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Yoshua Bengio | Hugo Larochelle | Han Zhang | Anirudh Goyal | Augustus Odena | Samarth Sinha | Yoshua Bengio | H. Larochelle | Anirudh Goyal | Augustus Odena | Hang Zhang | Samarth Sinha
[1] Yuichi Yoshida,et al. Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.
[2] Yingyu Liang,et al. Generalization and Equilibrium in Generative Adversarial Nets (GANs) , 2017, ICML.
[3] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[4] Kasturi R. Varadarajan,et al. Geometric Approximation via Coresets , 2007 .
[5] José Bento,et al. Generative Adversarial Active Learning , 2017, ArXiv.
[6] Trevor Darrell,et al. Discriminator Rejection Sampling , 2018, ICLR.
[7] Augustus Odena,et al. Open Questions about Generative Adversarial Networks , 2019, Distill.
[8] Chuan Sheng Foo,et al. Efficient GAN-Based Anomaly Detection , 2018, ArXiv.
[9] Fabián A. Chudak,et al. Near-optimal solutions to large-scale facility location problems , 2005, Discret. Optim..
[10] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[11] Yinda Zhang,et al. LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop , 2015, ArXiv.
[12] Lars M. Mescheder,et al. On the convergence properties of GAN training , 2018, ArXiv.
[13] Andrew M. Dai,et al. MaskGAN: Better Text Generation via Filling in the ______ , 2018, ICLR.
[14] Kaiming He,et al. Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour , 2017, ArXiv.
[15] Silvio Savarese,et al. Active Learning for Convolutional Neural Networks: A Core-Set Approach , 2017, ICLR.
[16] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[17] J. Zico Kolter,et al. Gradient descent GAN optimization is locally stable , 2017, NIPS.
[18] Kenneth L. Clarkson,et al. Coresets, sparse greedy approximation, and the Frank-Wolfe algorithm , 2008, SODA '08.
[19] Rameshwar Pratap,et al. Faster Coreset Construction for Projective Clustering via Low-Rank Approximation , 2018, IWOCA.
[20] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[21] Vladimir Braverman,et al. Data-Independent Neural Pruning via Coresets , 2020, ICLR.
[22] Yi Zhang,et al. Do GANs learn the distribution? Some Theory and Empirics , 2018, ICLR.
[23] Jerry Li,et al. Towards Understanding the Dynamics of Generative Adversarial Networks , 2017, ArXiv.
[24] Ivor W. Tsang,et al. Core Vector Machines: Fast SVM Training on Very Large Data Sets , 2005, J. Mach. Learn. Res..
[25] Dimitris N. Metaxas,et al. StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[26] Ian J. Goodfellow,et al. Skill Rating for Generative Models , 2018, ArXiv.
[27] Yong Yu,et al. Long Text Generation via Adversarial Training with Leaked Information , 2017, AAAI.
[28] Jeff M. Phillips,et al. Coresets and Sketches , 2016, ArXiv.
[29] Han Zhang,et al. Improving GANs Using Optimal Transport , 2018, ICLR.
[30] A. J. Goldman. Optimal Center Location in Simple Networks , 1971 .
[31] Han Zhang,et al. Self-Attention Generative Adversarial Networks , 2018, ICML.
[32] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Jorge Nocedal,et al. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima , 2016, ICLR.
[34] Piotr Indyk,et al. Approximate clustering via core-sets , 2002, STOC '02.
[35] Sridhar Mahadevan,et al. Generative Multi-Adversarial Networks , 2016, ICLR.
[36] Jascha Sohl-Dickstein,et al. Measuring the Effects of Data Parallelism on Neural Network Training , 2018, J. Mach. Learn. Res..
[37] Franziska Abend,et al. Facility Location Concepts Models Algorithms And Case Studies , 2016 .
[38] Trevor Darrell,et al. Variational Adversarial Active Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[39] Jeff A. Bilmes,et al. Using Document Summarization Techniques for Speech Data Subset Selection , 2013, NAACL.
[40] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[41] Andreas Krause,et al. Scalable Training of Mixture Models via Coresets , 2011, NIPS.
[42] Raymond Y. K. Lau,et al. Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[43] VARUN CHANDOLA,et al. Anomaly detection: A survey , 2009, CSUR.
[44] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[45] Trevor Campbell,et al. Coresets for Scalable Bayesian Logistic Regression , 2016, NIPS.
[46] Marc G. Bellemare,et al. The Cramer Distance as a Solution to Biased Wasserstein Gradients , 2017, ArXiv.
[47] Tatjana Chavdarova,et al. Reducing Noise in GAN Training with Variance Reduced Extragradient , 2019, NeurIPS.
[48] Sariel Har-Peled,et al. Smaller Coresets for k-Median and k-Means Clustering , 2005, SCG.
[49] Quoc V. Le,et al. Don't Decay the Learning Rate, Increase the Batch Size , 2017, ICLR.
[50] Bernd Girod,et al. What's wrong with mean-squared error? , 1993 .
[51] Paul S. Fisher,et al. Image quality measures and their performance , 1995, IEEE Trans. Commun..
[52] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[53] Vladimir Braverman,et al. On Activation Function Coresets for Network Pruning , 2019, ArXiv.
[54] Sebastian Nowozin,et al. Which Training Methods for GANs do actually Converge? , 2018, ICML.
[55] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[56] Yoshua Bengio,et al. Maximum Entropy Generators for Energy-Based Models , 2019, ArXiv.
[57] Yiming Yang,et al. MMD GAN: Towards Deeper Understanding of Moment Matching Network , 2017, NIPS.
[58] Tatjana Chavdarova,et al. SGAN: An Alternative Training of Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[59] Jinoh Kim,et al. A survey of deep learning-based network anomaly detection , 2017, Cluster Computing.
[60] Bernt Schiele,et al. Feature Generating Networks for Zero-Shot Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[61] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[62] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[63] Laurence A. Wolsey,et al. Integer and Combinatorial Optimization , 1988 .
[64] Jeff Donahue,et al. Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.
[65] David M. Blei,et al. Prescribed Generative Adversarial Networks , 2019, ArXiv.
[66] Richard E. Turner,et al. Variational Continual Learning , 2017, ICLR.
[67] Andreas Krause,et al. Practical Coreset Constructions for Machine Learning , 2017, 1703.06476.
[68] Ted K. Ralphs,et al. Integer and Combinatorial Optimization , 2013 .
[69] Gregory Piatetsky-Shapiro,et al. High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality , 2000 .
[70] Gauthier Gidel,et al. A Variational Inequality Perspective on Generative Adversarial Networks , 2018, ICLR.
[71] Sanjoy Dasgupta,et al. An elementary proof of a theorem of Johnson and Lindenstrauss , 2003, Random Struct. Algorithms.
[72] Andreas Krause,et al. Training Gaussian Mixture Models at Scale via Coresets , 2017, J. Mach. Learn. Res..
[73] Sariel Har-Peled,et al. On coresets for k-means and k-median clustering , 2004, STOC '04.
[74] Honglak Lee,et al. Consistency Regularization for Generative Adversarial Networks , 2020, ICLR.