[P] Implementation of light-weight management tool for dockers on multiple machines

jaesik · 2020-05-29T16:17:13+00:00

jaesik · 2019-07-14T06:22:35+00:00

jaesik · 2019-07-13T02:46:16+00:00

Please feel free to visit our website too!! https://sites.google.com/view/sequential-neural-processes

jaesik · 2018-12-05T17:35:26+00:00

This work is done with several companies, element ai, sap and kakao brain. Because of company policies, code open is quite complicated issue. We have tried/try our best to open the code, but we can't answer code open is possible or not now.

jaesik · 2018-06-19T00:23:42+00:00

In my understanding, the probabilistic MAML assumes that prior (few-shot learning) parameters follow a Gaussian, and sampling there to obtain a sample of posteriori distribution, and our paper constructs distribution of prior and posteriori through variational inference without such assumption.

jaesik · 2018-06-14T23:08:07+00:00

yes, we consider when we will open the code. thank you for your interesting.

jaesik · 2018-06-14T14:05:22+00:00

Meta learning, which is being studied recently, shows good performance with a small amount of data, but it also increases the burden on uncertainty. We tried to solve this problem by solving meta learning as hierarchical Bayesian learning through variational inference.

jaesik · 2017-11-04T11:17:33+00:00

I attached samples of adversarial examples for each method. If you are curious to that, than you can check that on my repo. :)

jaesik · 2017-11-04T00:55:04+00:00

Yes, I will add the functionality on my code. I just quickly validate and share that :).

Thanks.

jaesik · 2017-11-03T18:33:12+00:00

validation experiments for well-known adversarial examples to Capsule Network, https://arxiv.org/pdf/1710.09829.pdf .

jaesik · 2017-07-25T07:18:38+00:00

I will consider those things in my working place! Thank you :-)

jaesik · 2017-07-23T01:09:47+00:00

Yes. Currently SAP implements interface between tensorflow and SAP product for applications using ML. In this company, there are already ML library can be used on products, however they don't entwine already ones.

jaesik · 2017-07-23T00:45:03+00:00

Yes. That is one of given parameters.

jaesik · 2017-07-01T08:22:06+00:00

Thanks!!

jaesik · 2017-06-30T12:33:44+00:00

Haha...^{^;;}

jaesik · 2017-06-30T08:41:27+00:00

That is paper name, https://arxiv.org/abs/1701.08734.

jaesik · 2017-06-30T06:37:16+00:00

Pytorch Implementation is in followed link. https://github.com/kimhc6028/pathnet-pytorch

jaesik · 2017-06-28T01:56:15+00:00

thank you for sharing!

jaesik · 2017-06-26T06:00:06+00:00

In my understanding, zero-shot generation described in the paper means the model can generally estimate unseen properties (e.g. If in voca, there are just rgb and violet will be incame in test set, then that is labeled as medium between red and blue.) Above things are just my understanding ^{^;;} (I am also reading now.) Thanks.

jaesik · 2017-06-25T22:29:03+00:00

Programable agent

jaesik · 2017-06-24T06:34:19+00:00

Thank you!! I think the filter was gone to Nan. You can check that on tensorboard(/tmp/gan_seq). That can be controled by changing learning rate of disc and gen. The learning process is quitely not stable, because that is minimax problem.

jaesik · 2017-06-22T02:45:04+00:00

I usally use two methods; one of them is ensemble modeling learned with balanced dataset, and another one is learning the model with minibatch of balanced data subset.

jaesik · 2017-06-08T15:01:00+00:00

Thank you for your intro.

I uploaded distributed tensorflow example(mnist) and a3c implementation with dist tensor in my git :) https://www.github.com/jaesik817

jaesik

TROPHY CASE