arXiv preprint arXiv:1409.0473 (2014). Lower bounds show that this exponential dependence on depth is unavoidable when no additional properties of the training data are considered. case-insensitive prefix search: default e.g., sig matches "SIGIR" as well as "signal" exact word search: append dollar sign ($) to word e.g., graph$ matches "graph", but not "graphics" boolean and: separate words by space e.g., codd model boolean or: connect words by pipe symbol (|) e.g., graph|network Update May 7, 2017: Please note that we had to disable the phrase search operator (.) Google Scholar; Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Recent empirical and theoretical studies have shown that many learning algorithms -- from linear regression to neural networks -- can have test performance that is non-monotonic in quantities such the sample size and model size. Their combined citations are counted only for the first article. By using the integral estimation method combined with the Gronwall inequality, we point out that the global strong solutions of the problems decay to zero exponentially with the passage of time to infinity. arXiv is committed to these values and only works with partners that adhere to them. Their combined citations are counted only for the first article. This "Cited by" count includes citations to the following articles in Scholar. The following articles are merged in Scholar. Add co-authors Co-authors. Merged citations. We suspect that this conundrum comes from the fact that these bounds … They are widely believed to stabilize training, enable higher learning rate, accelerate convergence and improve generalization, though the reason for their effectiveness is still an active research topic. The ones marked * may be different from the article in the profile. Merged citations. Add co-authors Co-authors. ... Tengyu MA Stanford University Verified email at stanford.edu. This striking phenomenon, often referred to as "double descent", has raised questions of if we need to re-think our current understanding of generalization. Add co-authors Co-authors. Their combined citations are counted only for the first article. The ones marked * may be different from the article in the profile. Add co-authors ... Tengyu zhang. Existing Rademacher complexity bounds for neural networks rely only on norm control of the weight matrices and depend exponentially on depth via a product of the matrix norms. Google Scholar The ones marked * may be different from the article in the profile. An emerging design principle in deep learning is that each layer of a deep artificial neural network should be able to easily express the identity transformation. 2014. Their combined citations are counted only for the first article. A simple but tough-to-beat baseline for sentence embeddings. (2016). ... Abhishek Kumar Google Brain Verified email at google.com. The following articles are merged in Scholar. This "Cited by" count includes citations to the following articles in Scholar. This "Cited by" count includes citations to the following articles in Scholar. In … Google Scholar; Sanjeev Arora, Yingyu Liang, and Tengyu Ma 2016natexlabb. The following articles are merged in Scholar. This "Cited by" count includes citations to the following articles in Scholar. Neural machine translation by jointly learning to align and translate. Authors: Yuanzhi Li, Tengyu Ma, Hongyang Zhang Download PDF Abstract: We show that the gradient descent algorithm provides an implicit regularization effect in the learning of over-parameterized matrix factorization models and one-hidden … Normalization layers are a staple in state-of-the-art deep neural network architectures. The ones marked * may be different from the article in the profile. The following articles are merged in Scholar. Tengyu Ma; ICLR (2017) Download Google Scholar Copy Bibtex Abstract. 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