วีดีโอ

different tasks bring different learning mode boundaries, how can we ensure that the robot can classify every task's boundary when meet a multi-task or a multimodal problem?

The work is highly interesting. It has a lot of future. The Confidence of the candidate😊 is very high.... 😊😊😊

Excellent talk....

Incredible research and vision for future

🙏🏻

This should have more views!

It would be great if you could repeat the asked question (11:13:16). Now it's a game of Jeopardy to guess the question. Thanks for sharing anyways! Lab tour fly was nice too.

Thank you for this! presentation starts 19:33 :)

This is so intriguing. I am so inspired

Thanks!

Promo-SM 😕

It's a good presentation. Very useful for me! Thanks a lot!

Talk starts 32:57

Does this presentation correspond to some paper? It would be nice to have access to the slides/citations, very interesting stuff

Talk starts at 16:30

Interesting explanation

I think a sense of humor in robotics would lead to clownish appeal.

Starts at 15:25

Great one, thank you! I would like to be part of it. One day it will be possible.

Content actually starts at 17:30, and the speaker's audio starts at 18:30

For the initial conditions that work, have anybody look at how much wiggle room you have. Is there an epsilon-neighborhood of the initial state you can safely start from, and how small is epsilon?

is there any formal course that covers multi agent RL? I find it confusing thus far. Thanks!

1:07 subtitles "my experience with drugs couldn't be better"

I was always confused by MA-MDP. You made it look very simplistic. Mathematical notations were very concise and research paper ready.

how to do convolution lstm and other things on JAX more tutorials please sir Mathew Johnson

u and a confuse me

Very insightful

Thank you sir

Nazi.

Great work!

Really enjoyed the presentation. The 'Puzzle' slide is problematic. All three have 'lots of wiring', the camera has smaller wires in a better package.

How do you linearize the KG without getting into exponential complexity ?

How do you combine selfsupervised learning with declarative knowledge ?

👌

Which book I can refer to

The diehold foundation is along with the suspecious observers pushing a 12 thousand year cycle of super nova and magnetic reversal which brings a catastrophic event please debate them on their theory's they have tens of thousands of people following them. I fear that the theory might be partially right. Find them on TH-cam under those names.please look into this thanking you in advance Carl.

This is a great talk. Personally I haven't had the prerequisite for manifold learning, but the idea behind hybrid message passing is quite profound. Just wandering: if you have a Bayesian GNN where the prior encodes the linear assumption, would that be equivalent to the GNN + PGM model presented here? or is there a limit to the expressiveness of a Bayesian prior?

Great talk, thank you for uploading.

Amazing work

Talk starts at 6:00

Can we get the PPT plzz.....??

good

Starts at 12:08

Starts around 7:03

this was amazing, thank you so much for uploading it!!

Starts at 11:20

thank you for sharing information

exellent

Here from Reddit. (Y)