Hello good people!

Background : I need your help. First of all, I am out of my elements here. I am just learning about RL. I got a job on it luckily. It’s more code oriented but I need some concepts as well. I decided to throw myself in the water to break my stagnation. I hope you can help me here.

Issue : I want to run the code from the Rainbow paper. When I run it with default arguments it just keep running. I think by default it is set to run 5 million episodes(T-max = 50e6). I want to run one successful run before I start playing with it so I have an idea on what the result is supposed to look like. Should I just change the T-max variable? There are about 20 more arguments and I am not sure if it affects other or not. For example, I think the target-update is related to this. And since my concepts are not so clear, I could use some help here.

I hope I was clear, if not please ask me here.

Edit : spelling and stuff

This decade really belonged to Deep Learning and in a bid to recap, I have written a post covering the most significant contributions to it in the past decade.

The post is split into the 3 main domains of Deep Learning: Natural Language Processing, Computer Vision and Reinforcement Learning. Each subtopic covers an important milestone that has shaped Deep Learning as we know it today, with links to the original papers. The aim is to look back at what will be remembered from this decade and stem discussion regarding what areas of Deep Learning will play a major role in the 2020s.

https://medium.com/%C3%A9clair%C3%A9/a-decade-in-deep-learning-19b611588aa0?source=friends_link&sk=7567f3da9e88ae105289376a2f9f4485

I wrote a blog post a while ago that I thought I’d post here for discussion. It discusses some building blocks for AGI, e.g. intuitive physics, intuitive psychology, intrinsically-motivated RL, etc. I also discuss what I think may be most promising.

Please criticize, discuss and let me know if there’s any more recent work in any of these fields that has changed the landscape since I wrote this piece.

I’m still pretty confident in it other than:

use one giant neural network, or use more than one

I’d generalize this to “use one end-to-end learning system, or more than one”.

I am currently working with a company that has a lot of data issues in terms of Quality and Quantity. By quality, I mean that they don’t have standardized, clean, and structured data. And in quantity, they don’t have a good amount of historical data.

After doing data cleaning and wrangling, the final dataset is only suitable for descriptive analytics. And the management wants to know the RoI from implementing this use-case.

From my PoV, descriptive analytics will only aid the decision-makers in knowing the status of different components of their projects and identifying any issues related to them. Any financial value that can be generated is in the hands of these decision-makers. But the management wants to put a price tag to it and I don’t know how to guesstimate it.

Any ideas?

https://openreview.net/forum?id=Bke_DertPB

https://arxiv.org/abs/1907.05681

source code: https://github.com/dterjek/adversarial_lipschitz_regularization

TL;DR: alternative to gradient penalty

(FKA Virtual Adversarial Lipschitz Regularization)

Hi, I’m trying to adjust bids(Max. CPC) of products, advertised on google ads, based on their past performance like Clicks, Impressions, Conversions, Product Price, etc. Google provides those data in CSV format. Now I want to utilize those data for bidding intelligently instead of using Smart Bidding feature provided by Google itself. Is there any good Machine Learning algorithm that can be used for such a case. Or, no need to use any ML algorithm here? Or, any better idea that can be applied for this use-case? Thanks in advance.

Hi all, I am currently trying to implement AAGAN paper

I found some inconsistency in the paper.

In Generator we have 5 encoding blocks. As far as I understand they change number of channels in such way: 32 – 64 64- 128 128- 256 256 – 512 512 – 1024

But the residual layers that follow the enBlocks have c512. What should I do with it? I make res layers with c1024 but I don’t know is it correct. Because it seems my network does not work correctly (it stucks at some point and G loss decreases VEERY slowly, while the D converges pretty fast. The dehazed images look better than hazed, but they are noticeably darker than the original ones and they do not remove haze effect completely.

I will share my code today later after fixing some code issues.

Hi, i’m considering writing a paper on upcoming ICML 2020, with my theoretical works and Primary experiments(e.g., MNIST things). At the moment my results is quite closed/or outperform some other SOTA models, but i think there’s no time to finish other two or three experiments for main conference paper. If i’d accepted with some workshop tracks in a conference, is it allowed to republish the same paper with supplement experiments/and some other theoretical claiming? If it’s ok, then are there any possible penalty for future submission? (e.g., lack of novelty compared to my prev workshop paper, …) Thank you in advance and happy new year!

Hello,

I am following Lex’s podcast recently, however, I have noticed some researchers like Anima says that he is a clown and mouthpiece of Elon musk. Also, he had blocked many peoples on Twitter, which is maybe questionable? I would like to ask the community here how do they see the podcast is I am still newbie in the field?

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Thanks,