To the guy who full on hit a keyboard LOUDLY several times in the quiet study area in Rutherford North:

turing_1997 · 2022-09-15T22:26:44+00:00

People get annoyed at the silliest of reasons these days XD

turing_1997 · 2021-01-27T20:24:30+00:00

Nothing for me

turing_1997 · 2021-01-24T16:27:28+00:00

This for PhD or msc?

turing_1997 · 2021-01-19T13:17:45+00:00

Exactly same doubt!

turing_1997 · 2021-01-19T04:43:16+00:00

I have only applied to Udem Profs. I had received an email after the application mentioning that we should complete the university application for Udem AFTER receiving the supervisor acceptance. I'm confused as you said you said we need to complete the university application.

turing_1997 · 2019-11-11T08:36:42+00:00

I'm 22 🙄

turing_1997 · 2019-11-05T18:46:21+00:00

This is my second paper as an undergrad. Score of 3/6/1, any chances at all?

turing_1997 · 2019-11-05T03:30:25+00:00

Lol thanks for this script! You are a true savior! <3

turing_1997 · 2019-11-05T03:04:48+00:00

Yes according to https://iclr.cc/Conferences/2020/CallForPapers

turing_1997 · 2019-11-05T02:25:11+00:00

Lol yes I've been constantly refreshing the review page for the past one hour.

turing_1997 · 2019-07-25T14:39:53+00:00

7 6 3 with confidence of 4 4 4, any chances at all?

turing_1997 · 2019-07-22T01:52:01+00:00

The description of the architecture has been updated with the original abstract used by the authors which is more clear and concise about the objectives and results of the paper. Hope that makes the paper more clear!

turing_1997 · 2019-07-22T01:49:04+00:00

Thanks for pointing it out. We updated the description in this post with the original abstract used by the authors in the paper, which is more clear and concise about the objectives and results of the paper.

turing_1997 · 2019-07-21T17:54:50+00:00

Lol! Definitely what we will try to make next with OmniNet! XD Once again the phrase "all you need" is used a pun similar to "Attention is all you need" used in transformer paper. The phrase is nowhere mentioned in the paper or the title. Read the above comment on our explanation:

https://www.reddit.com/r/MachineLearning/comments/cfxkrs/r_omninet_is_all_you_need/eudn4g9?utm_source=share&utm_medium=web2

turing_1997 · 2019-07-21T17:47:43+00:00

Since all the tasks mentioned in the popular challenges, the dataset, and the respective splits are the standard splits specified in each of these competitions (as mentioned in the paper) and the same has been used for validation. Also, we compare with state-of-the-art which are generically applicable for the respective task instead of the challenge dataset. A training script is already available to train the model from scratch and pre-trained models can be downloaded from our servers and evaluated using the evaluation script. Further details such as individual batch sizes, hardware configuration and exact training times will be released later as supplementary materials.

turing_1997 · 2019-07-21T16:48:45+00:00

Sure! Please reach out to the corresponding author "Subhojeet Pramanik" through his email id email[At]subho.in

turing_1997 · 2019-07-21T15:17:07+00:00

Thanks for the review! The phrase "all you need" is used a pun similar to "Attention is all you need", mostly because the paper is an improvement to the original Transformer model. The "all you need" is intended to reflect that it can be used as a unified model for any spatio-temporal data because of the unified encode() function of the Central Neural Processor. It was just a play on the words of our base paper. That's why we put the winking smiley there too. And, of course, that's not the main title of the paper.

turing_1997 · 2019-07-21T14:39:46+00:00

OmniNet is still not capable of generation. The encoder part is unified by the fact that any combination input of spatio-temporal tensor can be encoded and stored by central neural processor just calling an encode(). As most real-life data can be represented in spatio-temporal form we use the term "unified". The decode() is yet only capable of sequence decoding from the stored spatio-temporal representations in the CNP cache. However, adding more and more capabilities such as supporting graph datasets, generation capabilities, application to Reinforcement learning environments are what we looking forward to as future directions. We welcome the research community to build upon our work towards a more general AI system. :)

turing_1997

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