Identity Mappings in Deep Residual Networks · HackerPapers discussions

Identity Mappings in Deep Residual Networks — mentioned by cute_boi

Tue, 07 Jul 2026 19:03:23 GMT

No need stupid moving texts. CS231n: Convolutional Neural Networks for Visual Recognition - https://cs231n.github.io/ The Unreasonable Effectiveness of Recurrent Neural Networks - https://karpathy.github.io/2015/05/21/rnn-effectiveness/ Understanding LSTM Networks - https://colah.github.io/posts/2015-08-Understanding-LSTMs/ ImageNet Classification with Deep Convolutional Neural Networks - https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436... Deep Residual Learning for Image Recognition - https://arxiv.org/abs/1512.03385 Multi-Scale Context Aggregation by Dilated Convolutions…

Paper and discussion history. A mention is not an endorsement.

Identity Mappings in Deep Residual Networks — mentioned by gooob

Tue, 07 Jul 2026 18:54:32 GMT

https://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436... https://papers.nips.cc/paper_files/paper/2012/file/c399862d3... https://arxiv.org/pdf/1512.03385 https://arxiv.org/pdf/1511.07122 https://arxiv.org/pdf/1603.05027 https://arxiv.org/pdf/1409.2329 https://arxiv.org/pdf/1512.02595 https://arxiv.org/pdf/1409.0473 https://arxiv.org/pdf/1506.03134 https://arxiv.org/pdf/1706.03762 https://nlp.seas.harvard.edu/annotated-transformer/ https://arxiv.org/pdf/1410.5401 https://arxiv.org/pdf/1706.01427 https://arxiv.org/pdf/1806.01822…

Paper and discussion history. A mention is not an endorsement.

Identity Mappings in Deep Residual Networks — mentioned by is_true

Sat, 18 May 2024 12:26:29 GMT

a backup just in case https://nlp.seas.harvard.edu/annotated-transformer/ https://scottaaronson.blog/?p=762 https://karpathy.github.io/2015/05/21/rnn-effectiveness/ https://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://arxiv.org/pdf/1409.2329.pdf https://www.cs.toronto.edu/~hinton/absps/colt93.pdf https://arxiv.org/pdf/1506.03134.pdf https://proceedings.neurips.cc/paper_files/paper/2012/file/c... https://arxiv.org/pdf/1511.06391.pdf https://arxiv.org/pdf/1811.06965.pdf https://arxiv.org/pdf/1512.03385.pdf https://arxiv.org/pdf/1511.07122.pdf https://arxiv.org/pdf/1704.01212.pdf…

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Identity Mappings in Deep Residual Networks — mentioned by ogrisel

Tue, 22 Nov 2016 19:05:36 GMT

For deep convnets the vanishing gradient problems can mostly be solved by using residual architectures. See: https://arxiv.org/abs/1603.05027 This is kind of related to solving the vanishing gradient issue in RNNs by using additive recurrent architectures like LSTMs and GRUs. Alternatively it's possible to use concatenative skip connections as in DenseNets: https://arxiv.org/abs/1608.06993 Still using 1000 layers is useless in practice. State of the art image classification models are in the range 30-100 layers with residual connections and varying numbers of channels per layer depending on…

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Identity Mappings in Deep Residual Networks — HN submission

Thu, 17 Mar 2016 10:28:57 GMT

Identity Mappings in Deep Residual Networks

3 points; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.