"Strong woman creates healthy society" - said yesterday, here is an example. She is a creator of the world. Don't know in the middle how they have lost the track...#tali_ban https://t.co/BqYQPjr6tC
— SSanatani\U0001f1ee\U0001f1f3\U0001f1e8\U0001f1e6 (@Sanatani_bhau) October 8, 2021
A Women is a spiritual mother...that's why we call her Janani ( a creator)...she is happy everything is at peace, the ambience is beautiful. Don't compare her or even equate her, she is much more than a men.
Women is colourful....... pic.twitter.com/FUrN90bbnL
— Mrudapriye \U0001f1ee\U0001f1f3 \U0001f6a9 (@MANGALADR1) December 28, 2020
More from All
How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
The best morning routine?
Starts the night before.
9 evening habits that make all the difference:
1. Write down tomorrow's 3:3:3 plan
• 3 hours on your most important project
• 3 shorter tasks
• 3 maintenance activities
Defining a "productive day" is crucial.
Or else you'll never be at peace (even with excellent output).
Learn more
2. End the workday with a shutdown ritual
Create a short shutdown ritual (hat-tip to Cal Newport). Close your laptop, plug in the charger, spend 2 minutes tidying your desk. Then say, "shutdown."
Separating your life and work is key.
3. Journal 1 beautiful life moment
Delicious tacos, presentation you crushed, a moment of inner peace. Write it down.
Gratitude programs a mindset of abundance.
4. Lay out clothes
Get exercise clothes ready for tomorrow. Upon waking up, jump rope for 2 mins. It will activate your mind + body.
Starts the night before.
9 evening habits that make all the difference:
1. Write down tomorrow's 3:3:3 plan
• 3 hours on your most important project
• 3 shorter tasks
• 3 maintenance activities
Defining a "productive day" is crucial.
Or else you'll never be at peace (even with excellent output).
Learn more
How to be 5x more productive.
— Ben Meer (@SystemSunday) August 1, 2022
A best-selling author\u2019s 3-3-3 Method:
2. End the workday with a shutdown ritual
Create a short shutdown ritual (hat-tip to Cal Newport). Close your laptop, plug in the charger, spend 2 minutes tidying your desk. Then say, "shutdown."
Separating your life and work is key.
3. Journal 1 beautiful life moment
Delicious tacos, presentation you crushed, a moment of inner peace. Write it down.
Gratitude programs a mindset of abundance.
4. Lay out clothes
Get exercise clothes ready for tomorrow. Upon waking up, jump rope for 2 mins. It will activate your mind + body.
You May Also Like
1/OK, data mystery time.
This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE
That's weird. Income inequality in China is legendary.
Let's check this number.
2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".
The report is available here:
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.
The graph cites the World Development Indicators as its source for the income inequality data.
4/The World Development Indicators are available at the World Bank's website.
Here's the Gini index: https://t.co/MvylQzpX6A
It looks as if the latest estimate for China's Gini is 42.2.
That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.
I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.
This New York Times feature shows China with a Gini Index of less than 30, which would make it more equal than Canada, France, or the Netherlands. https://t.co/g3Sv6DZTDE
That's weird. Income inequality in China is legendary.
Let's check this number.
2/The New York Times cites the World Bank's recent report, "Fair Progress? Economic Mobility across Generations Around the World".
The report is available here:
3/The World Bank report has a graph in which it appears to show the same value for China's Gini - under 0.3.
The graph cites the World Development Indicators as its source for the income inequality data.
4/The World Development Indicators are available at the World Bank's website.
Here's the Gini index: https://t.co/MvylQzpX6A
It looks as if the latest estimate for China's Gini is 42.2.
That estimate is from 2012.
5/A Gini of 42.2 would put China in the same neighborhood as the U.S., whose Gini was estimated at 41 in 2013.
I can't find the <30 number anywhere. The only other estimate in the tables for China is from 2008, when it was estimated at 42.8.
