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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)
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Trading view scanner process -
1 - open trading view in your browser and select stock scanner in left corner down side .
2 - touch the percentage% gain change ( and u can see higest gainer of today)
3. Then, start with 6% gainer to 20% gainer and look charts of everyone in daily Timeframe . (For fno selection u can choose 1% to 4% )
4. Then manually select the stocks which are going to give all time high BO or 52 high BO or already given.
5. U can also select those stocks which are going to give range breakout or already given range BO
6 . If in 15 min chart📊 any stock sustaing near BO zone or after BO then select it on your watchlist
7 . Now next day if any stock show momentum u can take trade in it with RM
This looks very easy & simple but,
U will amazed to see it's result if you follow proper risk management.
I did 4x my capital by trading in only momentum stocks.
I will keep sharing such learning thread 🧵 for you 🙏💞🙏
Keep learning / keep sharing 🙏
@AdityaTodmal
1 - open trading view in your browser and select stock scanner in left corner down side .
2 - touch the percentage% gain change ( and u can see higest gainer of today)
Making thread \U0001f9f5 on trading view scanner by which you can select intraday and btst stocks .
— Vikrant (@Trading0secrets) October 22, 2021
In just few hours (Without any watchlist)
Some manual efforts u have to put on it.
Soon going to share the process with u whenever it will be ready .
"How's the josh?"guys \U0001f57a\U0001f3b7\U0001f483
3. Then, start with 6% gainer to 20% gainer and look charts of everyone in daily Timeframe . (For fno selection u can choose 1% to 4% )
4. Then manually select the stocks which are going to give all time high BO or 52 high BO or already given.
5. U can also select those stocks which are going to give range breakout or already given range BO
6 . If in 15 min chart📊 any stock sustaing near BO zone or after BO then select it on your watchlist
7 . Now next day if any stock show momentum u can take trade in it with RM
This looks very easy & simple but,
U will amazed to see it's result if you follow proper risk management.
I did 4x my capital by trading in only momentum stocks.
I will keep sharing such learning thread 🧵 for you 🙏💞🙏
Keep learning / keep sharing 🙏
@AdityaTodmal