There\u2019s a reason former @FEC chair @NRA @WhiteHouse & @POTUS @VP CAMPAIGN counsel, Don McGahn @JonesDay was paid $2M by @GOPChairwoman when he refused @HouseJudiciary subpoena & former @GOP chair @Reince law firm @MichaelBestLaw defended @alangarten @Trump Org v. @HouseJudiciary pic.twitter.com/OnyneCiPzt
— Mary Pat Flynn (@MaryPatFlynn1) September 1, 2020
Today is the day
Wonder if @HouseJudiciary will ask Don McGahn why @JonesDay closed their Moscow office in 2019🤔
Don McGahn @HouseJudiciary interview is tomorrow, June 4
— Mary Pat Flynn (@MaryPatFlynn1) June 3, 2021
IRONICALLY, my June 4, 2017 Memo of Law cites McGahn @JonesDay & 3 Russian oligarch \u201cclients\u201d (Fridman/Aven/Khan) mentioned in \u201cdossier\u201d for 2003 TNK-BP deal re: OIL/GAS in RUSSIA/UKRAINE
PUTIN WAS AT THE SIGNING CEREMONY https://t.co/LrQjtYDLeL pic.twitter.com/hvaPwF2Z7j
More from All
1. Mini Thread on Conflicts of Interest involving the authors of the Nature Toilet Paper:
https://t.co/VUYbsKGncx
Kristian G. Andersen
Andrew Rambaut
Ian Lipkin
Edward C. Holmes
Robert F. Garry
2. Thanks to @newboxer007 for forwarding the link to the research by an Australian in Taiwan (not on
3. K.Andersen didn't mention "competing interests"
Only Garry listed Zalgen Labs, which we will look at later.
In acknowledgements, Michael Farzan, Wellcome Trust, NIH, ERC & ARC are mentioned.
Author affiliations listed as usual.
Note the 328 Citations!
https://t.co/nmOeohM89Q
4. Kristian Andersen (1)
Andersen worked with USAMRIID & Fort Detrick scientists on research, with Robert Garry, Jens Kuhn & Sina Bavari among
5. Kristian Andersen (2)
Works at Scripps Research Institute, which WAS in serious financial trouble, haemorrhaging 20 million $ a year.
But just when the first virus cases were emerging, they received great news.
They issued a press release dated November 27, 2019:
https://t.co/VUYbsKGncx
Kristian G. Andersen
Andrew Rambaut
Ian Lipkin
Edward C. Holmes
Robert F. Garry
2. Thanks to @newboxer007 for forwarding the link to the research by an Australian in Taiwan (not on
3. K.Andersen didn't mention "competing interests"
Only Garry listed Zalgen Labs, which we will look at later.
In acknowledgements, Michael Farzan, Wellcome Trust, NIH, ERC & ARC are mentioned.
Author affiliations listed as usual.
Note the 328 Citations!
https://t.co/nmOeohM89Q
4. Kristian Andersen (1)
Andersen worked with USAMRIID & Fort Detrick scientists on research, with Robert Garry, Jens Kuhn & Sina Bavari among
Our Hans Kristian Andersen working with Jens H. Kuhn, Sina Bavari, Robert F. Garry, Stuart T. Nichol,Gustavo Palacios, Sheli R. Radoshitzky from USAMRIID and Fort Detrick to tell more fairy tales? Full emails listed for queries...https://t.co/kLRoQTxiGD pic.twitter.com/uHNuGraPP2
— Billy Bostickson \U0001f3f4\U0001f441&\U0001f441 \U0001f193 (@BillyBostickson) August 26, 2020
5. Kristian Andersen (2)
Works at Scripps Research Institute, which WAS in serious financial trouble, haemorrhaging 20 million $ a year.
But just when the first virus cases were emerging, they received great news.
They issued a press release dated November 27, 2019:
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)