Fetching the paper…
Reading the bibliography…
Measuring the similarity between data points often requires domain knowledge, which can in parts be compensated by relying on unsupervised methods such as latent-variable models, where similarity/distance is estimated in a more compact latent space.
An introduction to kernel and nearest-neighbor nonparametric regression
Altman, N. S · 1992
Earlier work this paper cites.
Magnification factors for the SOM and GTM algorithms
Bishop, C. M., Svens’ en, M., and Williams, C. K · 1997
Earlier work this paper cites.
Kernel principal component analysis
Schölkopf, B., Smola, A., and Müller, K.-R · 1997
Earlier work this paper cites.
Distance metric learning with application to clustering with side-information
Xing, E. P., Jordan, M. I., Russell, S. J., and Ng, A. Y · 2003
Earlier work this paper cites.
Neighbourhood components analysis
Goldberger, J., Hinton, G. E., Roweis, S. T., and Salakhutdinov, R. R · 2005
Earlier work this paper cites.
Riemannian manifolds: an introduction to curvature , volume 176
Lee, J. M · 2006
Earlier work this paper cites.
The relationships among various nonnegative matrix factorization methods for clustering
Li, T. and Ding, C · 2006
Earlier work this paper cites.
Gaussian process dynamical models for human motion
Wang, J. M., Fleet, D. J., and Hertzmann, A · 2007
Earlier work this paper cites.
Distance metric learning for large margin nearest neighbor classification
Weinberger, K. Q. and Saul, L. K · 2009
Earlier work this paper cites.
The neural autoregressive distribution estimator
Larochelle, H. and Murray, I · 2011
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Earlier work this paper cites.
Metrics for probabilistic geometries
Tosi, A., Hauberg, S., Vellido, A., and Lawrence, N. D · 2014
Earlier work this paper cites.
Importance weighted autoencoders
Burda, Y., Grosse, R. B., and Salakhutdinov, R · 2015
Earlier work this paper cites.
Efficient movement representation by embedding dynamic movement primitives in deep autoencoders
Chen, N., Bayer, J., Urban, S., and Van Der Smagt, P · 2015
Earlier work this paper cites.
Deep metric learning using triplet network
Hoffer, E. and Ailon, N · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
Cited alongside, same era.
Simple online and realtime tracking
Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B · 2016
Cited alongside, same era.
Generating sentences from a continuous space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R., and Bengio, S · 2016
Cited alongside, same era.
Dynamic movement primitives in latent space of time-dependent variational autoencoders
Chen, N., Karl, M., and van der Smagt, P · 2016
Cited alongside, same era.
Bayesian representation learning with oracle constraints
Karaletsos, T., Belongie, S., and Rätsch, G · 2016
Cited alongside, same era.
Fixing a broken ELBO
Alemi, A. A., Poole, B., Fischer, I., Dillon, J. V., Saurous, R. A., and Murphy, K · 2018
Later among the works it cites.
Latent space oddity: on the curvature of deep generative models
Arvanitidis, G., Hansen, L. K., and Hauberg, S · 2018
Later among the works it cites.
Learning graph embeddings on constant-curvature manifolds for change detection in graph streams
Grattarola, D., Zambon, D., Alippi, C., and Livi, L · 2018
Later among the works it cites.
Improving DNN robustness to adversarial attacks using Jacobian regularization
Jakubovitz, D. and Giryes, R · 2018
Later among the works it cites.
Rezende, D. J. and Viola, F · 2018
Later among the works it cites.
VAE with a vampprior
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Improving Variational Inference with Inverse Autoregressive Flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M · 2016
Cited alongside, same era.
Mot16: A benchmark for multi-object tracking
Milan, A., Leal-Taixé, L., Reid, I., Roth, S., and Schindler, K · 2016
Cited alongside, same era.
Performance measures and a data set for multi-target, multi-camera tracking
Ristani, E., Solera, F., Zou, R. S., Cucchiara, R., and Tomasi, C · 2016
Cited alongside, same era.
Ladder variational autoencoders
Sønderby, C. K., Raiko, T., Maaløe, L., Sønderby, S. K., and Winther, O · 2016
Cited alongside, same era.
POI: multiple object tracking with high performance detection and appearance feature
Yu, F., Li, W., Li, Q., Liu, Y., Shi, X., and Yan, J · 2016
Cited alongside, same era.
Geometric deep learning: going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P · 2017
Cited alongside, same era.
Tomczak, J. M. and Welling, M · 2018
Later among the works it cites.
Deep cosine metric learning for person re-identification
Wojke, N. and Bewley, A · 2018
Later among the works it cites.
mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
Later among the works it cites.
On adversarial mixup resynthesis
Beckham, C., Honari, S., Lamb, A. M., Verma, V., Ghadiri, F., Hjelm, R. D., Bengio, Y., and Pal, C · 2019
Later among the works it cites.
Fast approximate geodesics for deep generative models
Chen, N., Ferroni, F., Klushyn, A., Paraschos, A., Bayer, J., and van der Smagt, P · 2019
Later among the works it cites.
Frenzel, M. F., Teleaga, B., and Ushio, A · 2019
Later among the works it cites.
Learning hierarchical priors in VAEs
Klushyn, A., Chen, N., Kurle, R., Cseke, B., and van der Smagt, P · 2019
Later among the works it cites.
Hierarchical representations with Poincar \ \backslash ’e variational auto-encoders
Mathieu, E., Lan, C. L., Maddison, C. J., Tomioka, R., and Teh, Y. W · 2019
Later among the works it cites.
Towards a better understanding and regularization of GAN training dynamics
Nie, W. and Patel, A · 2019
Later among the works it cites.
Manifold mixup: Better representations by interpolating hidden states
Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Courville, A., Lopez-Paz, D., and Bengio, Y · 2019
Later among the works it cites.