[Kumar et al. 21]http://proceedings.mlr.press/v139/kumar21a/kumar21a.pdf
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#interpretable-deep-learning
[Ribeiro et al. 16]https://arxiv.org/pdf/1602.04938.pdf
[Kim et al. 16]https://people.csail.mit.edu/beenkim/papers/KIM2016NIPS_MMD.pdf
[Choi et al. 16]https://papers.nips.cc/paper/6321-retain-an-interpretable-predictive-model-for-healthcare-using-reverse-time-attention-mechanism.pdf
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[Selvaraju et al. 17]http://openaccess.thecvf.com/content_ICCV_2017/papers/Selvaraju_Grad-CAM_Visual_Explanations_ICCV_2017_paper.pdf
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[Coenen et al. 19]https://papers.nips.cc/paper/9065-visualizing-and-measuring-the-geometry-of-bert.pdf
[Heo et al. 20]https://proceedings.icml.cc/static/paper_files/icml/2020/579-Paper.pdf
[Agarwal et al. 20]https://arxiv.org/pdf/2004.13912v1.pdf
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#reliable-deep-learning
[Guo et al. 17]https://arxiv.org/pdf/1706.04599.pdf
[Lakshminarayanan et al. 17]http://papers.nips.cc/paper/7219-simple-and-scalable-predictive-uncertainty-estimation-using-deep-ensembles.pdf
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[Filos et al. 20]https://proceedings.icml.cc/static/paper_files/icml/2020/2969-Paper.pdf
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#adversarially-robust-deep-learning
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[Goodfellow et al. 15]https://arxiv.org/pdf/1412.6572.pdf
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https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#graph-neural-networks
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[Baek et al. 21]https://openreview.net/forum?id=JHcqXGaqiGn
[Liu et al. 21]http://proceedings.mlr.press/v139/liu21k/liu21k.pdf
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#self-supervised-learning
[Dosovitskiy et al. 14]https://papers.nips.cc/paper/5548-discriminative-unsupervised-feature-learning-with-convolutional-neural-networks.pdf
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[Caron et al. 21]https://arxiv.org/pdf/2104.14294.pdf
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#federated-learning
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[Konečný et al. 16]https://arxiv.org/abs/1610.05492
[McMahan et al. 17]http://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf
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[Li et al. 21]http://proceedings.mlr.press/v139/li21h/li21h.pdf
https://patch-diff.githubusercontent.com/SteveJayH/AdvancedML#neural-architecture-search
[Zoph and Le 17]https://arxiv.org/abs/1611.01578
[Baker et al. 17]https://openreview.net/pdf?id=S1c2cvqee
[Real et al. 17]http://proceedings.mlr.press/v70/real17a/real17a.pdf
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[Tan et al. 19]https://arxiv.org/abs/1807.11626
[Cai et al. 19]https://openreview.net/pdf?id=HylVB3AqYm
[Zhou et al. 19]http://proceedings.mlr.press/v97/zhou19e/zhou19e.pdf
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[Guo et al. 19]https://papers.nips.cc/paper/8362-nat-neural-architecture-transformer-for-accurate-and-compact-architectures.pdf
[Chen et al. 19]https://papers.nips.cc/paper/8890-detnas-backbone-search-for-object-detection.pdf
[Dong and Yang 20]https://openreview.net/forum?id=HJxyZkBKDr
[Zela et al. 20]https://openreview.net/pdf?id=H1gDNyrKDS
[Cai et al. 20]https://openreview.net/pdf?id=HylxE1HKwS
[Such et al. 20]https://proceedings.icml.cc/static/paper_files/icml/2020/4522-Paper.pdf
[Liu et al. 20]https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490766.pdf
[Dudziak et al. 20]https://proceedings.neurips.cc/paper/2020/file/768e78024aa8fdb9b8fe87be86f64745-Paper.pdf
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[Lee et al. 21]https://openreview.net/forum?id=rkQuFUmUOg3
[Mellor et al. 21]https://arxiv.org/pdf/2006.04647.pdf
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