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> At the same time Schmidhuber's tone and anal-retentive attitude explains why he is considered so annoying. The people calling him annoying are generally hang

> At the same time Schmidhuber's tone and anal-retentive attitude explains why he is considered so annoying. The people calling him annoying are generally hangers-on trying to display that they're part of the group - people who took a class once 20 years ago and think they need now to come explain. He's a lovely man, and he's 100% correct to be angry that people are getting rich and famous while not citing him. It's an ethical disaster. I really wish HN people wouldn't pile onto him this way. . > The field is full of huge egos and overly simplified narratives from a singular point of view. Stop it. Primacy in publishing is a core topic in science. If these people were at universities, and not Google or Facebook, they'd have been fired for what they're doing. Schmidthuber doesn't actually have a huge ego. He's just saying "guys, stop re-publishing my papers and claiming you came up with it." You should really be deeply disgusted in Hinton right now, not explaining Schmidthuber's ego. Hinton is being a plagiarist *and he knows it*. This isn't an accident and it isn't the first time. Hinton is committing fraud, according to academic doctrine. This is an automatic termination at most universities on Earth, even through tenure. It's time to stop making excuses for these rich men cherry picking other peoples' work for their and their employers' gain. Schmidthuber is, if anything, being remarkably calm and polite, for having been robbed blind by megacorporations of credit and wealth for decades.

random314 4y ago
> Hinton is being a plagiarist and he knows it. What are some of the papers he has plagiarized?
- P-NP 4y ago
  They are listed here: https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html https://people.idsia.ch/~juergen/scientific-integrity-turing...
  - random314 4y ago
    These are some examples of non citations in talks (not papers), not plagiarism. I will discuss a representative example here. > 9. LBH claim ReLUs enabled deep learning to outperform previous methods for object recognition, referring to their GPU-based ImageNet 2012 winner called AlexNet,[GPUCNN4] without mentioning that our earlier groundbreaking deep GPU-based DanNet[GPUCNN1-3,5-8][DAN] did not need ReLUs at all to win 4 earlier object recognition competitions and to achieve superhuman results already in 2011[GPUCNN1-8][R5-6] (see Sec. XIV). If we click and look into the details, Alexnet won imagenet - a general purpose image recognition dataset. Whereas Dannet worked on specific domains- Chinese handwriting recognition, mitosis etc. So Dannet is not comparable in impact to Alexnet at all. ReLUs are in all complex DNNs now - wouldn't have happened if ReLUs are redundant as implied by Schmidhuber. https://people.idsia.ch/~juergen/computer-vision-contests-won-by-gpu-cnns.html https://people.idsia.ch/~juergen/computer-vision-contests-wo...
    - P-NP 4y ago
      You conveniently left out the plagiarism part regarding ReLUs: > 8. LBH devote an extra section to rectified linear units (ReLUs), citing papers of the 2000s by Hinton and his former students, without citing Fukushima who introduced ReLUs in 1969[RELU1-2] (see Sec. XIV). This is only one of many concrete examples given. DanNet obviously worked on all kinds of image data, otherwise it would not have won all those competitions before the similar AlexNet. However, the CNN pioneer was Fukushima who introduced CNNs and ReLUs.
      - random314 4y ago
        The key result is not the introduction of ReLU, this is a misdirection. The key result is the outstanding performance on a general image data set by Alexnet. If the predecessors did all of the work, why was Hinton's lab the first to produce these results. ReLU is an absurdly simple gate. The question revolved around its effectiveness, which was proven by Hintons lab. The key result is the outstanding performance on imagenet. If Schmidhuber was the actual pioneer, why wasn't he able produce the same results before Hinton? NN were known to work for hand writing recognition since the 90s (including papers by Hinton). Dannet being able to do it for Chinese characters in 2010s is unremarkable.

random314 4y ago

> Hinton is being a plagiarist and he knows it. What are some of the papers he has plagiarized?

P-NP 4y ago
They are listed here: https://people.idsia.ch/~juergen/scientific-integrity-turing-award-deep-learning.html https://people.idsia.ch/~juergen/scientific-integrity-turing...
- random314 4y ago
  These are some examples of non citations in talks (not papers), not plagiarism. I will discuss a representative example here. > 9. LBH claim ReLUs enabled deep learning to outperform previous methods for object recognition, referring to their GPU-based ImageNet 2012 winner called AlexNet,[GPUCNN4] without mentioning that our earlier groundbreaking deep GPU-based DanNet[GPUCNN1-3,5-8][DAN] did not need ReLUs at all to win 4 earlier object recognition competitions and to achieve superhuman results already in 2011[GPUCNN1-8][R5-6] (see Sec. XIV). If we click and look into the details, Alexnet won imagenet - a general purpose image recognition dataset. Whereas Dannet worked on specific domains- Chinese handwriting recognition, mitosis etc. So Dannet is not comparable in impact to Alexnet at all. ReLUs are in all complex DNNs now - wouldn't have happened if ReLUs are redundant as implied by Schmidhuber. https://people.idsia.ch/~juergen/computer-vision-contests-won-by-gpu-cnns.html https://people.idsia.ch/~juergen/computer-vision-contests-wo...
  - P-NP 4y ago
    You conveniently left out the plagiarism part regarding ReLUs: > 8. LBH devote an extra section to rectified linear units (ReLUs), citing papers of the 2000s by Hinton and his former students, without citing Fukushima who introduced ReLUs in 1969[RELU1-2] (see Sec. XIV). This is only one of many concrete examples given. DanNet obviously worked on all kinds of image data, otherwise it would not have won all those competitions before the similar AlexNet. However, the CNN pioneer was Fukushima who introduced CNNs and ReLUs.
    - random314 4y ago
      The key result is not the introduction of ReLU, this is a misdirection. The key result is the outstanding performance on a general image data set by Alexnet. If the predecessors did all of the work, why was Hinton's lab the first to produce these results. ReLU is an absurdly simple gate. The question revolved around its effectiveness, which was proven by Hintons lab. The key result is the outstanding performance on imagenet. If Schmidhuber was the actual pioneer, why wasn't he able produce the same results before Hinton? NN were known to work for hand writing recognition since the 90s (including papers by Hinton). Dannet being able to do it for Chinese characters in 2010s is unremarkable.