He who makes Impossible, Possible
असाध्य साधक स्वामिन, असाध्यं तव किंवद
राम दूत कृपा सिंधो, मत्कार्यं साध्यप्रभो
Karmanghat Hanuman Temple,
Hyderabad.
One among the oldest Temple which Hanuman protected from Invasion...
@LostTemple7
More from Anu Satheesh 🇮🇳
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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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The entire discussion around Facebook’s disclosures of what happened in 2016 is very frustrating. No exec stopped any investigations, but there were a lot of heated discussions about what to publish and when.
In the spring and summer of 2016, as reported by the Times, activity we traced to GRU was reported to the FBI. This was the standard model of interaction companies used for nation-state attacks against likely US targeted.
In the Spring of 2017, after a deep dive into the Fake News phenomena, the security team wanted to publish an update that covered what we had learned. At this point, we didn’t have any advertising content or the big IRA cluster, but we did know about the GRU model.
This report when through dozens of edits as different equities were represented. I did not have any meetings with Sheryl on the paper, but I can’t speak to whether she was in the loop with my higher-ups.
In the end, the difficult question of attribution was settled by us pointing to the DNI report instead of saying Russia or GRU directly. In my pre-briefs with members of Congress, I made it clear that we believed this action was GRU.
The story doesn\u2019t say you were told not to... it says you did so without approval and they tried to obfuscate what you found. Is that true?
— Sarah Frier (@sarahfrier) November 15, 2018
In the spring and summer of 2016, as reported by the Times, activity we traced to GRU was reported to the FBI. This was the standard model of interaction companies used for nation-state attacks against likely US targeted.
In the Spring of 2017, after a deep dive into the Fake News phenomena, the security team wanted to publish an update that covered what we had learned. At this point, we didn’t have any advertising content or the big IRA cluster, but we did know about the GRU model.
This report when through dozens of edits as different equities were represented. I did not have any meetings with Sheryl on the paper, but I can’t speak to whether she was in the loop with my higher-ups.
In the end, the difficult question of attribution was settled by us pointing to the DNI report instead of saying Russia or GRU directly. In my pre-briefs with members of Congress, I made it clear that we believed this action was GRU.
12 TRADING SETUPS which experts are using.
These setups I found from the following 4 accounts:
1. @Pathik_Trader
2. @sourabhsiso19
3. @ITRADE191
4. @DillikiBiili
Share for the benefit of everyone.
Here are the setups from @Pathik_Trader Sir first.
1. Open Drive (Intraday Setup explained)
Bactesting results of Open Drive
2. Two Price Action setups to get good long side trade for intraday.
1. PDC Acts as Support
2. PDH Acts as
Example of PDC/PDH Setup given
These setups I found from the following 4 accounts:
1. @Pathik_Trader
2. @sourabhsiso19
3. @ITRADE191
4. @DillikiBiili
Share for the benefit of everyone.
Here are the setups from @Pathik_Trader Sir first.
1. Open Drive (Intraday Setup explained)
#OpenDrive#intradaySetup
— Pathik (@Pathik_Trader) April 16, 2019
Sharing one high probability trending setup for intraday.
Few conditions needs to be met
1. Opening should be above/below previous day high/low for buy/sell setup.
2. Open=low (for buy)
Open=high (for sell)
(1/n)
Bactesting results of Open Drive
Already explained strategy of #opendrive
— Pathik (@Pathik_Trader) May 27, 2020
Backtested results in 30 stocks and nifty, banknifty.
Success ratio : approx 40-45%
RR average 1:2
Entry as per strategy
Stoploss = Open level
Exit 3:15 PM Or SL
39 months 14 months -ve, 25 +ve
Yearly all 4 years +ve performance. pic.twitter.com/nGqhzMKGVy
2. Two Price Action setups to get good long side trade for intraday.
1. PDC Acts as Support
2. PDH Acts as
So today we will discuss two more price action setups to get good long side trade for intraday.
— Pathik (@Pathik_Trader) June 20, 2020
1. PDC Acts as Support
2. PDH Acts as Support
Example of PDC/PDH Setup given
#nifty
— Pathik (@Pathik_Trader) June 23, 2020
This is how it created long setup by taking support at PDC.
hopefully shared setup on last weekend helped. pic.twitter.com/2mduSUpMn5
