Ok, I'll hop on this Elizabeth Warren/Native American testing bandwagon (gently and non-politically).
I've been a @23andMe customer for a while, and have followed their ancestry updates closely.
All is more or less as expected....except for this bit about Native American.
https://t.co/lTNeT7IsQg
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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A THREAD ON @SarangSood
Decoded his way of analysis/logics for everyone to easily understand.
Have covered:
1. Analysis of volatility, how to foresee/signs.
2. Workbook
3. When to sell options
4. Diff category of days
5. How movement of option prices tell us what will happen
1. Keeps following volatility super closely.
Makes 7-8 different strategies to give him a sense of what's going on.
Whichever gives highest profit he trades in.
2. Theta falls when market moves.
Falls where market is headed towards not on our original position.
3. If you're an options seller then sell only when volatility is dropping, there is a high probability of you making the right trade and getting profit as a result
He believes in a market operator, if market mover sells volatility Sarang Sir joins him.
4. Theta decay vs Fall in vega
Sell when Vega is falling rather than for theta decay. You won't be trapped and higher probability of making profit.
Decoded his way of analysis/logics for everyone to easily understand.
Have covered:
1. Analysis of volatility, how to foresee/signs.
2. Workbook
3. When to sell options
4. Diff category of days
5. How movement of option prices tell us what will happen
1. Keeps following volatility super closely.
Makes 7-8 different strategies to give him a sense of what's going on.
Whichever gives highest profit he trades in.
I am quite different from your style. I follow the market's volatility very closely. I have mock positions in 7-8 different strategies which allows me to stay connected. Whichever gives best profit is usually the one i trade in.
— Sarang Sood (@SarangSood) August 13, 2019
2. Theta falls when market moves.
Falls where market is headed towards not on our original position.
Anilji most of the time these days Theta only falls when market moves. So the Theta actually falls where market has moved to, not where our position was in the first place. By shifting we can come close to capturing the Theta fall but not always.
— Sarang Sood (@SarangSood) June 24, 2019
3. If you're an options seller then sell only when volatility is dropping, there is a high probability of you making the right trade and getting profit as a result
He believes in a market operator, if market mover sells volatility Sarang Sir joins him.
This week has been great so far. The main aim is to be in the right side of the volatility, rest the market will reward.
— Sarang Sood (@SarangSood) July 3, 2019
4. Theta decay vs Fall in vega
Sell when Vega is falling rather than for theta decay. You won't be trapped and higher probability of making profit.
There is a difference between theta decay & fall in vega. Decay is certain but there is no guaranteed profit as delta moves can increase cost. Fall in vega on the other hand is backed by a powerful force that sells options and gives handsome returns. Our job is to identify them.
— Sarang Sood (@SarangSood) February 12, 2020
