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Cihang Xie
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Deep Disturbance Disentangled Learning For Facial Expression Recognition Proceedings Of The 28th Acm International Conference On Multimedia
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An algorithm for designing overcomplete dictionaries for sparse representation.
Kaiming he google scholar. Xiangnan he zhankui he xiaoyu du and tat seng chua. Deep residual learning for image recognition. The following articles are merged in scholar. Kaiming he xiangyu zhang shaoqing ren and jian sun.
Aharon m elad m bruckstein a. In proceedings of the sigir. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. Jifeng dai yi li kaiming he and jian sun neural information processing systems nips 2016.
An empirical study of training self supervised vision transformers xinlei chen saining xie and kaiming he tech report arxiv apr. Ali farhadi associate professor. Training imagenet in 1 hour priya goyal piotr dollár ross girshick pieter noordhuis lukasz wesolowski aapo kyrola andrew tulloch yangqing jia kaiming he arxiv preprint 2017 bibtex article goyal2017imagenet1hr author priya goyal and piotr doll a r and ross girshick and pieter noordhuis and lukasz wesolowski and aapo kyrola and andrew tulloch and. Tsp 54 11 4311 4322 2006 google scholar.
Deeper neural networks are more difficult to train. The recent progress of scientific technologies is producing a cambrian explosive in developing new techniques that lead the world entering promptly into the new artificial intelligence ai era computer systems injected by the new ai techniques are intelligent to perceive and understand the visual world and even smarter than humans in a number of specific tasks. Instance sensitive fully convolutional networks jifeng dai kaiming he yi li shaoqing ren and jian sun european conference on computer vision eccv 2016. Kaiming he research scientist facebook ai research fair.
In proceedings of the ieee conference on computer vision and pattern recognition pages 770 778 2016. Adversarial personalized ranking for recommendation. Xiangnan he lizi liao hanwang zhang liqiang nie xia hu and tat seng chua. We provide comprehensive empirical evidence showing that these.
Google scholar profile. Accurate large minibatch sgd. Google scholar digital library. Their combined citations are counted only for the first article.
Their combined citations are counted only for the first article. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs instead of learning unreferenced functions. Sepp hochreiter yoshua bengio paolo frasconi and jürgen schmidhuber. Google scholar cross ref.
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Enhancing The Interoperability Between Deep Learning Frameworks By Model Conversion Proceedings Of The 28th Acm Joint Meeting On European Software Engineering Conference And Symposium On The Foundations Of Software Engineering
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