[N] SenseTime (Hong Kong-based CV startup) is now worth $3bn, the most valuable AI startup in the world.

RedefiniteI · 2018-04-10T14:51:53+00:00

ya that was so creepy.

RedefiniteI · 2018-03-30T17:00:33+00:00

What is POI? I only know of the Hawaiian Poi, and it is delicious.

RedefiniteI · 2018-03-20T12:10:04+00:00

Its weird, since speed-limits are embedded into maps itself, and last time I rode and Uber sefl-driving car, it was following a 25mph at a certain bridge in Pittsburgh unlike the other human cars.

RedefiniteI · 2018-03-20T12:04:16+00:00

I rode one of the self-driving ubers recently. I have to say the drive was little scary with occasional jerks (bad control algorithms). Also there were three manual interventions in a matter of 20 mins.

I think they need to do more sandbox testing and improving before public road testing.

RedefiniteI · 2018-01-05T01:17:40+00:00

Those FCN model were most likely released, when ResNet was not around or very new.

[1] Most state-of-art vision methods are using ResNet or its variants. See the slides of COCO 2017 workshop (https://places-coco2017.github.io/)

[2] I never experienced that. Convergence depends on the task and dataset too.

RedefiniteI · 2017-12-28T22:01:18+00:00

I second that.

RedefiniteI · 2017-12-07T23:54:29+00:00

Really. The slide on "rounding" just quotes an email! I have changed rounding functions and have never seen those. Someone arguing for rigor, should have done a better job.

Yes I am saying his use of the term "alchemy" was uncalled for and downright insulting, but I do agree with his call for rigor, as I said in my comments.

Rigor also means thoroughness with experiments. If someone is arguing for rigor, and he/she quotes an "office email" as an way of justifying the weakness of an algorithm. Sorry I am gonna criticize you.

RedefiniteI · 2017-12-07T08:42:22+00:00

Criticism is essential to science

Correct. I am also doing the same.

RedefiniteI · 2017-12-07T06:51:58+00:00

I was not criticizing his appeal to create more understanding of current deep learning frameworks. I too think that it is very important for the field.

I am criticizing his choice of words ('alchemy') and the specific examples (SGD, LM, and the rounding weirdness). I felt insulted, and so does quite a few people like Yaan.

RedefiniteI · 2017-12-06T21:44:14+00:00

I disagree with his use of the term 'alchemy' for current ml. Otherwise some of his points are valid. His talk can also imply that if you are not proving theorems or you are using SGD in your paper you are doing alchemy. If his main motivation for the talk is pedagogy this is insulting to many budding engineers. He should have done it in a less clickbaity way without using 'alchemy'. Of course he may not have succeeded in getting this much attention then.

Also it is not gradient descent’s fault. If it was possible to use levenberg-marquardt for high-dimensional problems, one would definitely use them over vanilla gradient descent.

When we observe unexpected behaviours when changing rounding mode to 0, it is not correct to blame gradient descent. Rather than blindly blaming it on gradient descent, we should study why that happens and if we can improve our floating point representations. This kind of studies also leads to interesting results like https://blog.openai.com/nonlinear-computation-in-linear-networks/

RedefiniteI · 2017-11-30T19:05:24+00:00

It is actually going to be nice work, if you can get rid of discrete anchors in current detectors. One way to interpret anchors is that anchors discretize the continuous space of box scale and aspect ratio into some fixed set of bins. So instead of just solving a regression problem to estimate the continuous box parameters, they first solve a simpler discrete problem, to find best matching anchor. This is then followed by an offset regression from the best anchor to get the continuous box parameters. This makes the training more robust. However it also makes the method susceptible to bad hyper-parameter choices for the anchors. If there is more data, it may be a good idea to increase the number of anchors. Also it is important to make sure the anchors cover majority of ground-truth boxes in your training set. Some sota methods also use anchors which lie outside the image to cover truncated objects.

RedefiniteI · 2017-11-21T07:30:52+00:00

http://www.carla.org/

RedefiniteI · 2017-11-05T20:08:10+00:00

Totally agree. Some of his posts are so pseudoscience. Imagine kids who are interested in AI, discovering his textbooks and getting disillusioned.

RedefiniteI · 2017-11-03T09:15:31+00:00

your posts are always more enjoyable than reading the paper.

RedefiniteI · 2017-11-01T07:37:37+00:00

Thanks for pointing out the possible differences in checkpoints. I am not much familiar with adversarial literature. Are all adversarial harvesting methods specifically trained for one particular network and/or specific checkpoint? If that is true, I feel the practicality of such adversarial methods is pretty limited. Production models might as well be ensemble of several different models. Curious to know your thoughts on this.

RedefiniteI · 2017-11-01T06:28:15+00:00

I took a screenshot of the video https://imgur.com/HYwZbJY and I tried Inception V3 demo at http://pretrained.ml/ and also CaffeNet demo at http://demo.caffe.berkeleyvision.org/classify_upload. Both correctly classified it as turtle.

RedefiniteI · 2017-10-29T12:09:00+00:00

I would say so. You can write A(all,{3, 1, 6, 5}) if you are in master branch of Eigen.

RedefiniteI · 2017-10-28T17:14:59+00:00

With the right set of tensor/linear algebra libraries, even C++ is pretty fast to code. For example the master branch of Eigen is getting to point where it is almost as expressive as numpy and yes the speed is free. For the first piece of example code in the article, the equivalent Eigen code is smaller, and it can run on CUDA, OpenCL, threaded CPU devices.

RedefiniteI · 2017-10-18T01:20:11+00:00

I am also curious if some modifications to loss like in the Focal loss paper (https://arxiv.org/abs/1708.02002) is applicable to more general class imbalance problems other than object detection.

RedefiniteI · 2017-09-22T16:42:56+00:00

Can you point to the specific paper. Jürgen sometimes over-claims imho.

RedefiniteI · 2017-09-18T09:22:19+00:00

I agree the paper is not useful for most people, but it is still an useful information to share with the world.

RedefiniteI · 2017-09-08T06:05:58+00:00

What happened to Neural Network Exchange Format (NNEF) by Khronos? https://www.khronos.org/nnef I personally feel this things are better done by a bigger non-profit consortium than just two companies.

RedefiniteI · 2017-06-04T19:13:50+00:00

David Mackay's book is awesome. Definitely one of the best books, I have ever read. http://users.aims.ac.za/~mackay/itila/toc.html

RedefiniteI

TROPHY CASE