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Understanding the difficulty of training deep feedforward neural networks.

, and . AISTATS, volume 9 of JMLR Proceedings, page 249-256. JMLR.org, (2010)

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Large-Scale Learning of Embeddings with Reconstruction Sampling., , and . ICML, page 945-952. Omnipress, (2011)beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework., , , , , , , and . ICLR (Poster), OpenReview.net, (2017)Adding noise to the input of a model trained with a regularized objective, , , and . CoRR, (2011)Training a First-Order Theorem Prover from Synthetic Data., , , , , , , , and . CoRR, (2021)Learning invariant features through local space contraction, , , , , and . CoRR, (2011)Deep Learners Benefit More from Out-of-Distribution Examples., , , , , , , , , and 7 other author(s). AISTATS, volume 15 of JMLR Proceedings, page 164-172. JMLR.org, (2011)Theano: A Python framework for fast computation of mathematical expressions, , , , , , , , , and 103 other author(s). (2016)cite arxiv:1605.02688Comment: 19 pages, 5 figures.Contractive Auto-Encoders: Explicit Invariance During Feature Extraction., , , , and . ICML, page 833-840. Omnipress, (2011)Proving Theorems using Incremental Learning and Hindsight Experience Replay., , , , , , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 1198-1210. PMLR, (2022)Understanding the difficulty of training deep feedforward neural networks., and . AISTATS, volume 9 of JMLR Proceedings, page 249-256. JMLR.org, (2010)