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> It should be "Neural networks Confronts...". I disagree, for the following reasons: - The paper is called The Elephant in the Room. Technically the paper co
by dpwm 8y ago
> It should be "Neural networks Confronts...".
I disagree, for the following reasons:
- The paper is called The Elephant in the Room. Technically the paper confronted the popular machine learning approaches, so the reversal would have been a more eye catching headline. "The Elephant in the Room confronts Machine Learning" is a rare opportunity to honestly play with word order in a way that would arguably be more attention-grabbing.
- Machine learning is more general than Convolutional Neural Networks, but less general than AI. This seems appropriate for this type of popular science publication.
- There are approaches that use attention and RNNs and they are an interesting approach, but by no means mainstream nor producing state-of-the-art classification accuracies. Actually, second take is exactly what an attention-based RNN can do.
- The last time I checked the state-of-the-art solutions to image classification involve Convolutional Neural Networks.
If there was an approach that was even close to approaching ConvNet performance, I would agree that it is perhaps unfair to condemn the entire field. Even then, we would be in the territory of generalizations in headlines, which are often fair. "{party} proposing legislation" is a probably bigger generalization of headline that would generally pass without criticism.
- antpls 8y ago> - The paper is called The Elephant in the Room. How is this argument related to the confusion of the terms Machine Learning and Neural Networks...? > - Machine learning is more general than Convolutional Neural Networks, but less general than AI. This seems appropriate for this type of popular science publication. The most appropriate term is the most precise one, in this case : neural networks > - There are approaches that use attention and RNNs and they are an interesting approach, but by no means mainstream nor producing state-of-the-art classification accuracies. Actually, second take is exactly what an attention-based RNN can do. Correct, but you are still talking about neural networks... > - The last time I checked the state-of-the-art solutions to image classification involve Convolutional Neural Networks. And again neural networks. ️
- dpwm 8y ago> How is this argument related to the confusion of the terms Machine Learning and Neural Networks...? We're talking about the title. > The most appropriate term is the most precise one, in this case : neural networks That is generalizing RNNs and CNNs into "neural networks". If I take a popular CNN architecture, chop off the final dense layer and activation and attach an SVM classifier, is it an SVM or a convnet I am using? Somehow the title "The Elephant in the Room confronts faster_rcnn_inception_resnet_v2_atrous_coco, faster_rcnn_nas_coco, ssd_mobilenet_v1_coco, mask_rcnn_inception_resnet_v2_atrous_coco, mask_rcnn_resnet101_atrous_coco." (or the reverse) doesn't have the same ring to it and is probably inappropriate for the audience.