www-ai.cs.tu-dortmund.de/PERSONAL/MORIK/papers/pdf/Learning_Low_Rank_Document_Embeddings_with_Weighted_Nuclear_Norm_Regularization.pdf
Learning Low-Rank Document Embeddings with Weighted Nuclear Norm Regularization
Information Theory, vol. 62, no. 11, pp. 6535–6579, 2016.
[25] R. Ge, J. D. Lee, and T. Ma, “Matrix Completion has No Spurious Local Minimum,” no. Nips, pp. 1–9, 2016.
[26] C. D. Sa, K. Olukotun, and C. Ré, “Global [...] Absil, and C. Cartis, “Global rates of convergence for nonconvex optimization on manifolds,” pp. 1–31, 2016.
[29] U. Shalit, D. Weinshall, and G. Chechik, “Online Learning in the Manifold of Low-Rank Matrices [...] and O. Vinyals, “Understand- ing deep learning requires rethinking generalization,” in ICLR 2017, 2016.
[34] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” Journal of Machine Learning …