Learning Deep ResNet Blocks Sequentially using Boosting Theory — mentioned by nl

Tue, 31 Oct 2017 10:06:48 GMT

"boys" Not sure what this was supposed to mean? Yes, I think Fei Fei Li's datasets are much better tests than MNIST if that is what you were getting at? I don't see any of these people submitting MNIST to NIPS in 2017 None of them submitted things as entirely new and different as this, either. Having said that, I think my point holds. The completely awesome 2017 "Generalization in Deep Learning" paper[1] was co-authored by Bengio and uses MNIST - because everyone can follow it. Yann LeCun was co-author on the 2017 "Adversarially Regularized Autoencoders for Generating Discrete…

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

Learning Deep ResNet Blocks Sequentially using Boosting Theory — mentioned by daddyo

Tue, 18 Jul 2017 18:56:53 GMT

Seems that deep learning can benefit from gradient boosting too (at least, from a computational perspective). https://arxiv.org/abs/1706.04964 "Learning Deep ResNet Blocks Sequentially using Boosting Theory" (As for the lay-man description: I thought boosting performed better out-of-the-box on dense data than on sparse data, because most feature sub-selections for bagging are on zero'd features)

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

Learning Deep ResNet Blocks Sequentially using Boosting Theory · HackerPapers discussions

Learning Deep ResNet Blocks Sequentially using Boosting Theory — mentioned by nl

Learning Deep ResNet Blocks Sequentially using Boosting Theory — mentioned by daddyo