https://arxiv.org/abs/1905.10027

​

Does this mean that we can use deep neural networks in TD(0) without worrying about its convergence?

https://medium.com/ai%C2%B3-theory-practice-business/practical-classes-for-robots-8a0ee915e2b8

Robots need to learn behaviors in intuitive and practical ways for widespread deployment in human environments. To learn a robot behavior end-to-end, we train a variant of the ResNet that maps eye-in-hand camera images to end-effector velocities. In our setup, a human teacher demonstrates the task via joystick. We show that a simple servoing task can be learned in less than an hour including data collection, model training and deployment time. Moreover, 16 minutes of demonstrations were enough for the robot to learn the task.

We are trialing the preregistration protocol with a brand new ICCV workshop.

In a nutshell:

1) Authors write a proposal paper illustrating their idea and the experimental protocol that will be used to evaluate it.

2) Paper is submitted to OpenReview and goes through an efficient two-phases double-blind peer review process.

3) After the paper gets accepted, experiments are conducted and results are reported.

–> The overall paper gets published irrespective of the results achieved, focus is on soundness/originality of ideas and scientific rigor of the experimental protocol.

Website: https://preregister.vision/

Given the recent controversy of who inspired who: https://twitter.com/fchollet/status/1132713291757711361

​

Let’s look at one of the early README’s of Keras itself: https://web.archive.org/web/20150522055317/https://github.com/fchollet/keras

Keras is a minimalist, highly modular neural network library in the spirit of Torch, written in Python / Theano so as not to have to deal with the dearth of ecosystem in Lua. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

​

Some people forget the history of torch, one of the original deep learning libraries which inspired many libraries thereafter.

Dear r/MachineLearning friends,

I just wanted to present my latest work to you. We, humans, have the ability to not only learn quickly from a few labelled examples, but we can also adjust our notions in light of new unlabelled data. I found this fascinating and wondered whether meta-learning an intrinsic loss function to do the same in the few-shot learning would, in fact, improve generalization performance. Turns out, it does. Any and all feedback is welcomed. You can be as harsh as you want. You can’t top reviewer #2 anyway.

Regards, Antreas

Paper: https://arxiv.org/abs/1905.10295

Code: Soon to follow (should be up before 29/05/2019)