W e thank all reviewers for their comments. As you point out, we incorrectly asserted that it suffices to consider a one-layer neural network. Thank you for this interesting comment, we will clarify/fix these issues in the final version of our paper. I think that in Line 84, the authors should add a reference to "A Geometric Analysis of Phase Retrieval". R#3: We really appreciate your positive feedback!

Eq. (3), Eq. (5) and its model details) is consistent with the target task. The reward and constraints are reasonably designed. The experimental setting is remarkable (especially the Online Evaluation by simulator and the four proposed evaluation metrics) and the results are positive. However, this paper still has the following minor issues.

Originality: Although VAEs using a stick-breaking construction with Kumaraswamy distributions has been considered before (Nalisnick, Smyth, STICK-BREAKING VARIATIONAL AUTOENCODERS, 2017), the idea to use such a construction and extend it by mixing over the orderings to obtain a density more similar to a Dirichlet is new and interesting. Related work is adequately cited. Quality: The paper seems technically sound and claims are largely supported. Although Theorem 1 is a standard result, reiterating it is likely useful for the subsequent exposition. Experimental results show that the method outperforms some baselines, however, I feel that some additional experiments would be useful (see details below in Section 5. Improvements).

As your business considers where and how to invest for growth, it's crucial that you try to imagine how business will be conducted in the future. Rather than trying to incrementally improve what you already have today, get ahead of what will happen two years from now. What if instead of dozens of applications running through unrelated interfaces, every business task spanning IT, HR, Finance, and Administration could be conducted through one platform on a single interface? What if instead of contacting the IT department, the HR team, or the finance lead for minor issues, you could connect this overarching platform to an Intelligent Virtual Agent (IVA) designed to automate, inform and accelerate tasks? You would have entirely reinvented the way you do business.

Sunny Mishra, RPA CoE - Consulting Architect at ExxonMobil at ExxonMobil Great deck, but I have some minor issues. As a universal law, we cannot teach machines more intelligence than what we have at this point in time. So, instead of calling "Artificial Intelligence", we should drop the word "Artificial" and the word "Intelligence". I do not believe that there is any "artificiality" to any intelligence. First of all, Intelligence is gained/learned from us following "Rules" and Data" that we have associated with since we are born. This learning process dictates our outcome and that is fixed. There is no such thing as "Gut Feeling". It does not exist....me simply make it up to make any point heard across. So no matter how big or complex machines we build, it will only learn to behave by the "Rules" and the associated "Data", which always has a "fixed" outcome or result, same as ours. By laws of universe, without evolving, we would have remained as cave dwellers. So, every day of our life, we observe new rules and results which evolves us to the next level. But, If for example, I am locked up in a dark room, isolated from observing any new rules or data or results, I will be at the same level of intelligence as the day I get locked in. Similarly, if we cannot generate any new intelligence in isolation, we cannot feed the robots any new rules and hence they will remain at a certain level of intelligence for ever. What I am trying to say here is "...AI will never be more intelligent than its creator...". 3 months ago Reply Are you sure you want to Yes No Your message goes here Great deck, but I have some minor issues. As a universal law, we cannot teach machines more intelligence than what we have at this point in time. So, instead of calling "Artificial Intelligence", we should drop the word "Artificial" and the word "Intelligence". I do not believe that there is any "artificiality" to any intelligence. First of all, Intelligence is gained/learned from us following "Rules" and Data" that we have associated with since we are born.

Sunny Mishra, RPA CoE - Consulting Architect at ExxonMobil at ExxonMobil Great deck, but I have some minor issues. As a universal law, we cannot teach machines more intelligence than what we have at this point in time. So, instead of calling "Artificial Intelligence", we should drop the word "Artificial" and the word "Intelligence". I do not believe that there is any "artificiality" to any intelligence. First of all, Intelligence is gained/learned from us following "Rules" and Data" that we have associated with since we are born.

In this post, I will demonstrate how to use Google Colab for fastai. Colab is a Google internal research tool for data science. They have released the tool sometime earlier to the general public with a noble goal of dissemination of machine learning education and research. Although it's been for quite a while there is a new feature that will interest a lot of people. You can use GPU as a backend for free for 12 hours at a time.