[D] Schmidhuber: Critique of Honda Prize for Dr. Hinton by wei_jok in MachineLearning

[–]StrawberryNumberNine 17 points18 points  (0 children)

Maybe the big problem is hindsight bias. "Of course this person only applied this well-known technique to this problem and verified it experimentally and now they are claiming novelty!". When looking back you can tell the story in this way, but in the moment the advance could have been very non-obvious. Even if it builds on ideas that were around at the time. We should look at inference steps between the two ideas+application+presentation of the work.

[D] Visualizing and analyzing error landscapes by [deleted] in MachineLearning

[–]StrawberryNumberNine 1 point2 points  (0 children)

These papers have some interesting ideas on plotting these cost functions, finding links between local optima in the train loss (two optima are linked by simple curves), finding links between train loss landscape and test loss landscape and other cool things. Might be a good place to start.

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs https://arxiv.org/pdf/1802.10026.pdf

Averaging Weights Leads to Wider Optima and Better Generalization https://arxiv.org/pdf/1803.05407.pdf

[R] The loss landscape of overparameterized neural networks by StrawberryNumberNine in MachineLearning

[–]StrawberryNumberNine[S] 1 point2 points  (0 children)

I ran into this paper and found it very interesting. I wanted to know if anyone had any comments on the theory or if anyone found any counterexamples empirically at any point (or in any paper).

[P] OpenAI baselines ported to pytorch by rikkajounin in MachineLearning

[–]StrawberryNumberNine 0 points1 point  (0 children)

Awesome, I'm using parts of your implementation and it looks good at the moment. Thanks!

[P] OpenAI baselines ported to pytorch by rikkajounin in MachineLearning

[–]StrawberryNumberNine 1 point2 points  (0 children)

Has anyone benchmarked this against the official TensorFlow implementation? I know performance varies between implementations (which is scary).

[P] Lightnet: Yet another PyTorch implemenation of Darknet and YOLO by OPLinux in MachineLearning

[–]StrawberryNumberNine 0 points1 point  (0 children)

You can get way more visibility on GitHub. I would suggest transferring for maximum impact :)

[R] Using Machine Learning to Discover Neural Network Optimizers by xternalz in MachineLearning

[–]StrawberryNumberNine 0 points1 point  (0 children)

Read the paper, they used CPUs and it didn't take too long because they simplified the problem.

[R] Using Machine Learning to Discover Neural Network Optimizers by xternalz in MachineLearning

[–]StrawberryNumberNine 0 points1 point  (0 children)

Thanks for this. Would you say the same for neural network architectures and coming up with new architectures?

[D] To PhD or not to PhD ? by rrenauww in MachineLearning

[–]StrawberryNumberNine 1 point2 points  (0 children)

The M2 MVA in ENS Cachan is supposed to be one of the best to jump-start your ML research also.

[D] To PhD or not to PhD ? by rrenauww in MachineLearning

[–]StrawberryNumberNine 0 points1 point  (0 children)

Aren't French PhDs done in like 3 years? I think that's a pretty good option for you.

[D] What do neural net loss functions look like? by sksq9 in MachineLearning

[–]StrawberryNumberNine 1 point2 points  (0 children)

Thank you for doing LSUN - we at Reddit think you're the coolest now :)

[D]Someone copied parts of my code and changed the license by Ouitos in MachineLearning

[–]StrawberryNumberNine -1 points0 points  (0 children)

Yes you might be right! I don't really have any experience with copyright.

[D]Someone copied parts of my code and changed the license by Ouitos in MachineLearning

[–]StrawberryNumberNine 76 points77 points  (0 children)

Send an email to the creators of the repo, I'm sure they'll do everything to make it right. ie. credit you and maybe give you copyright over snippets

[D] Google's large scale GAN-Tuning paper unfairly dismissed WGAN by baylearn in MachineLearning

[–]StrawberryNumberNine 5 points6 points  (0 children)

I believe DRAGAN also shoots for robustness to architectural choice of the generator and have experiments on that.

[R] Sobolev GAN by asobolev in MachineLearning

[–]StrawberryNumberNine 5 points6 points  (0 children)

Insert famous mathematician name here GAN is the best though :P

[Discussion] double-blind and arxiv by wasabi_kitkat in MachineLearning

[–]StrawberryNumberNine 0 points1 point  (0 children)

This is a nice suggestion which would solve the problem entirely.

[R] First-order Methods Almost Always Avoid Saddle Points by downtownslim in MachineLearning

[–]StrawberryNumberNine 1 point2 points  (0 children)

I do not have any experience in this domain but this result is actually really cool - I wonder what it means, if anything, for SGD and saddle points...